Titanicupdate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic survival prediction \n",
    "\n",
    "This notebook goes through a basic exploratory data analysis of the Kaggle dataset with Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Importing relevant depedencies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Dependencies\n",
    "%matplotlib inline\n",
    "\n",
    "# Start Python Imports\n",
    "import math, time, random, datetime\n",
    "\n",
    "# Data Manipulation\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Visualization \n",
    "import matplotlib.pyplot as plt\n",
    "import missingno\n",
    "import seaborn as sns\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "\n",
    "# Preprocessing\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder, label_binarize\n",
    "\n",
    "# Machine learning\n",
    "import catboost\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import model_selection, tree, preprocessing, metrics, linear_model\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression, SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from catboost import CatBoostClassifier, Pool, cv\n",
    "\n",
    "# Let's be rebels and ignore warnings for now\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "conda install -c conda-forge catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing train and test data\n",
    "\n",
    "train = pd.read_csv(\"train.csv\")\n",
    "test = pd.read_csv(\"ttest.csv\")\n",
    "gender_submission = pd.read_csv(\"gender_submission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>892</td>\n",
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       "      <td>893</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
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      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         1\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
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      "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Pclass</th>\n",
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       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
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       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
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       "      <td>894</td>\n",
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       "      <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",
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       "      <th>3</th>\n",
       "      <td>895</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",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</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",
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      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "418"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking out missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RiBEjZLFYdP36dUlSTEyM+vXrp/Lly6tz5846ffq0d9H3X375RcHBwYqKivLlJiAT/tga+8EHH9TcuXNVuHBhff75597PcdL/h2YtW7bUW2+9pdmzZ/uqbGSRb7/9Vvv27dO0adM0ePBgjRw5Up06dZLD4dCFCxe8Syq8/fbbaty4sYoVK6bFixcrIiKCsNTPEHDmTeXKlVPv3r0VGhqq0aNH680335TFYtFbb72lypUra9myZfr888+9F8JIv4dmjRs39l4UCf9zq+ctIiJCN27cUEREhPbv368pU6aobt266tixo2JjY3Xo0CGNGjVK8+fPV8GCBVW8eHEfVA7kTgYPr6Z5nt1u19NPP618+fLphRde0I0bN1SrVi0FBwdLkk6dOqWlS5fq3XffVUBAgCIiIhQdHa3XX39dAQEBf+qjDv+xc+dOdenSRdOmTdPjjz+uEydOqHv37goKCtLYsWMVExNz09VJDofDe8IF/iP9egUej0cej0czZ87U119/rWrVqunw4cPat2+fHnroITVu3Fhff/21fvnlF61YsUL58+f3cfW4Xemf9ylTpujAgQMqVKiQWrdurQcffFDLli3TkiVLFBgYqE8++URms1mXL1/W1KlTdeTIES1btkyRkZE+3goAQHonT55U165d9dBDD6lPnz7/83368OHDGjVqlE6cOKGnnnpKbrdbO3fulCStXbuW43c/lP5z18aNG3XixAkVKFBAjz32mM6ePavY2FiVLFlSPXr0uGkdk/Pnz2vt2rV64oknVKZMGV+Vj9vwx3XHtm7dqlGjRuntt99W+fLldfLkST311FNq2LChOnfurLffflvPPPOM90I3u93uvSAK/iP9vp4Wkku/zxBljaq84fz58xoxYoR27typ0aNHq02bNkpNTVXr1q11+fJlvfTSS2rUqNFN52euXLnC53c/lJqaqoCAAEn/f84t7bV/+/bt3pCsQIECmjt3riRp3rx5+uCDD/Too4+qTZs2Kl26tO82AMiFzL4uAL63a9cuud1uTZw4UdHR0XI6nfr888/17rvvymazqVevXho3bpy6dOmiU6dOqWDBgqpUqZKMRqN3MVLkfH88sHa5XLr77rvVtGlTbdy4UXXq1FHp0qW1ZMkS9ejRQ2PHjtX48eNVtWpV7+8Rlvmf9B+2bty4IYPBoNDQULVt21Y//fSTvv76axUvXlzjx49XmzZtJP0+uyguLu6mq9bgf9JOrowePVqff/65KleurOPHj2vMmDEaM2aMnn32WSUnJ+vVV19Vhw4dVKBAASUmJurQoUN68803Ccv8DBev5D2cMMubfvrpJxUuXFjt27dXvnz5vI9fvHhRO3fu1Ndff63AwEDdddddat26tSZPnqyJEyfq9ddfV926ddW6dWu1bt1aJpOJ1w0/lPZ8xcbGav/+/QoJCVFSUpKCgoL05JNPatKkSRoxYoRee+01SfKGZlFRUerbty/Pt5/5Y2iStt+mpKQoKipK165d05NPPqm6detq/PjxOnjwoDZt2qQHH3zQG5gRlvmf9M/7ggUL9Msvv+jatWsqVaqU+vTpoyJFivi4Qvwb/nhcFxUVpUGDBmnJkiUaN26cJKlNmzZ65513vO/vBoNBDz/8sPc8DWGZ/7Db7YqPj1fx4sW9YdmCBQt0+PBhOZ1O3XvvverYsaPq1q2rGzduKCEhQQ0bNpQkXb58WadOndJjjz2m3r1704YR+BfwKRuy2+06cuSIEhMT9d///ld9+vTx9sS9evWqevbsqQMHDqhMmTKqX7++qlSp4l1IlLDMP6Q/+Dp37pyk3z9wBwUFqW7duoqLi9OpU6ckSaVLl9bixYuVmpqq/v3768cff/RZ3cic9B+2pkyZom7duqlVq1basmWLSpYsqYULF+qdd97RihUrvGHZ1atXtW3bNhUrVsw7yxT+Ja3tovT71Wo2m01TpkzR4sWLNXHiRN15550aMWKEvvrqK/Xp00dvvfWWihYtKpPJpMqVK2vNmjW03/QzHo/Hu68fPnzYx9UgO6Rff/aXX37R9u3bdeXKFSUlJfm6NPzLDh06pLNnz6pMmTIyGo1KSUnRnj171K1bNw0YMEBbt27Vpk2bNHv2bM2bN0933HGH+vfvr3r16unHH39U1apVvWuaEZ74p5UrV2rfvn2aPn26Nm3apJUrV+rJJ5+UJNWtW1dz5szRqVOn9Prrr2vbtm3e3+P59j9pz9nAgQO1Zs0aSb+vQ1i6dGk99dRTatSokR566CGNHz9eAQEBcjqdioiIUEREhA+rRmalPe/9+/fXunXrFBERoQIFCuinn37SE088oW3btsnpdPq4SmQlh8PhPV9z/Phx7d27Vw6HQ5UqVdKAAQP08MMPa9y4cVq/fr0CAgL0zjvvqHDhwho8ePBNbXjhH9xut5577jnFxsbqxIkTkqQhQ4bo7bffVkBAgK5evaq33npLbdq00dmzZxUWFqaiRYtq1apVWrx4scaOHavt27erRYsWhGXAv4TALI9JfyI1TY0aNVSjRg21bNlSAwYM0JEjRzRr1iytXr1ac+bMUXh4uDdMSY+rmv1D+hYOCxYsUPv27bV27VolJCRIklq0aKF7771XU6dO9S4eW7p0aS1cuFCRkZFcpeSn0p9Af+GFF/Thhx+qePHiio6O1pAhQ/Tee+8pODhY+fPn18GDB9WvXz/1799fI0eO1FdffaWRI0cqLCzMx1uBjEofkm7evFkffPCBdu/erZCQEEnSPffcox49eujuu+/WiBEj9PHHHysmJkZz5szR/PnzNWTIEFo1+aG02YTjx4/XxIkTJf3+QQy5U9oFS4mJierQoYN69Oih5557Tk888YQmTZqkkydP+rpE/IvKly+vgIAALVmyRJ988okmT56s5557TidPnlTr1q310UcfafXq1apfv76+/vprORwOxcTE6IUXXlC5cuXUs2dP7du3j64BfuSPr+eHDh1SqVKlVKNGDRmNRu96dNevX9eqVasUHBys+fPna9++fVqzZo2Sk5N9UTZuQ3JyskaMGKHTp097H7t8+bI+++wz79o0+fLlU4cOHeR0OmWxWDRgwACFh4fr4sWLWr9+vYKCgmjNlQt8+eWX2r9/v6ZNm6YxY8Zo9uzZ6tevn65du6bTp0/LbrdL4njPnyUlJWnatGnyeDze9+TBgwerS5cueuaZZ/Twww9r2rRpCg4O1ogRI9SwYcObQrM1a9aoatWqKl++vI+3BBllNBrVp08fnT9/XhMnTtRXX32lY8eOaerUqZozZ47WrFmjsWPHymg0qmPHjkpKSlKPHj1UvHhxrVy5UufPn9fy5ctVrlw5X28KkGsxPSgPSWufmJycrBUrVighIUGVK1dWq1atNH78eO3Zs0cRERGqVKmSSpQoIUk6e/asAgMDFRoa6uPqcTvShybTpk3TiRMn9PDDD2vs2LHavHmz7rvvPr3wwgtq166dFi9erO3bt+vhhx+Wy+VSuXLltHbtWu/0cPiXtBPor7/+ug4dOqRXXnlFVatW1d69e3XkyBGNHTtWbrdbrVu3VmBgoBITE5WamqrChQtr9erVHHz5ofT7e79+/fT555/LarXKZrPpyJEjqlatmiSpWrVq6tGjhxYvXqwpU6YoOTlZLVq08GHlyCrJycm6cuWKPB4PF7XkYmkzy3r06KHg4GCNHTtWVapU0QcffKCXX35Zv/zyi9544w1vUI7cpV69etqwYYNeffVV2Ww2SdITTzyh//znP95WPUWLFlWrVq3UsWNHnThxQhUqVFDVqlX10ksvafjw4Ro0aJA+/PBDQjM/kL5LxLFjx1SuXDmlpKQoJSVFdrtdgYGBMhqN8ng8cjqd2rJli44ePaoxY8Zo0aJFioqK4upzP3L06FG99957On36tKZNm6YiRYrIarUqODjYG4wYjUa1bNlSNptN69ev15NPPqkKFSro+vXrOnv2rN544w0VLFjQx1uCjPpji9xz587JZrOpVKlSMplMOnXqlAYPHqyWLVuqWrVqmjZtmgYPHswFjn5sx44deuONN3Tu3DnNmzdPixcv1s6dOzVgwACVKVNG77zzjj7//HMdPHhQM2bM0PDhw2U0GjV58mTZ7XZ16NBBb7/9tq83A7epTp06WrhwoZ5//nlNnDhRDofjpnMwDRs2lNVq1fDhwzV79myNHDlSlStXVkpKigIDA29qyw0g6xGY5REej8d7NfJTTz0li8Uim82mkJAQXb58WWXLllXZsmUlSd99950uXryoGzdu6NVXX1WhQoVuWjQa/iH9B+zly5frs88+07hx41SnTh098cQTWr16tdasWaMvvvhCzZs3V3x8vL766is9/PDD3oN1wjL/kZSUpLfeekvt27f3Btxut1uHDh1S3bp1VbVqVTmdTu3du1d33XWXJGnMmDFyu91q06aNlixZIpPJ5F1kFv4l/f6+b98+/frrr1q+fLmuXr2qDz/8UGPHjlW+fPn02GOPSfo9NHv++ec1a9Ysvfbaa2rUqJFCQkJuWlQeOdut1h6qXr26vv76a128eFGFChXyLhaN3OfkyZM6d+6cRo0apbp168psNqtIkSJKSkpS+/btdfToUZUpU0bh4eG+LhVZyO12KyIiQvPnz9f27dtlt9sVFRWlOnXqeH/G4/HI5XLpyJEjuueeexQdHe393l133aVp06YpX758vNf7gfSv8z179tS1a9e0cuVK3X333dqyZYu++eYbNWrUSNLvF0pFRkYqNDRU586dk8fjUe3atX1ZPm7DXXfdpTfeeEODBw/W4MGDNWPGDBUtWlRBQUHetvrS7+tKd+jQQXXq1NHmzZt1+fJl1ahRQ02bNlWpUqV8uAW4Hekvevvoo4/UpEkTmc1mmUwmhYaG6sSJE3rqqadUp04djRkzRvv379eWLVvUsGFDNWjQwLfF47bVqlVLU6ZM0YQJE9S3b19FRUWpbdu23gsZ77nnHq1atUorVqzQ7NmzNWHCBPXu3VtJSUlatGiRnnjiCYWGhnKRnJ9J//msevXqevXVVzVw4ECdP39eJ06cUPHixWUwGGQ0GlWvXj3VqlVLe/fuVWpqqvLly0dQBmQTArM8wmAwyOl0atCgQYqMjNTMmTMVGhqq0NBQXb9+XadPn1aRIkWUmJio+fPna+/evSpVqpQKFy6s5cuXszC4H0o7cHrttdf01VdfqUmTJt4TKlWrVtUdd9yha9euaerUqdqxY4eSk5O1du1aPfTQQxx4+6Gvv/5ac+bMUXx8vAYOHOgNP1JSUnTlyhVJv1/FtmTJEg0aNEiPPPKIOnTooOnTp+vXX39VixYtVKFCBUJSP5W2v0+aNElXrlzRnXfeqerVq8tgMHjXuXnxxRfl8XjUpEkTSVJMTIwGDx6sQoUKMYvYD6W9H8+dO1clSpRQxYoVdffddyspKUlHjhxRoUKFvB/GeP/2f39cCP78+fOKj49X2bJlZTabtXHjRg0bNkyxsbG6++67NXr0aHXv3l3169f3YdXIrLTwy2w2e2eOOhwO5cuXz/taniZtP3e73Tp37pw2bdqkkiVLKjAw0DuWwWBQ5cqVfbEpuA1pr9uHDh2S0+lUr1695PF41LlzZ23fvl1jx45VYGCg7rvvPlksFl26dEkGg8F7EST8j8Fg0H333acZM2Zo0KBBGjBggGbNmiWLxaJvvvlGx44dU8OGDVWoUCFVrFhRlSpVUqVKlXxdNjIh/THakCFD9M033+jOO+/Ugw8+qEmTJmn48OHas2eP7r//fk2aNEmBgYFKSUmRxWJR4cKFfVw9MiM0NFSPPvqoPB6PZs+erYsXL6pv376S5L2ItX379jp79qzeeecd9e3bVxUqVNDgwYMVHh7ORVF+6I+fyVJTU1W9enXNnj1bvXv31ooVK1S5cmXlz5/f+zmuXLlyOnTokOx2O+dqgGxEYJaHXL16VRcuXFDPnj0VFRWlM2fOaPXq1VqyZIm3FdvKlSu1aNEiHThwQKGhoapataqMRqO3nSNyvvRvwr/++qu+++477du3TxUrVvR+32AwKDg4WMHBwZo3b5727t2r7777TuvXr6cVn59KW/B7/PjxcrvdGjx4sEJCQvT444/LZDIpMTFRw4cPV8uWLdWmTRtJUlBQkMxmszZt2qROnTpJErNR/NiFCxd06dIlffLJJzddVV6uXDn17t1bkjRgwAAZjUb95z//kfR7eA7/9dFHH2nVqlVKTk6W0Wj0zjDasmWLEhMTValSJRUvXpywzM+lhWUOh0MJCQkqXry4KleurLCwMG3ZskV33nmnhg0bpv79++v5559XcnKyfvzxR/36668EZn4qPj5eAQEBioyMlNlsls1m08KFC70Lvzdp0kR169b1/vyKFStUs2ZNFS5cWN9//71WrFihpKQkTZ48WQaDgdmmfmzBggV6+eWXVbRoUZUtW9Z7smz06NEaNWqU+vbtq8cee0wRERE6efKk9uzZo0GDBvF8+5n0n98MBoNq1aqlmTNnauDAgerbt6/sdruOHj2qc+fOacOGDZKkihUrKl++fGrfvr3q168vg8HA8+6H0s8sk6RZs2YpOjpaFotFw4YN09y5cxUSEqLRo0crJCREly5d0pYtW1SoUCHv+oXwL+kvggoODlbjxo0l/X4R3K5duyT9Pos0rfVe79699cYbb2jnzp1q2bIla5b5qfQzSWfOnKmjR4+qcOHCatu2rapVq6YFCxbo+eef15gxY/TCCy+oQoUKunz5svbv36+oqCjCMiCbkYDkYn/8cBwWFqbU1FStXr1aR48e1datW/XLL7+oefPmKl++vNavX6/p06drxowZN7VgTFtcHv4h7U34xIkTKlOmjPr166eFCxfq7bff1gMPPKCHHnrI2wM/7WCtWrVqqlatmrp06aLg4GBflo/bZLFY1KJFC7ndbk2cOFFut1ujRo3So48+Kknau3evPB6Pnn76aUm/t/OyWq1asGCBKlWqxFo3fuiPV6gVLlxYffr0UWhoqNatW6d3331XrVu3lvT/oZnJZFL//v21cOFCbxsn+I8/zjJq0qSJGjdurBMnTuj8+fP69ttv9e2332rz5s3asGGDgoKClD9/fhUvXlwtWrTw/v8A/5G2nzudTvXr10/58+dX9+7dVbZsWTVt2lTLly/X9evXNWTIEHXt2tXbijcyMlKlS5f2dfm4DWfPnlWHDh3UsGFDvfjiiwoNDVXLli0VFBTkbbf3zjvvqE+fPmrfvr2OHz+uyZMnKygoSKmpqSpWrJjKli2rlStXymw2c9Gbn0n/+c3j8ahhw4aKi4vTd999p59//llFixaV2WxWiRIltGjRIs2bN0+7d+9WcnKyypQpo5UrV3Lxm59Jfzy3adMmXbx4UaGhoXrooYc0a9YsjR8/XufOnVPfvn3Vt29fff/99zp16pS+/PJLnT59WqVKlaIlm5/79NNPNWDAAElS06ZNvS1zGzduLLvdrpkzZ6pPnz4KDg6Wy+XSwYMH9eabbyoyMtKXZeM2pN/fT58+rZCQEEVGRqpZs2aSpAkTJmjYsGGaOnWqd4b4iRMnVKBAARUrVsxndSPz0t7bhw8frq+++krR0dE6duyYdu3apQkTJqhmzZp69dVX1adPHx04cEDFixdXWFiY4uLitHz5clmtVh9vAZC38Okpl0r7cOxyueR0OmW1WmW1WtWtWze98soreuONN3T33Xdr+PDhuv/+++VyubRnz55bfqDmANz/zJw5U+vXr9fLL7+sGjVqKDY2Vg6HQwMGDNCiRYtUu3btm068pn04Z1Fw/5P+oNtisaht27byeDyaNGmSpN9be4SGhspmsykxMVE7duzQjRs3tHHjRp07d05RUVGEZX4o/fO+Y8cO2Ww2Va9e3RuMJSUlaeTIkQoMDFTTpk0l/R6adevWTRaLhRPpfij9c37t2jVdvXpVpUqVksFgUPny5VW+fHnVq1dPM2fO1I0bN7Rhwwbt3btXO3bs0JEjR7xrF8J/pD3nycnJ2rt3r44cOaKUlBQFBQXphRdeULdu3XTixAkdPXpUSUlJ3qtQlyxZosKFC6tevXq+3gTchuLFi6tatWr65JNPFBQUpIIFC6pAgQKaOnWqSpUqpcuXL+udd97RrFmz5Ha7FRsbq61bt2r79u0yGAyqUKGCqlWrRocIP5T+dT7tfpUqVTR8+HANHTpUU6ZMUfHixb2v54GBgRo6dKiuX78ui8Uig8HACTU/k37GQf/+/bV3714FBwfr1KlTCgkJUdOmTb2zCb/55hu1adNGtWrVUq1atdS8eXNJYj1CP/THC5vvvPNO9e3bV0uWLNGXX36pBx54QCaTSQUKFFD79u1133336a233pLdbleJEiU0ZswYlSlTxodbgNuR/jV+3Lhx+u6773T9+nV17txZrVu39q5dNmHCBCUmJqp79+5KSkrSBx98IIPBwPqEfir9856UlCSbzabZs2erVq1a+uqrr7R48WINHTpU06dPV82aNbVo0SINHTpUe/bs0fz58zVixIib1qMFkD0MHo/H4+sikLXSPhzbbDZNnjxZp06dUpUqVdSiRQtVrFhRly9fls1mU7Fixbwv3L/99pt69eqlevXqaeDAgT7eAmTWb7/9ps6dO8tisWjs2LGqUaOG9u/fr9mzZ2v//v16+eWXVadOnT/NVoB/SX/w9dVXX+natWsqWrSoypcvr//+978aN26cnnzySb300kuyWCzq2LGjfvjhB4WEhMhqtWrx4sXeVp3wH+n324EDB+rrr7/W9evXVaZMGb344otq3Lix4uPjNXPmTH344YeaMWOG96pF6f974sN/pH/OR48erbi4OJ04cUJ33HGHGjRooC5dunjXMfjmm2/0wgsvaNOmTd4PV3a7nZOofspms6lly5beNekMBoN27dqlp59+WgMGDND169e1ZMkS/fe//5XNZlPhwoUVHR2tJUuWKCAggPXr/Mju3bu1ZcsWjRo1SpI0atQobd++XQUKFFC+fPn0+uuvS/r/E62LFy/W7Nmz9cYbb3jXqE2PYzz/kn5fXbJkiQ4dOqSyZcuqYcOGqlSpko4dO6aBAwfKZrNp7ty5qlKlivd3abnpn9I/b0uXLtWqVas0d+5clS9fXk6nU2FhYZJ+fw+Pi4vT8OHDVbx4cU2ePJmTp34s/b5+6dIl5c+fX0ajUVeuXNEbb7yhxYsXe2cTSv//Wp72/wv7u//r16+f9u7dqyZNmujatWvavHmzunbtqs6dOysyMlIbN27UtGnTdO3aNdWrV08ej0cDBgxgDVI/lH5//+KLL2Sz2TRr1iy98sor3vUnv/zySy1evFjnz5/XtGnTVKNGDX3//fcaNmyY1q1bp0KFCvlyE4A8i8Asl0k7oEpMTFTr1q3l8XgUHR2t3bt3KyYmRr169fKubbNlyxZt3rxZFStW1BdffCG3260NGzZwJaqf+ePVw2n/jo+PV4cOHWQ2mzVhwoSbQrODBw9q5syZXH3ux9J/WBowYIB27dql1NRUXb16VcuWLVPNmjX1zjvvaOLEiWrevLl3xtmWLVtkNptVuXJlFS9e3JebgEwaPHiw9uzZ410Aun///goKClLfvn31yCOPeEOzjz/+WOPGjVOrVq18XTIyafjw4fruu+/UuXNnlSlTRp999pm++OILlS5dWq+//rqsVqsOHTqk1q1ba+7cud41EeC/pk6dqu3bt2vRokUqUqSIAgICtHjxYi1ZskRNmjRR//79lS9fPl28eFEHDx5U0aJFdccddzC7yI94PB45nU6tWLFCZ8+e1ejRo73fGz16tNatW6eSJUtqw4YNCg8PV9pHt4SEBD333HOqV6+eBg8eLLfbTTjqp9KHmy+88IJ27Nih4sWL6+jRo6pRo4b69u2rmjVr3hSazZ8/33uyDf4jOTlZs2fPVp8+fRQREeF9fOjQobp48aJefvllBQYGeo/zr1y5om+++UbR0dFyOBzq2rWr7r//fr3yyiu8vvuh9CfPp02bpr1798pgMKhr165q0KCB7Ha7XnvtNb3++uvq16+fdw1it9vtvWiGwMy//DHoXL16tZYtW6Z58+apcuXKunLlitq3b6+zZ8+qffv26tatm/Lly6cPPvhA48aNU6NGjTR58mTWr/JD6ffVfv366fPPP5fFYlFycrJeeeUVPfTQQ96fTQvNLl68qHHjxql27dreNewA+AaXHeYyaYvCDxo0SNHR0Vq6dKmWLl2q7t27a//+/Xr99de1Y8cOSb9f0XTixAlt375d5cuX94ZlLpfLx1uBjEj7sLRp0ybvv51Op4oUKaKVK1fK5XJp9OjR2r17t6pWraoBAwaoePHiGjVqlJKTk0Vm7p/SDr7mz5+vffv2afbs2d4Q/P7771dAQICaN2+uMWPGaNOmTRo9erQcDocee+wxNW7cmLDMjyQlJemtt97SjRs3vI/t3r1bBw4c0MSJE9WqVSvdcccdqlmzpk6fPq0ZM2bo008/VZEiRTR48GDVrVtX06ZNU2Jiog+3Apl14sQJ7d69W7GxsXrmmWdUv359dezYURcvXlS1atV07tw5paSkqGLFiipQoIB27tzJ67sf+uNzdvbsWRUvXlwlSpTwvt/36NFD3bt319q1azV//nydPHlShQsXVv369VWhQgUZjUbWn/UjBoNBAQEBeu655zR69GilpKRo0aJFkqTx48erQ4cOSkhI0Jo1a3Tjxg3vSdPChQvLZDIpKSlJBoOBsMyPpYVl06ZN05EjR/Tqq6/q/fff14gRI7R//3699tpr2rlzp8qVK6dZs2YpPDxcnTt31qFDh3xcOTIiMTFRL774omw2201t8B0Oh06ePCmTyeQNy9JYLBZNmzZNH3/8se677z4tX75cL730Eq/vfsjpdHpfp/v27av33ntPJUqUkN1u16hRo/Tuu+/KYrGoZ8+e6tatm+bPn6/XXntN0u+vEWmf/QjL/IPb7ZbT6dT58+eVmprqPcd24sQJVahQQZUqVVJqaqo2bdqkSpUq6dFHH9WyZcu0ZMkSXb58WS1atND48ePVq1cvwjI/4nA4dPnyZUn/v69u375dR44c0eLFizVq1CjVqlVLsbGxiouL8/5e/fr11bNnT1mtVk2dOlUpKSl0BwF8jCMtPxcfH6+TJ09KkooVK6YSJUro2rVrOnfunLp3764SJUrowoULunTpku69914dOHBAL7/8ssLCwtShQwe1a9dO169fV/78+SX9ebYSch6Hw6H9+/fr9OnTKlCggO655x4dOHBAQ4YM0f79+zVy5MibQrM333xT7du316RJk/TSSy+pZs2amjBhgvLnz8+aZX4kNTVVJ06ckMFgUHh4uAoXLixJ2rdvn2rWrKlq1arJZDJ5r1a9fPmyDhw4oHvvvde79kFwcLCGDh3KBy0/88knn2jSpEm6ePGiunfvrtDQUJ0/f15Xr171tmT64IMPdObMGc2dO1fTp0/XzJkz5fF41KBBA02dOlV2u12hoaE+3hL8E398ja9ataoiIiKUmJio3377TdHR0bJYLPrll1/UoUMHPfLII2revLkWLFigli1bql69eqpbt66eeuop9nU/ER8fr5CQEIWGhspgMNw02yQoKEjHjx+X9PsH79TUVAUEBKhHjx76+uuv9cEHHygwMFDPPvus931BYv1Zf3H69GkdOHBAhw8fVrly5VSnTh1t375d8+bN08WLFzVq1CiNHDlSNptNa9eulclk0tNPP63Q0FCdOHFCKSkpKlasmK83Axl0q2O6pKQk/fjjj3ryySdVvXp12Ww2Xbt2TXfeeacOHDigBQsWKDY2VjVq1NDUqVM1ZswYjuP9SGJiopo1a6ZSpUqpb9++slqtWr9+verVq6eoqCg1a9ZMEydO1KeffqpGjRrJ7XZLkkJCQnTvvffqwoULkqQaNWr4cjOQQYmJiZo5c6bGjh3rPceyePFiHT58WMuWLVPlypV14sQJtW3bVq+88ookqVWrVurZs6dMJpPmzJkjs9ms5557zpebgQxKW6Pqp59+0q+//qrChQsrNjZWjRo1UnJysk6dOiWDwaDdu3dr/vz5GjJkiJ5++mnduHFD69at0/Hjx9WjRw89/vjjvt4UZEBiYqKee+45dezY0bscwsyZM5WUlKQ6dero/vvvl/T7uuKzZ89Wp06dtGLFCsXExEiSHnzwQZnNZpUqVYqZZUAOQDLix3744QeNHj1aCQkJunr1qkqUKKFx48apSpUqOnLkiCIiImQwGLRmzRpt27ZN69ev108//aSePXtq1qxZql+/vrp06eINyzweD2FZDpeYmKg+ffrowoUL+u2335SSkqJhw4apUaNGGjRokObOnStJ3tAsNTVVRYsWVb9+/TRs2DBNmjRJo0ePVrVq1Xy7IciQxMREDRo0SL/88osSEhJUunRpDR48WPXq1dPx48dVpkwZmUymmwLvxMRExcbGasyYMWrTpo1MJpPuueceTqD7oSeeeEJXrlzR9OnT5Xa79cILL6hs2bIKDg5Wamqqfv75Z02YMEF9+vRRgwYNlJSUpAEDBmjKlCnatWuXXnrpJZ53P3Gr1/jBgwerc+fOKly4sAICAnTkyBGVK1dO7du31/3336/p06fLZDLpyy+/VJkyZVSvXj1NnjyZ59xPHD58WM2bN1eDBg0UHR2tfv36KSgoyBt4Pfjgg9qxY4deffVVdevWTQEBAfJ4PEpNTZXRaFTBggX1xhtvqFSpUmrbti3rVvmRvXv3avjw4TIajbpx44auXr2qCRMmqE6dOho8eLBmz54tl8ulsWPHasqUKRo+fLgWLVqkTZs2qXTp0kpISFBAQIC6du3q601BBtzqmG7o0KGqW7euDhw4oKZNm8pgMOibb77RmjVrNGPGDNntdnXv3l2LFi1SkyZN1Lp1a7355pvMOvATNptNzZs31x133KFJkyapSJEi3ouhHn30UQ0ZMkRNmjTRZ599psmTJ8tsNqtBgwaSpOvXr+vq1au6++67fbsRyLDExES1aNFChQoV0vXr171rzR4/flz333+/KlWqpOTkZH3xxReqW7euTp48qWnTpsnj8XhDM7PZrPr16/t4S5ARNptNTz/9tMLDw3XvvfeqZcuW+uGHH+RwOCRJHTt2VJ06deRwODRixAg98cQTevrppyX93rLTarXK6XTedBEUcr60/b1gwYLedWXPnTunH3/8Ud99991N64mndX1KC81WrlypqlWrSpI3VAPge6Qjfmr37t169tlnvSdZTp06pVWrVik2Nlbr1q3Tiy++qMjISO3YsUOvvPKKFi5cqIIFC6ps2bIym806f/68du7cqc6dOzO930/YbDa1adNGhQoV0ogRI1SqVCmdOnVKTqdTJUqUUOvWreV2uzV37ly53W6NHj3a+0HabrerTp068ng8LBrqZ9IOvooUKaJOnTrp0qVL2rRpk/r166d3331Xjz/+uFavXq1OnTqpZMmS3tkHJUuWVJkyZXTkyBFJUsuWLdnH/ZTBYFDnzp3l8Xg0ffp0eTwede7cWXPnzlXBggU1dOhQNWjQQD179pT0+8F5uXLlVK9ePT3zzDM8737iVq/xp0+f9rbwCQsL03/+8x+9/PLLmjRpkh577DFNnDhRJpNJJ0+eVMGCBVWmTBlJvJ/7kzNnzkj6vXVPXFycGjZsqMcff1xNmjRRjRo11KhRI33yySdat26dXC6X+vTpI4PBoPPnz8tut2vWrFlavXq1li5dqlatWslisfh4i/BP7N+/X926dVPz5s319NNPKzo6WkePHpXZbFZUVJRatWoll8ulefPmye12a/z48ZoyZYpCQkK0cuVKhYaGqlmzZnr66adlMpluWhcHOddfHdP17dtXGzZsUI8ePRQWFqYLFy5o6NCh6ty5s+677z65XC7lz59fhw8fVnx8vB566CFFRkb6enPwD9hsNj3zzDNKTEzUyJEjVaRIEUlS48aN1bFjR23dulUzZ87UiBEjNGDAAE2bNk19+/ZV586dZbFY9Ouvv+rIkSMaN26cj7cEGZGYmKjmzZsrOjpa06dP94Zlbrdbp0+fVlhYmAwGg3bt2qVFixZp7NixatKkiZo1a6alS5fq4MGD6tOnj1544QUfbwkywuFwaMCAASpUqJDGjRunkiVLSpI3EJOk0qVLq3z58jpy5IiSk5PVunVrSdL58+dlNBo1dOhQNWrUSCEhIT7ZBmRcYmKinnjiCZUsWVLTp09XgQIFJP3eAWzQoEFatGiRPvnkE23fvl1169aV9P+h2bx58/TUU09p/fr1XBgB5DAEZn5o9+7d6tKli9q1a6eBAwd6p+tGRUVp2LBh2rJli5599lkFBwdr2rRpqlu3rho1aqTU1FQdP35c9957r3r37q3atWuzcKyfcLvdmjZtmiIjIzVp0iRFR0dLkqKjo2/qc9+8eXNZrVbvTLMBAwbIZrNp//79ql69uvdEG/xD2sFXiRIlNGvWLG/YWblyZQ0fPlyrVq3SI488ok8//VQDBw7UnDlzVKJECUlSQkKCjEaj94M5z7t/SXtdTjsJajAY1KlTJ0nS9OnTvS3ZJOnq1asqW7aspN9bcR46dEh16tTRgAEDOHnuJ/7Xa3wal8ulJ554QqdOnVJAQIBiYmJktVp19OhRvfXWW0pJSdG9997rq03AbXr44Yf1+OOP6/Lly5o7d65Wr16tr776SmvWrNFjjz2mxx9/XFOnTtXQoUO1efNmvffeeypXrpyOHj2qoKAgVa5cWaVKldLPP/9MlwA/YbPZtHDhQj3yyCOKjY31tlK+55575HA45Ha7dePGDT388MMKDAzUnDlzJP2+ntnIkSN14cIFBQYGql27dje9TyBn+1/HdC+99JJWr16t3r17KzIyUqtWrVLJkiX14osvSpJ++uknlSxZUu3atVO9evUIy/xEWmiSkJCgwoULa+PGjerRo4eCg4MlSQMHDpQkffjhh5o0aZJGjhypKVOmaM2aNXr//fcVFham4sWLa8WKFd7jPOR8aft6Wlh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\n",
      "text/plain": [
       "<Figure size 2160x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missingno.matrix(train, figsize=(30,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating two new dataframes for data analysis\n",
    "\n",
    "We'll create one for exploring discretised continuous variables (continuous variables which have been sorted into some kind of category) and another for exploring continuous variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bin = pd.DataFrame() # for discretised continuous variables 0-10, 10-20\n",
    "df_con = pd.DataFrame() # for continuous variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Different data types in the dataset\n",
    "train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring each feature individually  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Target feature : Survived\n",
    "\n",
    "Key: 0: Not survied , 1: Survived\n",
    "\n",
    "This is the feature we want to predict based off all the others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    549\n",
      "1    342\n",
      "Name: Survived, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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zJmvWrElnZ2dmz56dj33sY2/4+T//+c/ZsGFDRo4cmSuuuCIdHR1ZuHBh2tralD8AwNuWW8AAgGHlxRdfzJQpU5IkEydOTEdHRy655JIkSVNTU1paWnLo0KHXfaZcLve/bm5uTlNTU+rr6/Oe97wnp06dqlp2AID/lQIIABhWWlpa8vzzzydJDh06lCeeeCK///3vkySdnZ3Zv39/xo0bl8bGxnR0dCRJdu/e3f/5Uqn0H9esq6tLX19fFdIDAPxvFEAAwLAyd+7cHD58ODfccEO+8Y1v5Ac/+EFee+21XH/99Zk/f35uueWWvPvd7878+fOzfPny3HTTTent7R3wms3Nzdm/f38efvjh6vwQAABvUan8//c0AwAAAFA4dgABAAAAFJwCCAAAAKDgFEAAAAAABacAAgAAACg4BRAAAABAwSmAAAAAAApOAQQAAABQcAogAAAAgIL7P5OfEMVYByWfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#How many survived \n",
    "\n",
    "fig,ax = plt.subplots(figsize=(20,2))\n",
    "sns.countplot(y= \"Survived\", data = train)\n",
    "print(train.Survived.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding this to our sub dataframes\n",
    "\n",
    "df_bin[\"Survived\"] = train[\"Survived\"]\n",
    "df_con[\"Survived\"] = train[\"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived\n",
       "0         0\n",
       "1         1\n",
       "2         1\n",
       "3         1\n",
       "4         0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived\n",
       "0         0\n",
       "1         1\n",
       "2         1\n",
       "3         1\n",
       "4         0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_con.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature: Pclass\n",
    "\n",
    "Description : Ticket class of a passenger\n",
    "\n",
    "Key: 1=1st , 2= 2nd , 3= 3rd \n",
    "\n",
    "* ordinal variable \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Density'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KkFlYjgHt3Z7oTdn6zBnYFpXVNVj7a4ro1zZ3DHsiMgidIOCQ+hZcbJUIbiXuqP4uX1cVRnXxwuaYNOQUV+ilDXPFsCcig0i8WYzs4gq9jervmjuwLap1AlYf5ej+zxj2RKR3giDg8KVbcFU1Q+dWjnpty9vFBuNCvLAtNh3ZRRzd38WwJyK9u5qjwa3iSoQGuEIu09+o/q5ZYf6o1unwNdfd12LYE5HeHU/Jg6qZBTp5ORikvVbONnguyBNbY9Og4SZpABj2RKRnOcUVSL6lQU9fZ1goDBc5M/r5oKSiGjtO3zBYm6aMYU9EenUi9TYs5DJ093ExaLtdvJ3QvY0zNvx+DdXcM4dhT0T6U6atxtn0AnRu5QhVM4M+GA8A8HJ/X2QWlmNfQrbB2zY1DHsi0pvT1wtQVSOgj19zo7Q/sL0bfJvbIupYKgRBMEoNpoJhT0R6UaMTcDIlD36utvBweLKdLR+XXC7DjH6+uJhZhNhr+UapwVQw7IlILxJuFqG4otpoo/q7RndtCScbS2w8fs2odRgbw56I9OLE1Ty42CoR4GFn1DqsLBWY2MMb/026hRv5ZUatxZgY9kQkujPpBbhRUI7efi4GuYnqYSJ7toFCJpP0w00Y9kQkuvW/X4OVpRxdRd7G+HF5OFjh+Y6e2HH6hmRvsmLYE5GoMgvLsT8hG91aO6OZhTgPJxHDtL4+KKmsRnScNG+yYtgTkag2nbwOQRDQ08+wN1E9THArR3TxdsTXJ65Dp5PeMkyGPRGJpkxbje2x6RgS5AEnG6Wxy7nPtD4+uH67DEcu5xi7FINj2BORaL6Pz0BxRTWm9/UxdikPNCTIAx72VtggwWWYDHsiEoVOJ2Dj8evo7OWArt6m8cbsvSwVckzq3RrHr97G5ewSY5djUAx7IhLFr8m5SM0rxbS+PpCZwHLL+kR084aVpVxyN1kx7IlIFBuOX4O7fTM839HT2KU0yMlWiVFdvPDD2Uzkl2qNXY7BMOyJ6Ildzi7Bb1fyMKlXG1gacM/6xzWtTxtUVuuw/VS6sUsxGNP/qRCRydt4/BqaWcgxsbu3sUtplLbudujXtjk2nbwObbU09rrXS9jrdDp88MEHGD9+PCIjI5GWllbn/MaNGzF06FBERkYiMjISqamp+iiDiAzgtqYSP5zNxOiuXnCyNb3llvWZ1scHt4orsS8hy9ilGIReniZw6NAhaLVa7NixA+fOncMnn3yCNWvW1J5PTEzE0qVLERQUpI/miciANsekobJah+l92xi7lEcSGuAK3+a22PD7NbzQuYVJv6ksBr2M7OPj49GvXz8AQHBwMBISEuqcT0xMRFRUFCIiIrB27Vp9lEBEBlBRVYNNJ9MwsL0b/N2Mu7vlo5LLZZjapw3OZxThTHqhscvRO72M7DUaDVQqVe3HCoUC1dXVsLC409zQoUMxceJEqFQqzJ49G0eOHEFYWNh911Gr1foo74EqKioM2p6YqquqkJX95H+KqtWlIlTTeOba52L1N2DYPtdHf/+cXIz8Ui3+0lpx37WzsotFaydQqdTLa+UpWx1sLeVYue883g11F/36pvQa10vYq1QqlJb+8SLW6XS1QS8IAiZPngw7uzujgNDQUCQlJT0w7AMDA/VR3gOp1WqDtiemn5Nj4enx5MvdAgMN++aaufa5WP0NGLbPxe5vnU7ArJ9+RceWDgh/pst90yBni8Vb6WJlVaq318qLGXKs//0aHDzboIWjtajXNsZrPD4+/oHH9TKN07VrVxw7dgwAcO7cOQQEBNSe02g0GDZsGEpLSyEIAmJjYzl3/xhuaypx/kYhTlzNQ3ZJFYrKqyT/jE0yrMOXcpCaV4qX+/ua9Xz3pF6tIQgCNp1Me/gnmzG9jOyfffZZHD9+HBMmTIAgCPj444+xd+9elJWVYfz48Zg3bx4mTZoEpVKJXr16ITQ0VB9lNCmCICAurQC7z2biyKUc3CyqqPsJCUWwVSrQ1t0OnVo6IMDDziQeGkFNV9RvqWjpaI3ngzyMXcoT8XKyweCnPLD9VDrmDPSHjVIvsWh0evmu5HI5/vGPf9Q55ufnV/v/I0eOxMiRI/XRdJMjCAKOXs7FpweTcTGzCNaWCoQGuGJaXye0cbGFysoCW48lAc1UyMgvw6XsEpy7UQhnWyUGtHNDsLcjQ59Edya9AKeu5WPh0EBYmMFNVA8zra8P9iVkIzo+A5N6tTF2OXrRNH+FNRE38svw3u4EHEvORWsXG3w8qiNGdWkJa2XdB0Icv6iEp4cL4OuCGp2ApKxiHEvORfSZDJxIycPorl6iz0WStH3+yxU42yoRYSY3UT3M062dENLaCV8dTcGEbt5QWpj/L7B7Nb3vqIn44WwGBn92DPHX8/HBsA44OC8UE3t43xf091LIZejY0gEzn/HD+KdboaSiGquPXsXRyzmc0ydRnL9RiKOXczGjnw9smzWN8aJMJsNfB/jjZlEFdp3JMHY5etE0flJNSFWNDv/8SY2vT1xHdx9nrBgfjJaPMSqXy2To3MoRAe522H0uE/9NuoWMgnKMDfGClaXpPCrOXFRU1SCjoBz5pVpkFlcB1uVwslFKsi9XHb4CRxvLJjfdERrgik5eDlh9NAVjQ7yaxPTUnzHsTUiephKvbzmDU9fzMa2PD959vv0TbyplrVRgQrdW8Ha2wb6ELKw+moKXenrDzc5KpKqbrsSbRfjxQhaOJefiUnYJav78KLvEIgBAc5USvq4qBLVwgK+rbZN/fyQhswiH1Dl469kAqJrIqP4umUyG2WH+eGVzPPacv4nRXb2MXZKomtZPy4zdyC9D5PpYZBdXYOWEYIwIbinatWUyGfr4N4enoxW2n7qBqGOpmNK7DbycbERro6kQBAG/qHPw5dGrOJteCIVchm5tnDAz1A/+bio0VzVD9Ek1rFWOuK2pRHp+Gc6lF+LUtXw42yrRx785urV2anKjwrs+/+UK7KwsMLlPG2OXohfPdnBHew87fHHkKkYEt4RC3nR+eTPsTUDyrRJEro9FubYGW2f0QEhrZ72049tchdf6+2LD8Wv492/X8FLP1vB3Uz38CyUiIbMIi39KQkxqPlq72ODvwztgZHDL+zb3ik1SwtPDofbjqhodkrKKcTLlNvaev4ljybkY2tETQS0d7m3CrCVkFuG/Sbcwd2Bb2FtZGrscvbgzd98Ws7adwY8Xboo66DI2hr2Rnb9RiMkbT0GpkGPna73Q3sNer+25qJrh1VA/fH38Or45eR3jn27V5ELpUZVpq/Hxz2psjU2Hk40S/xjxFCK6ezd6Cs1SIUdnL0d0aumAlNxS/HwxC9tOpaODpz2Gd24BB2vzD0ZBELD4pyQ42yoxvZ9pPl9WLM8FeaC9hx3+77/JGBLkgWYWTeN9mab5t6aZuJhRhJfWx8LOygLRr/XWe9DfZW9liZf7+aKlozW+PZ2OxJtFBmnXFF3IKMSwz3/H1th0TO3tg6Pzn3nsB3DIZDL4u6kwK8wfQ57yQPKtEnx2KBnnbhSKX7iBHUy6hZjUfMwb1HRH9XfJ5TK8+3wg0vPLsCWm6TzchGFvJIk37wS9g7Ulvn2lF7xdDDt/bq1UYErvNmjpaI3tp9KhzhJv0ypzIAgC1h1LxejVJ1BeVYOt03vgg+EdRAkyhVyG/gGumDuwLTwcrLAz7gZ2n81EVY15PiSjqkaHT/Zdgp+rbZNZV/8w/ds2R1//5lh1+AqKyquMXY4oGPZGoM4qxkv/joWtUoHtL/d8rKWVYrCyVGBqHx+0cLTGtth0/KK+ZZQ6DE1brcPfoi/gnz+rMSjQHfvn9kdv/+ait+OiaoYZfX3Rv60rTl3Px1e/ppjlM0+3xqQhNa8U7zWRu2UbQyaT4Z3n2qOovAqrj141djmikMZPzoQk3yrBi/+ORTMLBba/0hOtnI27IsbKUoGpvX3g4WCFmVvO4MilHKPWo28FpVpEro/Fd/EZmDPAH6tf7AoHG/1NSyjkMgwJ8sCknq1RUKbF6qNXcT3PsFtJP4misip89ssV9PF3QVg7N2OXY1BBLR0wKrglNh6/joyCMmOX88QY9gZ0NacEE9fFwEIuw/ZXeqK1i62xSwJwZ0pnWh8fBHio8OrmeBy93DQDPyVXg1Grj+NseiE+Gx+MN//SDnIDLa1r72mP10P9YW2pwPrj13A2vcAg7T6pT/arUVJRjfee72DWO1s+rrcGt4MMwOIfTWNP+ifBsDeQ1FwNItbFArgT9D7NTSPo77JWKrBleg/4u6nwyuZ4HEvONXZJojpxNQ+jvjyOkopqbHu5B0Z2MfySuuZ2zTDzGT+0drbBd/EZOJCYDZ3OdLewOHUtH9tP3cD0vj7o0MIwiwdMTUtHa8wZ2Bb7E7OxPyHb2OU8EYa9AaTkajAhKgY6nYDtL/eAn6tprm13tFFi64w79c3YFIcjTWSE/+2pdEzacAru9lbYPasPnm6jn/sYGsNGaYGpfXzQrY0Tfk3Oxetbz6BcW2O0eupTpq3G299fgJeTNd4Y1NbY5RjVK/190d7DDh/8J8Gs36xl2OvZ1Zz/Bb0gYPsrPdHW3bSf0+lkq8S2GT3Q1k2FVzfF41CS+b5pW6MT8M+fkvDOrovo7d8c37/e2+jvkQB35vFHBrfE80EeOJCUjfC1J3GruOLhX2hAS36+hOu3S7FsbOcmu797Y1kq5Fg6phPyNJVYuv+Ssct5bAx7PbqaU4IJUTEQBGD7yz0RYOJBf9edwO+JQE87zNwab5Z/vpZWVuPVzXFY99s1TO7VGhsmP21S68NlMhn6tnXFusinkZKrwYgvjiMh0zTudziUdAubY9Iwo68Pevm5GLsck9C5lSOm9fHBtth0xKbeNnY5j4VhrydXbt0JepkM+NYMRvT3crCxxOYZPRDU0gGztp3BTxfEecC2IdwsLMfYr07i8KUcLHrhKSwaEWSySwYHdXDHd6/1gkwGhK89iYNG/ksq7XYp5u08h6CW9njrL+2MWoupefMvAfB2tsGbO8+b5XSOaf4LMHMJmUWIWBcDuUyGb1/pabb7z9hbWWLTtO7o6u2Iv24/g52nbxi7pIc6k16AEV8eR0Z+GTZM6YbJvdsYu6SHeqqFA/4zq8//3hyPw+qjV43y7IGSiiq8ujkecpkMa14MkeT2zQ2xUVpg5YRg3CquwDvfXzC750Mw7EV24moeJkTFQKmQY/srPU32zdjGsrOyxNdTu6OPf3P87fsLWHEw2WRf5N+eSseEtTGwspTj+9d74xkzWhfuZm+FHa/0wvMdPfGv/Zfx6uZ4FFcYbvRYVaPDrG1ncSVHg1URXUzivQ1T1MXbCfMHt8O+hGys+y3V2OU8Eoa9iH68cBNTNp5GC0crfP96b7MP+rtsm1lgw5RuGBfihZW/XMHfoi+Y1K3/2modFu6+iHd2XUQPX2fsmdXXbN4f+TNrpQJfRHTBwqGB+OVSDkZ8cdwg21hU1+gwb8c5HEvOxcejgtA/wFXvbZqzV/r7YmhHT3yy75JZ3ZPCsBeBIAj48shV/HX7WXRu5YDvXu0NT4em9cxXS4Uc/xrbCW8Maovv4jMwISoG2UXGX0GSmqvBmDUnsCUmHa/298XGKd3u25LYnMhkMszo54vtL/eEprIaL3zxO1YfvVr3wSkiqqyuwdxvz+HHC1l497n2GN9NGnvfPAmZTIZl4zqhvYc9Zm09g4sZpvHG+sMw7J+QprIaM7ecwbIDl/FC5xbYPL2HXm+/NyaZTIY3BgVgVUQXXMoqxtDPf8PvV/KMUosgCNgZdwPDVv2OGwVl+OqlELz7fNPZu6W7jzP2z+2HQYHu+Nf+yxj71QlczdGI2kZ+qRaR/z6Fny5mYcHz7fFqqJ+o12/KbJQW2Di1GxxtlJiy8RSu3CoxdkkP1TT+ZRjJlVslGL36OP6blI2FQwPx2fhgSbypNbxzC/xndl+4qJSI3BCLj39WG/TGoBv5ZZjxTRz+Fn0BnbwcsG9uPwwJ8jBY+4biomqG1S92xcoJwUjNLcWQz47hg/8k4Lam8omvfSIlD8+tPIZzNwqxckIwXunPoH9U7vZW2DS9O+RyGSLWxeBytmkHPsP+Meh0Av79WyqGrvodtzVabJ7eAzP6+Upq7xB/NxV2z+qDCd28EXUsFX/57Fe9b7GgrdbhyyNX8eyKX3Ey9Tbeez4QW2f0bHJTZn8mk8kwIrglfnkrFBHdvbE1Nh2hy47is0PJjxX6NwvL8eaOc5i4Lha2zSyw6/XeTeppTIbm56rCt6/0hEIuw7ivTiDGhNfgS/vWuMeQmqvBu7suIvZaPp7t4I4lozuiuaqZscsyChulBZaM7oiRwS3w7g8XMWnDKQwKdMOcgW3RyctRtHYqqmrwXdwNrDmagptFFRjylAc+GN4BLYy0NbQxNFc1w0cjgzC5dxss3X8Jnx26gtVHU/BsoDuGdfJEb//m9T4RS1utQ0zqbXz9Ww6OXb8GuUyGWWF+mBXmL/m7Y8Xg56pC9Gu9MWXjKUSuj8Xfhz+FF3t4m9zgjz/pRioqq8Lnh6/gmxPXYW2pwLKxnTA2xMvkfqDG0MPXBT/P6Yf1v19D1LFUvPDFcYS1c8WUPj7o4+fy2PPoN/LL8J9zmdgck4ZbxZUIae2EpWM7oV9b6a4W8XdTYd2kp3E1pwRbYtKx5/xN/HQxC3IZ4NPcFn6uKriolJDJZCitrMaN/DIk3ixGZbUONpYyvNSzNWb08+HD5kXWytkG38/sjbnfnsPC3QmISb2Nj0YEGbusOhj2D1FUXoVtsemIOpaCwvIqTOjWCm8+2w6udtIczdfHylKBWWH+mNSrNTadTMO/f0vF5A2n4GKrxHMdPfBMgBs6eTnAzd6q3mtUVNUg8WYR4tMKcCgpB6eu5wMAevu5YEV4MHr5ufCX6//4u9nhwxeewntDA3HuRiGOX81D4s1iXL9dijPphQAEWCsVaOlojZd6tkZPXxe46fLQOegpY5feZDnaKLFxSjes+TUFKw4mIyY1Hy91skPbAJ1JLBxg2Ncj7XYptsSkYfupG9BUVqN/gCveHtIOT7WQ9sO5H8bOyhKzwvwxva8Pjl7Oxd7zN/FdXEbtszzd7JrB29kGdlYW0FWWwfZMGfI0WuSXapF+uwza/63fb+umwvzB7fBC5xa8wacBlgo5urVxRrdG7OSpVucboCJpk8tlmBXmj7B2bnhv90V8diIPP179DfMHt8NfOrgbdbDCsP+TnOIKHEjMxg9nM3EmvRAKuQzDOnnilf6+DPlHZGWpwJAgDwwJ8kC59s6I/UJGES5mFuFWcQXyNFrcLq6EdemdVSdt3VQY2N4NXVs7oau3E/9yIrPWoYU9ds3sjfUH4rEtUYNXN8ejjYsNwru1wtgQL7jZ1f8Xrr7oJex1Oh0+/PBDXL58GUqlEosXL0br1q1rzx8+fBhffvklLCwsMGbMGISHh+ujjIe6VVyBCxlFOH09HwcvZuBawZ3bn9u52+Gd59pjRHCLJr3Sw1CslQo83cb5vn3k1Wo1AgMDjVQVkX7JZDL0aW2LKc92xY8XsrDtVDr+tf8y/u+/yQhp7YTQAFf0b+uKQE87g0zz6CXsDx06BK1Wix07duDcuXP45JNPsGbNGgBAVVUVlixZgujoaFhbWyMiIgJhYWFwdRX/TbeKqhoUllUhT1OJG/llSM8vQ1p+GdJvlyH5VglySu4sXVMq5Ah0VeLtIe3xTDtXtPew49wwEYnCQiHHyC4tMbJLS6TkarDrTAaOXs7FsgOXsezAZVhZytHewx5BLe3h76pCdx8XvTwZTC9hHx8fj379+gEAgoODkZCQUHsuJSUF3t7ecHC4My0SEhKCuLg4PPfcc6K0/fkvV7Dj9A0UlGlR9oAbfZxsLOHtYou+/s3R0csBnbwc0MHTAddTkhEYyBtLiEh//FxVmD+4PeYPbo/ckkqcSMnDhYwiJGQW4T/nbqKkohquds1w+r1Boretl7DXaDRQqf7YBEyhUKC6uhoWFhbQaDSws/tjkypbW1toNA++DTw+Pv6R2+7jCPR5tjHz6zUA8oG8fCTlPX57puAZLwsAT35DU3y84Z87a459LlZ/A4bvc0P2dzsR06WszDxfK0DDdXsB8PIEnvdUAmjeqK95XHoJe5VKhdLS0tqPdTodLCwsHniutLS0TvjfFRISoo/SiIgkSS/vCnTt2hXHjh0DAJw7dw4BAQG15/z8/JCWlobCwkJotVrExcWhS5cu+iiDiIj+Rybo4UkUd1fjJCffedDFxx9/jKSkJJSVlWH8+PG1q3EEQcCYMWPw4osvil0CERH9iV7C3pQ9bFnoxo0bER0dDWfnO8sEFy1aBF9fX2OVe5/z589j+fLl2Lx5c53jprKctT711W2q/V1VVYUFCxYgMzMTWq0WM2fOxMCBA2vPm3J/P6x2U+3zmpoaLFy4ENeuXYNCocCSJUvg7f3H/vqm2ucPq9tk+luQmAMHDghvv/22IAiCcPbsWeG1116rc/6tt94SLl68aIzSHioqKkoYNmyYMG7cuDrHtVqtMGjQIKGwsFCorKwURo8eLeTk5BipyvvVV7cgmG5/R0dHC4sXLxYEQRDy8/OF0NDQ2nOm3t8N1S4IptvnBw8eFN555x1BEAQhJiamzr9NU+7zhuoWBNPpb+Nv2GBgDS0LBYDExERERUUhIiICa9euNUaJ9fL29saqVavuO/7n5axKpbJ2OaupqK9uwHT7e8iQIZg7d27txwrFH88pMPX+bqh2wHT7fNCgQfjoo48AADdv3kTz5n+sTjHlPm+obsB0+lty2yU0tCwUAIYOHYqJEydCpVJh9uzZOHLkCMLCwoxVbh2DBw9GRkbGfccfZTmrMdRXN2C6/W1rawvgTt/OmTMHb7zxRu05U+/vhmoHTLfPAcDCwgJvv/02Dh48iM8//7z2uKn3eX11A6bT35Ib2Te0LFQQBEyePBnOzs5QKpUIDQ1FUlKSsUpttMYuZzU1pt7fWVlZmDRpEkaMGIHhw4fXHjeH/q6vdlPvcwBYunQpDhw4gPfffx9lZWUAzKPPH1S3KfW35MK+oWWhGo0Gw4YNQ2lpKQRBQGxsLIKCTGtP6gcx1+WsptzfeXl5mDZtGubPn4+xY8fWOWfq/d1Q7abc57t3766d5rC2toZMJqudgjLlPm+oblPqb8muxqlvWeju3buxefNmKJVK9OrVC3PmzDF2yXVkZGTgzTffxM6dO7F3716zWc5aX92m2t+LFy/Gvn376qyaGDduHMrLy02+vx9Wu6n2eVlZGd59913k5eWhuroaL7/8MsrLy03+Nf6wuk2lvyUX9kREUiS5aRwiIili2BMRSQDDnohIAhj2REQSwLAnIpIAhj1JVmxsLHr16oXIyEhERkYiPDz8vo3a7oqMjERKSoqBKyQSj+S2SyD6s549e2LFihUAAK1WiyFDhmDEiBGwtxf/GaBExsSwJ/ofjUYDuVyOS5cuYfny5RAEAe7u7li+fHnt52RnZ+PDDz9EZWUlCgsLMWvWLAwaNAgrVqxATEwMdDodhg4diilTpmDr1q3YvXs35HI5unbtirffftuI3x1JHcOeJC0mJgaRkZGQyWSwtLTE+++/j8WLF2PFihXw8/PD1q1b60zfpKamYurUqejRowfOnDmDVatWYdCgQdi9eze2bNkCd3d37Nq1CwCwa9cuvP/++wgODsa2bdvqbLhHZGh85ZGk/Xka564FCxbAz88PAO67Jd/V1RVr1qxBdHQ0ZDIZqqurAQCffvopPv30U+Tl5dVuob1kyRJs2LABy5cvR3BwMHizOhkT36AluoebmxuuX78OAIiKisLBgwdrz61cuRIjRozAsmXL0KNHDwiCAK1Wi/379+PTTz/FN998gx9++AGZmZnYuXMnFi1ahC1btkCtVuPs2bNG+o6IOLInus+iRYuwYMECyOVyuLq6YsqUKdi0aROAOw8G+ec//4m1a9fC09MTBQUFUCqVcHBwwIgRI+Dg4IA+ffqgRYsWaNeuHcaOHQsnJye4u7ujc+fORv7OSMq4ERoRkQRwGoeISAIY9kREEsCwJyKSAIY9EZEEMOyJiCSAYU9EJAEMeyIiCWDYExFJwP8HKE2IHE2s9xQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train.Pclass) #Exploring the spread of values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the data in this feature is numerical yet they are categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bin[\"Pclass\"] = train[\"Pclass\"] #Adding Pclass directly to sub dataframes as there are no missing values\n",
    "df_con[\"Pclass\"] = train[\"Pclass\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass\n",
       "0         0       3\n",
       "1         1       1\n",
       "2         1       3\n",
       "3         1       1\n",
       "4         0       3"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bin.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature : Name\n",
    "\n",
    "Description : The name of the passenger "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Roebling, Mr. Washington Augustus II                                 1\n",
       "Dennis, Mr. Samuel                                                   1\n",
       "Watt, Mrs. James (Elizabeth \"Bessie\" Inglis Milne)                   1\n",
       "Shellard, Mr. Frederick William                                      1\n",
       "Goodwin, Master. Sidney Leonard                                      1\n",
       "Razi, Mr. Raihed                                                     1\n",
       "Bracken, Mr. James H                                                 1\n",
       "Panula, Master. Urho Abraham                                         1\n",
       "Gustafsson, Mr. Alfred Ossian                                        1\n",
       "Maenpaa, Mr. Matti Alexanteri                                        1\n",
       "Calic, Mr. Jovo                                                      1\n",
       "Carter, Rev. Ernest Courtenay                                        1\n",
       "Palsson, Master. Gosta Leonard                                       1\n",
       "Hale, Mr. Reginald                                                   1\n",
       "Harris, Mrs. Henry Birkhardt (Irene Wallach)                         1\n",
       "Andersson, Master. Sigvard Harald Elias                              1\n",
       "Duff Gordon, Lady. (Lucille Christiana Sutherland) (\"Mrs Morgan\")    1\n",
       "Van der hoef, Mr. Wyckoff                                            1\n",
       "Doharr, Mr. Tannous                                                  1\n",
       "Coxon, Mr. Daniel                                                    1\n",
       "Sdycoff, Mr. Todor                                                   1\n",
       "Reed, Mr. James George                                               1\n",
       "Wick, Mrs. George Dennick (Mary Hitchcock)                           1\n",
       "Andrews, Miss. Kornelia Theodosia                                    1\n",
       "Ivanoff, Mr. Kanio                                                   1\n",
       "Ross, Mr. John Hugo                                                  1\n",
       "Lindblom, Miss. Augusta Charlotta                                    1\n",
       "Carter, Mrs. William Ernest (Lucile Polk)                            1\n",
       "Ali, Mr. Ahmed                                                       1\n",
       "Kilgannon, Mr. Thomas J                                              1\n",
       "Brocklebank, Mr. William Alfred                                      1\n",
       "Horgan, Mr. John                                                     1\n",
       "Peduzzi, Mr. Joseph                                                  1\n",
       "Tomlin, Mr. Ernest Portage                                           1\n",
       "Dimic, Mr. Jovan                                                     1\n",
       "O'Leary, Miss. Hanora \"Norah\"                                        1\n",
       "Carrau, Mr. Francisco M                                              1\n",
       "Turpin, Mr. William John Robert                                      1\n",
       "Lemberopolous, Mr. Peter L                                           1\n",
       "Harris, Mr. Henry Birkhardt                                          1\n",
       "Gustafsson, Mr. Anders Vilhelm                                       1\n",
       "Asim, Mr. Adola                                                      1\n",
       "Nenkoff, Mr. Christo                                                 1\n",
       "Robbins, Mr. Victor                                                  1\n",
       "Emir, Mr. Farred Chehab                                              1\n",
       "Bengtsson, Mr. John Viktor                                           1\n",
       "Boulos, Mr. Hanna                                                    1\n",
       "Fynney, Mr. Joseph J                                                 1\n",
       "Christmann, Mr. Emil                                                 1\n",
       "McKane, Mr. Peter David                                              1\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To check different names \n",
    "\n",
    "train.Name.value_counts()[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature: Sex\n",
    "\n",
    "Description : The sex of the passenger\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution of sex\n",
    "\n",
    "ax,fig = plt.subplots(figsize=(20,5))\n",
    "sns.countplot(y=\"Sex\", data = train);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this data is already binary , we can add it to our sub dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bin[\"Sex\"] = train[\"Sex\"]\n",
    "\n",
    "df_bin[\"Sex\"] = np.where(df_bin[\"Sex\"]==\"female\", 1,0)\n",
    "\n",
    "df_con[\"Sex\"] = train[\"Sex\"]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex\n",
       "0         0       3    0\n",
       "1         1       1    1\n",
       "2         1       3    1\n",
       "3         1       1    1\n",
       "4         0       3    0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21b616812b0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#How does the Sex variable look compared to Survival\n",
    "\n",
    "fig,ax  = plt.subplots(figsize=(10,10))\n",
    "\n",
    "sns.distplot(df_bin.loc[df_bin['Survived'] == 1]['Sex'], kde_kws={'label': 'Survived'});\n",
    "sns.distplot(df_bin.loc[df_bin['Survived'] == 0]['Sex'], kde_kws={'label': 'Did not survive'}); #blue- survived, yellow = not survive\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More number of females survived than number of men."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature : Age\n",
    "\n",
    "Description: Age of passenger\n",
    "\n",
    "Out of 891 , 177 values are missing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22.  ],\n",
       "       [38.  ],\n",
       "       [26.  ],\n",
       "       [35.  ],\n",
       "       [35.  ],\n",
       "       [28.  ],\n",
       "       [54.  ],\n",
       "       [ 2.  ],\n",
       "       [27.  ],\n",
       "       [14.  ],\n",
       "       [ 4.  ],\n",
       "       [58.  ],\n",
       "       [20.  ],\n",
       "       [39.  ],\n",
       "       [14.  ],\n",
       "       [55.  ],\n",
       "       [ 2.  ],\n",
       "       [28.  ],\n",
       "       [31.  ],\n",
       "       [28.  ],\n",
       "       [35.  ],\n",
       "       [34.  ],\n",
       "       [15.  ],\n",
       "       [28.  ],\n",
       "       [ 8.  ],\n",
       "       [38.  ],\n",
       "       [28.  ],\n",
       "       [19.  ],\n",
       "       [28.  ],\n",
       "       [28.  ],\n",
       "       [40.  ],\n",
       "       [28.  ],\n",
       "       [28.  ],\n",
       "       [66.  ],\n",
       "       [28.  ],\n",
       "       [42.  ],\n",
       "       [28.  ],\n",
       "       [21.  ],\n",
       "       [18.  ],\n",
       "       [14.  ],\n",
       "       [40.  ],\n",
       "       [27.  ],\n",
       "       [28.  ],\n",
       "       [ 3.  ],\n",
       "       [19.  ],\n",
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      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "num_imputer = SimpleImputer(strategy = \"median\")\n",
    "\n",
    "num_feature = [\"Age\"]\n",
    "\n",
    "imputer= ColumnTransformer([(\"num_imputer\", num_imputer, num_feature)])\n",
    "\n",
    "#Transform the data \n",
    "filled_age = imputer.fit_transform(train)\n",
    "\n",
    "filled_age\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "Age_test = imputer.fit_transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"AGE\"] = filled_age\n",
    "test[\"Age_test\"] = Age_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>PC 17599</td>\n",
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       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
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      "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   AGE  \n",
       "0      0         A/5 21171   7.2500   NaN        S  22.0  \n",
       "1      0          PC 17599  71.2833   C85        C  38.0  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  26.0  \n",
       "3      0            113803  53.1000  C123        S  35.0  \n",
       "4      0            373450   8.0500   NaN        S  35.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>62.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>Wirz, Mr. Albert</td>\n",
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       "      <th>4</th>\n",
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       "      <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",
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       "      <td>12.2875</td>\n",
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      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  Age_test  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q      34.5  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S      47.0  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q      62.0  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S      27.0  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S      22.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(\"Age\", axis =1) # Dropping previous age columns which had missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.drop(\"Age\", axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "AGE              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_con[\"Age\"] = pd.cut(train[\"AGE\"],10) # bucketed into different values\n",
    "df_bin[\"Age\"] = train[\"AGE\"] #non bucketed values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "   Survived  Pclass     Sex               Age\n",
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       "1         1       1  female   (32.252, 40.21]\n",
       "2         1       3  female  (24.294, 32.252]\n",
       "3         1       1  female   (32.252, 40.21]\n",
       "4         0       3    male   (32.252, 40.21]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_con.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age\n",
       "0         0       3    0  22.0\n",
       "1         1       1    1  38.0\n",
       "2         1       3    1  26.0\n",
       "3         1       1    1  35.0\n",
       "4         0       3    0  35.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21b61568f40>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax  = plt.subplots(figsize=(10,10))\n",
    "\n",
    "sns.distplot(df_bin.loc[df_bin['Survived'] == 1]['Age'], kde_kws={'label': 'Survived'});\n",
    "sns.distplot(df_bin.loc[df_bin['Survived'] == 0]['Age'], kde_kws={'label': 'Did not Survive'});\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function to create count and distribution plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_count_dist(data, bin_df, label_column, target_column, figsize=(20, 5), use_bin_df=False):\n",
    "    \"\"\"\n",
    "    Function to plot counts and distributions of a label variable and \n",
    "    target variable side by side.\n",
    "    ::param_data:: = target dataframe\n",
    "    ::param_bin_df:: = binned dataframe for countplot\n",
    "    ::param_label_column:: = binary labelled column\n",
    "    ::param_target_column:: = column you want to view counts and distributions\n",
    "    ::param_figsize:: = size of figure (width, height)\n",
    "    ::param_use_bin_df:: = whether or not to use the bin_df, default False\n",
    "    \"\"\"\n",
    "    if use_bin_df: \n",
    "        fig = plt.figure(figsize=figsize)\n",
    "        plt.subplot(1, 2, 1)\n",
    "        sns.countplot(y=target_column, data=bin_df);\n",
    "        plt.subplot(1, 2, 2)\n",
    "        sns.distplot(data.loc[data[label_column] == 1][target_column], \n",
    "                     kde_kws={\"label\": \"Survived\"});\n",
    "        sns.distplot(data.loc[data[label_column] == 0][target_column], \n",
    "                     kde_kws={\"label\": \"Did not survive\"});\n",
    "        plt.legend()\n",
    "    else:\n",
    "        fig = plt.figure(figsize=figsize)\n",
    "        plt.subplot(1, 2, 1)\n",
    "        sns.countplot(y=target_column, data=data);\n",
    "        plt.subplot(1, 2, 2)\n",
    "        sns.distplot(data.loc[data[label_column] == 1][target_column], \n",
    "                     kde_kws={\"label\": \"Survived\"});\n",
    "        sns.distplot(data.loc[data[label_column] == 0][target_column], \n",
    "                     kde_kws={\"label\": \"Did not survive\"});\n",
    "        plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature : SibSp\n",
    "\n",
    "Description : No. of spouses/siblings the passenger has aboard the titanic\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    608\n",
       "1    209\n",
       "2     28\n",
       "4     18\n",
       "3     16\n",
       "8      7\n",
       "5      5\n",
       "Name: SibSp, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.SibSp.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add SibSp to our subset Dataframes\n",
    "\n",
    "df_bin[\"SibSp\"] = train[\"SibSp\"]\n",
    "df_con[\"SibSp\"] = train[\"SibSp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the counts of SibSp and distribution of values against Survived\n",
    "\n",
    "plot_count_dist(train,bin_df= df_bin, label_column=\"Survived\", target_column = \"SibSp\", figsize = (20,10))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Parch\n",
    "\n",
    "Description : The number of parents/children the passenger has aboard the Titanic.\n",
    "This feature is similar to SibSp\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the counts of Parch and distribution of the values\n",
    "#Against Survived\n",
    "\n",
    "plot_count_dist(train,bin_df=df_bin,label_column=\"Survived\", target_column = \"Parch\",figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    678\n",
       "1    118\n",
       "2     80\n",
       "3      5\n",
       "5      5\n",
       "4      4\n",
       "6      1\n",
       "Name: Parch, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Parch.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding Parch to our subset dataframes\n",
    "\n",
    "df_bin[\"Parch\"] = train[\"Parch\"]\n",
    "\n",
    "df_con[\"Parch\"] = train[\"Parch\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>(16.336, 24.294]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>(32.252, 40.21]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>(24.294, 32.252]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>(32.252, 40.21]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>(32.252, 40.21]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex               Age  SibSp  Parch\n",
       "0         0       3    male  (16.336, 24.294]      1      0\n",
       "1         1       1  female   (32.252, 40.21]      1      0\n",
       "2         1       3  female  (24.294, 32.252]      0      0\n",
       "3         1       1  female   (32.252, 40.21]      1      0\n",
       "4         0       3    male   (32.252, 40.21]      0      0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_con.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>AGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>35.0</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  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    male      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female      1      0   \n",
       "2                             Heikkinen, Miss. Laina  female      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female      1      0   \n",
       "4                           Allen, Mr. William Henry    male      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked   AGE  \n",
       "0         A/5 21171   7.2500   NaN        S  22.0  \n",
       "1          PC 17599  71.2833   C85        C  38.0  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S  26.0  \n",
       "3            113803  53.1000  C123        S  35.0  \n",
       "4            373450   8.0500   NaN        S  35.0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature: Ticket\n",
    "\n",
    "Description : Ticket number of boarding passenger \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "347082      7\n",
       "CA. 2343    7\n",
       "1601        7\n",
       "347088      6\n",
       "3101295     6\n",
       "           ..\n",
       "11755       1\n",
       "350026      1\n",
       "330919      1\n",
       "2689        1\n",
       "29103       1\n",
       "Name: Ticket, Length: 681, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Ticket.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "681"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train.Ticket.unique()) #681 unique values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature: Fare \n",
    "\n",
    "Description : The cost of ticket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kALBz504iIyPF+Z6hrgMHDqBpGlarVQyLpaenU6dOnQs+87fffvPalrJWi/34ofeg80/d+aQMWpIkXZ2qPLi4XC7mzJmDw+HAYrFwww038PXXX+NyuXjjjTd44IEHSmXdX3fddbz77rtA6WKUMTExWCwW0dOBkqXK27ZtA0qSKA0GA263WwSqPysrz+VuGQQkSZIuSZUHly1btmCxWAD4z3/+w5w5c7jnnnsA+PLLL8X7PMNiy5cvF8NeZrOZWrVqcebMGeLi4vD19S2V++J2u0XpGE3TSmXue6OXsRT5hw+7/bObvMqc21bVLfh3kM/p4lxLz6nTkxuqugmVpsqDS7169cSKsM8//5y8vDxGjBhBgwYNeOKJJwB466236NixI23btiU1NZWHH35YTPp7KiQ3bdqUL7/8El9fXwIDA8nIyMBisZQq/+Ln54fNZsNg8L6OQSZRSue7Z3D5vwhkPtDFkc/p6lXlwcVisZCUlISu65w9e5bBgwfzzDPPcN1113H//fezbds2xo4dK4JEUFAQt912G0uXLkVVVTHP4gkYDodD5LO4XC4SEhKAkp6LzWYDKDV0dr5/8mVyrZBfBpIkXYwqX4o8bdo0TCYTvr6+1K5dm8WLFxMaGsoNN9xAfn4+/v7+4r0mkwmj0cj48eNRVVUMbzmdTr7//nuxCZhnLsbX15dHHnmk1OcpisJdd91VSXcnSZJ0barynoun92EymTh79iyqqvLCCy/Qvn173n//fVwul+iVuN1uIiMjefvtt7n77rtFEKlWrRoWiwW3211qPkVRFIxGo/j5z0uV/2zdonu9Hv+/J76pqNuVJEm6JlR5z6Vp06aoqkpRURGqquLr68uYMWNYunQpTz75JH5+fkRHR4v3x8bGMnbs2FIrxQoKCkS9MU8pGSjJoVm4cCFQUlXZU7Dy+PHjXtuilfGSJEmSLk2VB5c9e/YAJVn1njphFouFnj17YjQa2bZtG4MGDRLvT0xM5OjRo6LeGJTMoSQnJ2OxWESOi6IoNGzYkEcffRSAwsJC0WMpq/yLDC6SJEkVo8qDi6eKsWcjL13Xefrpp9m0aRMrVqzg4MGDLF68WLx/woQJmEwlo3mxsbFi2Kt169YisHiut3r1arHRmK+vL76+vkDZE/q6ont9SZIkSZemyudcPLkn5w9zQUlP484776Rv376iIKWiKGzevFkMb9lsNoxGI6qqEhgYKLY/9tQYq1atGmfPnkXXdRRFEavIzq+8XKotl+UOJUmSrj1VHlz+yqeffkpKSoronei6zpkzZ4iJiSEzM5Njx46J9x46dAhd10VggZLgc8stt2A2m0v1VqZNm+b183o9vvEy3YkkSdK1pcqHxTw9lj/3XADRQzk/YERGRnL77bcDpTPt33777QvO9/X1xWw2l8raB9i9e/c/b7gkSZJUpirvuXiGqjxbEJ/PUwPMM/QF8OOPPxIUFASUJGB6Junz8/PFexs0aEBcXBznzp1j/fr1pbY2drlc7N+/v9QiAY/lH3Xx2saHHvv2n96mJEnSNaXKg4tnct5kMoksfI+aNWsCEB0dTVJSEqqqYrfbRda9JzDBH3u0KIrC2bNngZLeUOfOnZkzZ06paz/88MNe21LWnMunZQSda9X+XRV/zUdkAJekq0qVBxd/f3+ys7MvCCzwR8/lP//5D7NmzSIlJYX8/Hw6duzI999/L95nMBgYOnQoUDKE5hkG8/f3v2DZsdFoFD2fPytrXdgA+cUnyPIvkiRdjCqfc/FULT5//sQTaNq0aQPAuHHjyMjIwGg0ous6Bw8eJDAwkObNm4vNwsLCwmjdurUoAQMlCZrffvutuH5AQACqqpY5oS/zXCRJkipGlfdcVFXFaDTSpk0bMjMzSUhIEDtOepYpGwwGatasSUJCAoqikJ2dTXFxMUeOHBHzNB07duTFF18U55jNZkwmkwgu/v7+YhjNszXyn2kyp0WSJKlCVHlwiY6O5vTp0xw/flzUBvvuu+/Iz89n48aNTJ06lWXLlpGSkiLK5jscDrG/C0CzZs34+OOPRWCBkt6PyWSiRo0a7N69G7vdLgLRTTfd5LUtZYWWxR/f85f38Pij3136jUuSJF3Fqjy4PPDAA8ydOxdN08SKMU+dsP79++Pj44PRaKRZs2YcOXKE8PBwVqxYwcsvv8zPP/8MQP/+/VmzZs0F146Pjy81uQ8lvaBWrVp5bYsMEpIkSRWjyoOL0WgkOjqa0NBQ6tevz7p16wgPDyctLY0VK1awfv16du/ezcmTJ4mJicFsNvPEE0+QnJyMoigoisKiRYsIDw8HSobDPJuPBQUFERISwpkzZ8TnaZomVpb92QdLyrcq7OmBcsJfkiTpfFUeXHbt2oWqqmIOxel0EhAQQN26dRk0aBAnTpwASkq5pKWlUa9ePfr168eLL76IrusYDAax/Pj85cy6rpOamkrt2rVFnoyn5H6jRo28tqWsYbFnZPCQJEm6JFW+WkxRFJKTkwEoLi7G6XSSnJzMnj17yMzMFMuRAbFN8YQJE8T8iif35bnnnsNisZTawrh///4cOHCgVGBRFKVUgcvzaYr3lyRJknRpqrznEhISQlBQEPHx8YSGhmIymWjbti179+4lKSlJvO/06dMYDAZCQ0MvyOTPyMhgzpw5FBYWllrSvGLFCpo1a0ZGRgbwx2Zhy5cvZ+TIkRe0payey7tLr60kymEDZE9NkqR/psqDy9mzZ8nLy6Np06bk5OTgdrs5dOgQDz/8MGPHjqVFixa43W5q1KhBSkoKbrcbo9GIpmkEBgaSn5+PwWDg7rvvZs2aNaWqH7dr105M+p9v1y7vKebl7aUMf0R+GUuSJJ2vyoOL3W7H5XJx8OBBUQrG19eXlStXsmfPHjGHUlhYiK7r3HrrrRiNRo4dOybqiSmKws0330xGRgY//vgjULJQoF27dtSsWZNFixaVWqZ83XXXeW2LTJiUJEmqGJUeXFatWsWrr77K3r172b59O4cPH0bXdRo2bMh9993H22+/zYwZM5g9ezYHDx4U5+Xm5gKIIbTzi1lqmsaKFSv47bffxPtr1KiBwWCgefPmwB9DYgDXX3+917Y9L3sgkiRJFaJSg8tTTz3Fzp07RQ9l9OjR1KhRg8zMTE6ePMmCBQsAmD9/Pg6Hg0mTJvHggw+K8z07ShoMBtxuN/Xq1eP06dPouk5mZqYYDvPz8+PcuXPUq1ePF154AUCUjtF1vczNwmYv9z63MuohGXQkSZIuRaWuFmvYsCFjxowRq7Wys7Nxu93ouo6vr6+YqH/44YdJTEwUgcEzSa8oChs3bsTpdKJpGqdPny65CYNB5Ll45lx0XeeDDz4QRSpdLpe4fmxsrNf2qWW8JEmSpEtTqT2XsLAwPv74Y3RdZ+DAgei6ziOPPML7778vVnoFBgayc+dO7r33XuLi4jh16hQxMTEkJCRgsVjw8/O74Lq+vr7ccccdHDhwAJfLJfZuSU1NpXHjxpw6dUq8V1GUUrtSnq+sCf1XP/vr1WLj+8uejSRJ0vkqNbjExMTQunVrkpKSOHz4MAAHDhyguLgYRVEwGAzceOON4v07duwAKLV/S/v27fn1118xGAw0atSI33//HV9fXwoLCykuLsZsNoueS82aNdm/fz9QMlSmKAp2u53Dhw97LQFT3gn9GX8TfK42aw5UdQv+HariOb0o/9CRrhCVGly6dOnCJ598ApRk0CuKwsmTJ8XkvMViYfLkyWIb4nHjxvHCCy8QGRnJqVOn0HWdm2++mb1797Jjxw6OHTsGlGx9nJaWBvxRZdnlctGnTx/mzJmDw+GgoKBADK+1bdvWa/vK6rm83E/+B+sh93O5OPI5Sde6Sl8t1rJlS/bu3QuUJFCmpaWJMi4PPvggkZGRzJkzhzfeeIPGjRuzfv16VFWlU6dOWCwWnE4nPj4+pTYXO3v2LHFxceJnl8uFwWDg6NGjdO7cmffeew8oCWhWq5X69et7bZtaRnCZsrL8PZNJfWVgkiTp2lPpwaVt27YsX74cgAYNGpCfn4/D4eDs2bNiGOydd95h6tSpZGRkkJ6eTuPGjUWRyl9//VUkRnqqKNtsNpFkaTQaxWqy5cuX8+KLL5b6/KKiIk6cOOH1r0oZCCRJkipGpQaXBQsW8MUXX4hVW545kpSUFHRdF5t4DR48mFdffZXdu3ezePFijh8/jqZpYhMxz2qz8wtVzpgxg8GDB6Pruqgh1r17dz7++GPx3hYtWvDbb7+Rnp7uNbhM+Lyr13bP7LOxwp+FJEnS1axSlyLHxMQwf/58oqOjAXj33XdZvXo1nTp1wmq1kpeXB0CnTp0YP348S5YsQVVVmjdvjtlsJjQ0lPXr19OmTRsMBoPIlwkICGDDhg1iLxhN09B1naSkJLGNstvtFkmW59csO59b8f6SJEmSLk2lBpcuXbpgMpmwWCw0atSI6tWrA5Cfn09RUZHIuP/pp5+AkjkZXdfFMuTAwEB+/fVXIiMj0TRNLCkODg5m586dXHfddYSEhAAlSZPPPvusSJg0Go0i5yUgIMBr+1TF+0uSJEm6NFVSW8xisbBy5UoANmzYwO7du4mKiiI9PR2AqKgoOnfuzKefforBYKCwsJDBgwezc+dOpkyZwtGjR1EUBbPZjMViobCwkOnTpzNq1CgxZBYcHMzbb79Ny5Yt2bRpE7qui1pk59cZO5+7jPY+/4X34bKLMbeXHFKTJOnaU6WFK9euXcuKFSt48cUXSUlJYe7cuQDceOONzJ07V8yf3HTTTezevZs777yTr776CpPJJKoj22w2LBYLc+fOFVn4ZrOZ7OxsTpw4IXor8McCgPP3fDnf5RgCG7a67MD0bk8ZeCRJujpVWXBRVZXp06dTo0YN3n333VJ5KgsXLsTf319M8O/du5fw8HDmzJnD8uXL6d+/P1CyIABKkiuTkpIIDAwkJyeHgIAAcnJymDNnDo8//jjwR2/FYDCIzcn+zFVGcFnQQwYBSZKkS1HpO1FmZWXh4+OD0Whk2bJl+Pn5YTKZuOGGG1AUBaPRyEsvvURERAQ1a9bEZDLRs2dPzp49S+3atXn22WdRVRVFURgwYABQ0iMxmUyicnJOTg4mk4nmzZsTGhpa6vM1TSuz57Kgx0avL0mSJOnSVPpS5BUrVpCdnQ3Af//7X2w2GyEhIZw4cQJd11FVldmzZwPg7++P2+3m888/x2w2c+bMGaxWK35+fhQWFops/8DAQO677z6WLVsmAlRwcDCnTp0Sdcb8/PwoKipC13Xq1avntX3d15Z/buWaEvf3b5GQz+liyed0ccr5nL58sGr+QK7U4BIXF4fb7b6gcKRnst1oNAIlE/7+/v5kZWWhKAp+fn7k5+djsVh47rnnePvttwEwm82oqorT6eSrr74S+TNut5ucnBzWrVtHamoqBoNBBBZALE/+M4di9Hp84wPrK+T+rwayrMnFkc/p4sjndHH+jc+p0udc3G53qdVaLpeLI0eO4HK5CA8PF8NiK1asoHXr1nz77bdERESQk5OD2Wzmxx9/FCvCIiIiyMrKQtM0kfPioes6Dz74IF9//TU2m00UxtQ0rcyS+4p+YcVlgHvX9qmguy/tmwc/vyzXlSRJqmqVHlw8vRMoGfZ6/vnneeutt0hKSiI/P7/URL9nCXFCQgImk4ni4mLGjRtH3759UVVVbBAWHR1Nu3btWLVqFQaDgWrVqpGVlcWmTZsYMGAAs2bNQlVVDAYDBoOB4ODgMlrn4/XoNw8uvQxPQpIk6epVqRP6nTp1omvXP+Y1NE2jWrVq2O12NE0jMDAQo9HI7t27Wbt2LQsWLBDzLm63m9atW7Njxw7R8/GUfklNTWXjxpJxRV3XycjIQNM0QkND2bRpk0jO9ASYiIiIMlpoKeMlSZIkXYpKDS6JiYmsX//H/EV+fj4vvfSSmOD3DG2NHTuW5ORk1q1bJ/ZmgZKyLTVq1BA/P/vss1gsFtxuNxZLSRA4f7I+JCREzOWEhISIgpZ/HkL7g7mMlyRJknQpKn2zsJtuukkEGLfbTXZ2NqGhoWRnZ5caMgM4ceIEgYGB3HXXXXz++edkZWWV2lXy3XffFUuLw8LCyM7OJiMjAyjJZ/Hx8RG9nPz8/FI9Hs8qsvN98+D7l+W+JUmSrjWVGlxWrVrF9u3bgZIhqoSEBPz8/ETP5c+7Q95yyy3YbDbWrFkDlCwnrlu3rvh906ZNOX78eKnSLp6VYIqicOutt3Lu3DkxHObhLbAAdPtyVLnua0P32eU6T5Ik6WpVqcGlYcOGtG/fnldffZXDhw/j4+NDeno6Pj4+qKp6QUHJp556iqVLlxIWFkZ6ejr9+/cXmfwAx44dQ9M0Ue4FSmqKea67atUq7rjjDjZs2IDVahUZ/2XzvhT573T7cmy5zrucNnR/vaqbIEnSNaxSg8u4cePErpBNmzbF6XQSHBxMeHg4J0+epH379gC8/nrJF+OLL76In5+fCC5169Zlz549Yknx8OHDRU0xzwqx/Px8sbGYqqpiY7HzA0tGRkYZk/rlCy5/ZUP3mRV+TUmSpCtdlWToA3z99dcUFBRgMpk4d+4cAB988AHdunVj7NixjBgxgq+//hqn04nNZgNg4sSJ1K5dm8jISFJSUli0aJEIJJ7cF/ijxEtxcTHVqlWjoKBAFK0ELigJ84eKDy7dvny5wq95MTZ0n1YlnytJkgRVMKE/evRoxowZQ69evfj99985ffo0ubm5xMfHs3RpST7J66+/zoIFC/Dz8yMqKorvv/+eVq1aoSgKP/zwA82bNwdg8uTJjB49Gl3XRRFLHx8f/Pz8yM3NJSQkhJycHAwGA6+++iovv/wyTqeTlJQUateufWEDde/BZUOPKZfngUiSJF2lKn2zsLNnz4pVW9WqVePEiRNimGvUqJIJ9bvvvhtVVbnxxhvJysri4Ycfxul04nQ6ufPOO0UP5KWXXgJg2LBh3HfffRgMBpxOJwUFBWiaRnR0NDabDU3TGDt2LE6nE4AaNWqU0UJDGS9JkiTpUlT6sNjatWvx8yspsxISEsJTTz3FE088QXx8PFOnTgVg06ZNADz88MMYDAaWLVvGkiVLmDFjBg899BD/+9//cDgcmM1miouLee+99zAajWiaRrt27fj444+JjY3lwIEDBAQEkJeXJ4pdAmXmuWzoMakSnoIkSdLVr1L/LI+JiWHgwIFifmTevHnMnj2btm3b0rdv3wuWCPv4+FBQUMDAgQNZuHAhmqaxfPlykXHv2dclPDxcbAq2f/9+mjZtitvtpkmTJvj4lJR0OX9ORpIkSbq8KrXnkpiYyGeffSZ+btu2LYcOHaJevXrk5uZSp04doCRDPzIyktOnTwMlgSElJQWj0YjVahWT+Dt27ACgfv36xMfHA4ihLygJTp7eimcoruzSL9BtzWsVeLdXrg09xlV1EyRJuspV+oT+9OnTGTp0KABhYWHUr18fVVWpU6cOx48fB0om9MeMGYPbXbKrvc1mE1sbnz59+oLaYqqq8tZbbzFo0KBSn5WUlCSu4VktlpmZWWYJGKWMjtz6HmMq6AlIkiRdGyo1uHTp0oW9e/eKn3ft2sXw4cPp0aMHzzzzzAX7vHgCQGhoqOiB1KpVi0OHDlFcXMykSZN47LHHOHDgAE8++SQANWvWJC0tjcTERMLCwjAajZhMJrF7paewpddJfd17cLlv9V9n4K/vWb7MfkmSpKtVpZfcP9/MmTN58803Wb16NT4+PlitVqBkWOz6668nKyuLlJQUDh8+jMlkEuX3PcuOp02bhsvlIiAggJiYGOLi4khPT0fXdXx9fWnUqBFGo7FU7wX+amhM8Xp0fc+RFXvjkiRJV7lKDy5RUVE0atQIgFmzZjFx4kRatWrFkiVLSElJAf7I0A8JCWH79u306dOHTZs28fzzz7N9+3Y2bNiA2+3mnXfeoUuXLhQWFnL06FF0XRcBBEoKYzocDiwWC8HBwWRlZaHr+l9URS6r5zKnwu7/fOt7jrgs15UkSapqVdpzmTx5MlOnTsVsNhMeHi6WInsy9D2io6Px9fVl0aJFIkB4uN1uQkJCsNvtuFwujEYjiqIQHBxM9erV0TQNt9tNZmamOCcnJ8drlr78spckSaoYlR5coqOjWblyJQCxsbGlVo95eHouycnJWK1WHnvsMVatWiV+7wku0dHRQEk5fc8xVVXZv38///d//4eqqqUKVnoWAgQGBl6mu5MkSZKginsuf+f8IbTXXnuNqVOnkpSUJIKEh6+vLwCFhYXous6jjz7KCy+8gJ+fHzVr1uTEiROl3l/WsNh9q/9Xrnau7/lsuc6TJEm6Wl3RwSUrK0skQXpKxBgMBsxms1iG7PmdZ5LfkwMzduxYmjRpQvXq1Tl+/LiYizl/X5cLlbEUuefQirkhSZKka0SlB5cDBw7wxhtvsGTJEo4ePcqkSZMwGo3UrVuX6dOniy//BQsWsGTJEmw2GwMHDuTYsWMUFhYybdo03n77bVJSUnjooYdKbsJkwmazoSgKRqORlJQUdF3nyJEjJCQkAH8Mpf15t8tSdO+rxe77Yv5f3tP6XkMu9TFIkiRd1aqk5L5nY68ZM2aQl5dHtWrV2L9/P5s3b6ZTp05ASRLkokWLGDBgAKqq4nK5uOGGG+jevTuTJ08uabzJhNFoJDc3F0VRMBgMWK1W8vLyCA0N5aabbmL8+PGl2uByuUhISBDVAErzHlz+zn1flL098vpez5TrmpIkSf9mF11bzGazcfz4cZHMWB5xcXEYjUbRizhz5gzdunXj008/Rdd19u/fL97bpUsXfvrpJ4xGIxMmTCAyMpJdu3Zx7733itpin3zyiXi/2WxG0zSsVmuppEtP7oxneA3+Ls+lol+SJEnXnovquWzcuJH58+ejqipdu3ZFURSGDRt2yR/WqVMn2rRpw7RpJRtZ2Ww23n//fRYuXIjFYiEvL6/U+9u3b88HH3zA4MGDsdlshIWF0atXL958800AmjVrJnJb1q1bR/fu3UlPTyc8PJzi4mJSU1Np0qQJv/76q6igDIiqzH+2vtfTl3xPkiRJ0oUuqufy0UcfsXLlSkJCQhg2bBg//PDDJX+Qy+Vi/vz5vPrqq2iaxqZNmygsLCQiIoIWLVqgqio//vijeO+YMWMYNmwY+fn52O12VFUlPz+f1atXi9VigwYNQlEUzGYzY8aMEb2VzMxMLBYLvr6+pKWlAYjdLIFSeTKSJElSxbuonovBYMBisYiVWJ6hpkvx1VdfERERweDBgxk9erRImAwKCuLs2bM4nU6KiorYtGkTJ0+eZMeOHRQWFqIoCqqqEh0dTVJSEtWqVePUqVNAybCYrutomlaqN+LZu+W3334Tw2EWiwVVVVFVldzcXK9JlP/3xeJLvi+Adb0eL9d5kiRJV6uL6rnccMMNjBo1irS0NF555RVatmx5yR/UtWtX3nzzTbFay2g0YjAYyMzMJD8/H1VVMRgMvPTSSyxevBg/Pz+Cg4PRNA2z2Sx6Hh9//LG4ptlsFkuMExMTxTFPbszcuXNFCX6n0ynmas7P1i9NzqtIkiRVhIsKLk899RQPPvggffr04Y477rhgBdbF8Pf3JyAggICAABRFYcSIEfj5+WGz2VBVlYYNG1KjRg3y8/PJzc3lP//5D7fddhtQMkyWm5uLxWJhzJgxIqBomobRaKRRo0Y0a9YMi8VCVFQUhw8fBqBFixZERUVd0JZ69eqV0UoZXCRJkirCRQ2LPf300yxfvpyOHTv+ow9LSUnh9ddfp2bNmtx///1MmTKF1atX8+677/LLL79gs9nEkNZ1113H5MmTiYyMJCcnB7PZTP369alWrRpRUVGkpKTgdrsxGAwcPXqU+Ph43G43ycnJIvjYbDa6dOnCvn37xDFd1zlw4ABt27a9sIFl5Ln8nf9b9UmZv1vXe1CZv5MkSbpaXVRwCQ4O5uOPP6ZevXoiydHTq7hYqamp/N///R8RERFkZWWxadMm6taty8CBAyksLERVVcLCwnC73Wiaxr333oufnx/p6elASc2wJk2a8J///Ic1a9aIvVk8HnzwQb744gs0TUPXdQICApg7dy533303AHXq1MFut5ORkcHu3bu9B5dy9kLW9R5YrvMkSZKuVhcVXEJDQzl27BjHjh0Txy41uIwfPx63201ERAQhISGMGDGCDz/8kKFDh1JcXCxWfXkChtPppLi4WCw1bty4MV999RVxcXGYzWZMJhNWq5WCggIAtmzZUirY2Gw2du/eTVhYGKmpqZw5c0b8zrMk+c9kkJAkSaoYFxVcZs6cWepnT2/iUsyZM4cpU6aQlpZGcXExgYGBosfi4Vk27OfnJ7LydV3HYDCQlJRE69atGT16NI888ghut1tsAgbQqlUrNm3aJH5WFIV58+bRvHlzUlNTxUo3TdP+Ys5FkiRJqggXFVzmzp3LsmXLcLlcOBwO6taty/r16y/pgzZt2iT2bHnyySdRVZWFCxfSsGFDsdGXZ4K/du3amEwmTpw4gcvlomnTpgwaNIgJEybw+ON/LPs1mUzi/N9++w0oKad///33s2zZMhRFISAgAEAEFoPBUGaey/+tWu71+LreD13SvUqSJF3rLiq4bN26la1btzJjxgwef/xxpkyZcskf1LVrV9q0acOgQYPo3r07H3/8MW+++SYOh4PevXvTsGFDkpKSsFgszJ8/n7feeotTp07hcrmoXr06qampaJpGUVGRuKbL5eLEiRMYjUZ69OjBwoULsdvtrFmzBoD69euLvVs8iZd/zokpzfucy/+tunDPmfOt693/Ep+GJEnS1e2igktISAgWiwW73U6dOnVKfcFfrKKiIoYPH86YMWNYvHgxI0aMoHr16kybNo3CwkL27duHj48PtWrV4scff2Tnzp1izuWXX35hy5YtADRp0oTTp0+LlWJBQUFkZ2cTHByM0WjE7XaL9k2ZMoXHHnsMoNQCgJMnT5bRSu/BZV3vfpd8v5IkSdeyiwouUVFRrFq1CqvVyuzZs0uVUrlY8+fPJycnh1GjRhEZGcnKlSvp3LkzDRs2RFVVQkJCGDNmDL1796ZTp07Y7Xbq169Pw4YN2bhxI/7+/iiKQr169Thx4gSqqtKvXz/atm3LmDFjmD9/Pm63WwQRT+n906dPA4iqyefP8VyorJ7Lyku+34uxrnffy3JdSZKkqvaXweXdd99l2LBh/Pe//+XAgQN07dqVNWvW8NZbb13yBw0ZMoRt27bxyiuvcMsttwCwdu1a5s2bB0BkZCRr165l9erVpKWl4Xa7SUxM5OTJk1gsFn788Ufat2/PDz/8gKqq+Pn58fnnn7N582Z8fHyIiYkRczdQkqn/ySefiPwW+GNozFPW/0LlXYrcp1znSZIkXa3+MkN/586dJW8yGHjrrbcICAhg4MCBNGzY8JI/6N133+XcuXMMHz6c66+/nm7dujFlyhSysrLQdZ34+HixHbGqqvj7+4tSMU6nk1tvvZXq1avjcrkwGAwUFRXhcrlITU2levXqXgNGdHQ0FotF/OwJPJ7yMH+mlPN/kiRJUml/2XM5f1XVP60k3LRpU2rVqkVoaChFRUWkpKTw6KOP8vHHH+Pr6yuWJw8ePBiA4cOHi3wWl8uF2+0mMzOTkJAQcnNzgT+GuhwOB++//744pus6DodDLGeGP3otADk5OV4LV37du/c/ukdJkiSpxF8Gl/OHlM7/5/IoLi4WX/But5u8vDx++uknCgsLcTqdBAcHM2jQIKZOnUrNmjXRdZ1atWpx6tQpzGYzLpeL0NBQwsLCxM6Tfn5+2O12cnNz8fX1xel0ltrO2FNVGRABzGAweA0sAPev+vIf3eOl+rp390r9PEmSpMryl8Hl8OHD9O/fXwxbef5ZURQ+++yvl+f+mdFo5JZbbmH06NFiBZdnP5datWrhdDrFTpUFBQUYjUZRWt8TXNLS0kSipcFgYNasWYwYMYLi4mJCQkLIz89n5syZTJgwAShZoVarVi1Onz6Nw+EA+MsJ/bKGuL7q/eAl3askSdK17i+Dy1dffVVhH/Tggw+SmprKI488QmFhISEhIVSrVo2AgADRy1izZg0zZ84kIyODuXPnip6O2WwWBS09w16qqjJs2DDMZjMAt99+O59//jmvv/46RqMRVVWxWCxkZ2cDiJpomqZx7NgxmjZt6qWVcv5EkiSpIvxlcKlVq1aFfVBBQQG9e/dGVVV0XeeRRx5hw4YNhIWF4XQ6SUhIwGg0cv/99zNgwAB0XSc6OpqUlBTy8vJEcFiwYAFPPvkkFosFl8slJvi3bduGrusUFBSIoBQbG8uBAwcoKCjAZDKJXkvZwcX7+oYHVq37y3v7qvf/lf/BSJIkXYUuKs+lIrzwwgui52E2m1myZAkWi4WcnBxcLhdGoxFfX18A4uLimDFjBps2bSIpKQn4Y0L+ww8/RFEUnE6nmE+xWCy88sorPPPMMyK5Utd1nE4n8fHxaJqGy+USu1F6DyxQ3p7LA6surRTOv97hU1Xdgn8H+Zwuzl88p69631eJDZEqUqUFF09w8AQQp9NZKunR7Xaj6zoDBw6kZcuWLFy4kLS0NDRNw2KxiB0lX3jhBfr1K8mY95zbuXNnr0uO9+3bR506dcQSZ0815LKCy79pWfHa3t2q5HOPHj1Ks2bNquSz/03kc7o48jldvSotuFgsFnx8fGjevHmp0v1WqxWTyYTb7ebmm2/m3XffZfTo0SiKwokTJzAYDNSoUYOEhAQAHnrojyKSBoMBTdP47rvvSE1NFcd1XcdqtfLmm2/Srl07cezvVNUXtiRJ0tWm0oLLvHnzGD9+PDt37kRVVQICArDZbFgsFjRNw+l0sn37dgBatmzJO++8w8CBA9m5c6fYthhKJvc9PRBPz8fHx4f8/HwAgoKCsNvtGI1G1q9fT2FhIfBHIPorD676zuvxtb3v+cf3L0mSdC2ptOCydu1a9u7dS0REBIAIBh07duTll1/m9ttvF5P28fHxOBwOPvvsMxEcoCRAeJIiDQaDmFtRVZWUlJRS1/XUP/NM4nsSLj07VXrL2ylrWKz7qu//8f1fVQ4nVXULSvmyd+eqboIkSX9SacHlyJEj2Gw2nE4nkZGRFBUVYTabOXjwIPfddx8+Pj6iFH5cXBz3338/brebtWvXlup13HnnnWzatAlN08SWyUVFRSLYaJpGUFAQNpuNmjVrimXJvr6+olqy2+0WS5hL+8tqOGX6svfd5Trv30iOkUuSdDEqLbiYTCYMBgMxMTGkpqZSq1YtoqOjSUxMpKCggBo1anDrrbcCJUug77//fp599lnMZjOqqopejafemaIoZGRkYDAYMJvN+Pj4UFhYiNlspmnTpsTFxZGUlISu6+i6jt1uF23xHligvKvFuq/aXK7z/s6Xve+6LNeVJEm63CotuPj4+GA0Gjl58iQmkwmj0ciAAQN47rnn0DSNwsJChg8fDpT0LF555RUeeOABVq5cKbY61jQNu93O3Xffzc6dO7Hb7VgsFurXr4/NZsNut4tilna7ndq1a4tVZoGBgdjt9r+cd/mnJW4u1Zped1bq50mSJFWWSgsuderUwdfXl7lz59K0aVN69OjBypUradasGa+88gqLFi3i119/5a677qJ69ers3r2btWvXUq1aNRRFoV27dnzzzTc4nU42b94sVn9ZrVaOHz8u9moxGo2EhoaSlJREdnY2kZGRpKWlUVBQINried+FyjcsVl49vthS7nPX9Lq9AlsiSZJUsSotuJw6dQqTycS7776Ly+UiOzubRo0a8fvvv7NgwQK2b9/OoUOHRB0zu91O3bp18ff3p1WrVhw9epQGDRqQnZ0t6otBSYVjD0/v5sSJE1gsFkJCQsjLy7ugLefOnSMmJuaC4/ILW5IkqWJUWnCZMmUKU6ZMwWazMXToUKZPn46iKBQUFLBt2zbq1q3LO++8Q79+/SguLsZqtRISEkJSUhKff/45b7/9NmPHjqWgoAAfH59Sy499fX2pV68ex48fx8fHh/79+3PgwAG+/fZb6tevz9GjR7FarSJxs6w5l55fbCvXva3udWu5n4skSdLVqNKCi6qqjBo1ip9++onIyEhat25N7969iYmJwWq1YjQamTNnDkajEYPBwI4dO9A0jcGDB7Nr1y4WLFhA7dq1+f333/H19b1gmOuTTz6hQ4cOOBwOvvzySwIDA3E6neTm5or9XTzzLSEhIWW0snzDYj2/2FGu8/7O6l63XJbrSpIkXW6Vmufy888/895772EwGJg5cyYul4shQ4bQrVs3Hn30Ub777jsmT57Mp59+yu7du8nNzSU+Pp6YmBhGjRrFyJEjsVgsIvfFs8zY7XbTrl07VFXFZDIxa9YsnnrqKbp3786aNWuAksl6z/sTEhK8loBRLsOcyxe9bq7wa0qSJF3pKi24HDp0CJfLxfDhw3G5XPj7+2MymXj55Zd56aWXcDgctGzZkvvvv58NGzYwd+5ckpOT0TSN9PR0/vvf/5KZmYnZbMZgMJTafKxx48YUFhZy9uxZ3G43Tz31FIqicPDgQaKiojh9+jSapollzYcPH76kqsj/RK8v9lT4NQG+6HXjZbmuJElSRai04NK+fXuKiorQNI2NGzcSExPDkSNHmD17Nm+99RaBgYFiLuSmm26iW7duvPzyyxw+fJjZs2dz0003ceONN1JcXCx6IW63G4Ds7GyxGZjZbOaXX36hV69eBAcHi6XInirJAK1atSqjleVbivxFrxvKdZ4kSdLVqtKCS6dOnbjjjjswmUzExsby1ltv4XK52LFjB+fOnUPTNFRVpU+fPgwdOpSnn36aEydOEBwczMqVK1m0aJFYQuwZCvPIysoSSZYul4tbbrkFTdOYPXu2qENmMpnQNA1N0wgPD/faxvIOi/X+Yn+5zvs7q3pdf1muK0mSdLlVWnBZtWoVs2bNwmaz4Xa7CQwMxGg08tNPP4k9XmrXrk14eDht2rQhIiKCkydPkpOTw65du1BVFbPZzB133MHmzZtLbVesaRqBgYHk5eVhMpkwm80UFRXRtWtXNm7cCFAqGHl6ORe0UX6ZS5IkVYhKCy7x8fE4nU7atGnD2bNnSU9PR1EU8vPz0TQNf39/CgsLCQ8PZ+LEifTv35/jx4+TlZVF48aNSUtLIzk5mcTERBFYTCaTKFzp6+tLfn4+ERER5OfnYzAYaNiwIVlZWcAfk/8AoaGhXtvY54tDlfMwKsHnvVpUdRMkSbqGVVpwadq0KU8++STPPvss48aN4+uvv6Z58+b07NmTrKwsFi1aRG5uLl26dOG2224DYMOGDRw+fJjly5czatQoUlJSOHv27AUVkT11xnRd5+abb2b9+vUYDAYcDgc+Pj44nU5RCVnXdc6cOVPGajFvWfuXz8pesgCkJElXp0oLLvfccw8TJkygVatWFBcX88ADDzBs2DAmTpxIfHw8qqqyevVqhg4dynXXXcfo0aOpV68eOTk5tGzZEpfLRf369cnOziYgIID09HRx7bp165KdnU1BQQFff/01VquV+vXrU79+faKjozl69GipmmLHjh27pNViK3s1qejHIUmSdFWrtGJanpL6zZs3p3nz5qxbt46goCAaNWqE3W4nNDSU6OhoFEVh5syZ1KxZE13XKS4uJjY2lhYtWnDq1Cl8fX1LBRar1Up0dDRNmzYVE/YREREkJiZSXFws9nnx9/cXWyw3aeI9WCiK0etLkiRJujSV1nN56qmnOHHiBD///DPffPMNI0aM4KeffmLv3r2oqkpaWhodO3ZkzJgx+Pj4ALB8+XJyc3M5evQomzZtYtiwYcyfP5/u3buL67pcLrZt+6Nsi67r+Pv7c/r0ab744gtRat+zDPqvlLVarP/qU+W+78961i/3uZIkSf9WlRZcXn31VTp37kyzZs3QdZ369eszc+ZMnE6nKDjZuXNntm7dyocffsjw4cPFhL3nnPNXl3mqInu2Og4LCyMvLw9N0zh9+jQmk4k+ffpw+PBhkpOTS7WlXr16XttoLGPO5dOedSr2YUiSJF3lKi24/P7773Tr1k0kUQYFBVGvXj26d+/OwoULiYuLo127duzfv58ZM2YwevRoateuTUFBAfv376eoqIibb76Zd955B6BUgNF1nezsbDFp7+vri67r5OfnU6tWLZHp75GTk0ONGjUuaKMMIpIkSRWjypIoZ86cSfXq1dm7dy+RkZEUFhYyceJE3nvvPV566SXGjRvHI488gtPppF27dtSpU0fMqZhMJlwul7i2j48PMTExJCQkoCiK6MGMHz9eFKk0mUwoioLL5eKrr77imWeeuaCNI9d43xv+zR7Rl+WZSJIkXa0qbUJ/9erV9O3bl1tuuYWZM2cSFBSEpmnExMRgNBpRFIWOHTvy7LPPcuzYMUaOHIndbhe5KfHx8WiaRmxsLAEBAUBJ7orJZKK4uFjk0fTv3x8/Pz90XRfbJENJ5WRPQIqMjPTaRguK15ckSZJ0aSp1m+Pc3Fzq169PZmYmCQkJ1KxZE7vdTl5eHrquExgYSHh4ONdddx2jRo3ixRdfZPfu3QQHB4vS+W63W2wQVq1aNbKzswGIiIggMzOTjz/+GCjZOKxFixasXLkSoFSeS3S0956IuYxAMnFNstfj/9TUHjUvy3UlSZKqWqUFF5fLJeqGLV++nNdff51vvvkGVVW57777sFgsnDlzhhdffJFOnToBf8yrZGVlMWXKFEaPHk1qaipQ0mtJT0/HaDRiNBqpXbs2DoeD/Px8goODadasGR07diQ2NpaUlBQsFgtGo5GioiLWrl3LDTdcWGzS+xZif+8VGSQkSZJKqfQkyvbt25OTk8OwYcPw8fEhKysLs9lM06ZNWbFiBQBjx45lxIgR1KxZk06dOpGQkMCECROAkiXFRuMfq7o8e7gUFRWRn59PaGgoRUVFVKtWDYCdO3cCJdWSPcuSExISvLaxrJ6LJEmSdGkqLbj4+fnx9ttvA5CRkUHfvn0ZPHgwGzdupEePHgwdOlS89/XXX+fLL7/k1KlTDBw4kI4dO/LAAw+QmpoqSrlA6c3Cjhw5AoDdbsdsNvPtt9+yePFi7rjjDtatWycCC0Dz5s29ttFURnAZ0yOqQp6BJEnStaLSgsvq1atZunQpFosFKOlxHD9+nDlz5lCrVi0yMzN56aWXROn8e+65h+XLl/PSSy8BEBgYSLVq1dB1nZycHNxut5jsj4yMZNq0aQwdOhSn0ymWIjdo0IBvv/0WAIvFgtvtRtM0evTo4bWNMohIkiRVjEot/5Kbm4umafz+++8EBQUxdepUAgMDadOmDfHx8bRr146TJ08ybNgwBg4cSGFhIUVFRSiKgs1mQ9d1HnnkkVLDYgD5+fksXbpUrAwrLCzEbDbTvHlzDhw4AIDT6RS5LtWrV6+s25YkSbomVVrP5b777uOuu+5i9OjRhIaG0qBBA3bv3k1sbCw///wzgOiJaJrG2LFj+emnn0hISKB+/fpYrVauv/561q9fj6KUHr5yOBz8/PPPYl+X8PBwUlJSmDVr1gXvBUrlyJzvg9XpXo9fiZ7uKQOkJElXrkoLLgCzZ8/GbreTnZ3NbbfdRmJiInfddRdr1qyhqKiIoqIiAA4fPsxNN93ERx99hK7rpKenExUVxbJly4iIiPAaMM7XrVs3li5dSmFhIXfccQebNm0q9fs/93zE8TKuN1h+kUuSJF2SSp1zCQsL46OPPqJfv368/fbbfPjhh4wePZr09HSuu+468vPzgT9yUkJDQ2ndujWRkZEMHz6c++67D03TRBDy8JR8KSwspLCwkNq1a5Ofn8+tt97K/v37MZlMPPXUU7z33nsApKamitVk5zOWMaH/0eqMct/3Yz0jyn2uJEnSv1WlBZcPPvgAp9PJjh07iI+Pp7i4mPXr12O1WvH392ffvn243W7GjRsnEiPvuOMO5s2bR6NGjfDz87vgmnXr1iUxMRFd10XA0XWdGTNmEBERQc2aNZk/fz6qqorAAnDixAliY2MvuF5ZPZeBMkBIkiRdkkrtuUyYMIGUlBTsdjtDhgwhJCSEuLg4VFUlPDyc6OhopkyZwuOPP86GDRvIzc3l5MmTJCQksGbNGlFbzMfHh+LiYs6cOQPALbfcwpEjR3A4HLjdbpo0aUJycjK33norDoejVHY+QFSU91VhRt1725d9Uf6ey+XycC8Z8CRJunJVap7LG2+8wYgRI7BYLLRp04a5c+eyePFitm3bxoEDBzh37hwALVq0YPHixWiahsFgIDY2FkVROHz4sFg9Boggs2/fPhRFobi4GIB9+/YRERHB//73P7H02RNYANq1a+e1jfILW5IkqWJU6oR+69atRb7J5MmTcTgczJo1i6SkJIKDg7nhhhto0KAB7777LgAHDhxg7NixaJrGb7/9RkhIiEiahJLlxZ7/j46OJimppKqxyWQiNzeXrl27kpCQwIYNG7BarSKR0u12YzJV6q1LkiRdUyrtG3bp0qWoqkrLli05evQosbGx7Ny5k9zcXDER7ymPLxpnMuHv78+hQ4eAknwWs9kseiHnl97PyCgZurJYLISEhJCdnY3JZGL37t1iOM0jIyPD634uaz/P9Nr2B/uE/+P7lyRJupZUWhKlp1rxvn37KCgo4OjRo0DJF73D4SAwMFCsFvM4e/YsCQkJDB8+HKvVisViITg4GChZIaZpmhgi8yRQeqomu91uFi9ejNlsxmAwoKqqyP4PDQ312kaD7v0lSZIkXZpKCy69e/fGYDDg7++PwWDg3LlzuFwuIiMjsVqtZGVl4e/vX+qc+fPnY7fb2bZtGw6HA6fTSWhoqAgoqqqK7Y9r1KiBwWCgXr16RESUzJ34+/uLXosnQ9+zbNkbQxkvSZIk6dJU2rBYmzZtiIiIYMuWLfTp04eDBw9SXFws6n158lTgj6rIZ86cQdd19uzZA5T0VuLi4kpNzns2DAsICMBqtXL27FlRKTkoKEiU6Pfx8SlVj8ybsnop36zwPlzmcW8/OWwmSZJ0vkoLLv/73//Izs5m8ODBIji0bt2aUaNGMW/ePOLj48UmXq+//joAd911F7/88gstWrRgx44d+Pn5lapurCgKBoOB4uJiDh8+jNPpJDAwkJo1axIXF0fLli2JiIggPT1dTP5DSfkXzzDa+co7BPbtZ38dfKpCl/4y4EmSVHUqLbi0aNGC3bt3s2PHDhFcHnvsMQYPHozb7UbXdTp06ADA3XffzRtvvEFwcDA33XQTkydP5rbbbsNms2E2mzEajTgcDoxGI5qmERwcTHBwMImJidhsNo4fP85NN91Ehw4dyMrKEm3wfK63wALyC1mSJKmiVNqUwuDBg1EUBT8/P/z9/QkMDCQuLo5q1aqJOZP27dsD0LZtWyIjI3n++efRNI17770XXdfFMmYPzzBXXl4eiYmJwB8BZM+ePbz33nu0bdu21PG/q0smSZIk/XOV1nN5//33RVKkpmm43W5WrFhBbm6uyEHZsmULAwcOFMNin3zyCbt27RIT8T4+PhgMBhwOR6mMe6PRSNu2bdmzZw+KoqAoCrVq1eLBBx9k6dKlQElvxbNsuaw8ly1LvWfi3z5AJldKkiRdikoLLg0aNBABQdd1fH19yc7Oxt/fH6PRSHFxMUuWLGHgwIFiQv+3337D6XQSExPDyZMnURSl1NyJh6ZpHDp0SAQbXdfJzc0lNDRU1Cnz9Hh0XS8zgbKsOZefl/x1+ZcOA2XwkSRJOl+lBRdPvolnWCo3NxcovYorLS0N+GNCf8uWLQCkpKSg6zr5+fkiV+V8uq4THh5OYmKimE+x2+2sWLGCJk2aiJwaD1VVvZbdL++E/rZPrrzaY7cOkgFPkqSqU2nBJS4uDovFwu7du9m3bx+PPPIIAA8++CBDhw7l9ttvvyBweIaymjdvzt69e0WvxzO05ukJGY1GUZesWbNmpKamkpaWRnx8POfOncNoNKIoiui9lLWfS3mDyy2Pyi9ySZKk81VacFFVFZfLxY033oimaRiNRlRV5fvvv+fzzz/H5XKhKAqff/45e/bsYcSIEURERIiKxmazGafTidVq5bnnniM9PZ3FixcD0LNnTzZv3kxWVlap4bEDBw7g6+uL0+mkuLhYfGZOTo7XLH2DLtPxJUmSKkKlBRdfX1/MZjOqqqKqKpqm0aFDB+Li4rDZbISFhdG5c2dSU1MpLi5m3LhxJCcnAxAQEFBqldfrr79eKpFy3bp1+Pv7i56Mj48PRUVF9O3bly1btpTaRtlisZRZ/uXmx+SOk5IkSRWh0pYit23bFrfbLcq4mM1m7r77bjHPkpuby4kTJ7jjjjtwu908+uijtG3bFpvNxq+//orT6cRoNBIbG3vB8JnL5eLFF19E13WxU6XBYKBVq1bs27ev1Hu9LQiQJEmSKlal9VxmzpyJpml07NiRjIwM4uLiWLdunZj/iIqKolu3bowePZqNGzeiKAqzZs0iICCAffv28eyzz7J582by8vJE8qTJZBIl+MePHw/AjTfeSGpqKmfPnhW7VAKlli7v2LGDW2655YI2/rYg3Wvb2zwlezSSJEmXotJ6Lvn5+SiKwi+//MLRo0fx8/Nj+vTpWK1WIiMjcTqdxMbG4uPjw4gRI0hOTsbX1xer1Uq7du3YtGkTULJyLDAwEEDkrdSpU4fq1UsCwNGjR8WWx7m5udSpUweTyYTVahVt8fSW/kxWRZYkSaoYlRZcPMUkd+/ezfvvv09eXh7+/v5i62On00lAQABFRUW8+eab1KxZk6ioKKKjo+nYsSM+Pj5iyCs7O1usGtN1nYSEBDE/Y7PZyMvLQ1EUCgsLCQsLQ1VVEXAAYmNjvbbRoOteX5IkSdKlqbRhsbCwMHJzc/Hz86NJkyYAZGdn07t3b+bNm0deXh4DBgxg9uzZGI1G3n//ffbv309RURG5ubl07NiR7777DlVViYqKIisrSywMCAsLIywsjPj4eKCk1H5eXh6RkZEcPXr0gqGxevXqeW2jQfN6mCPvee/peDQfGlnOpyJJknR1qrSey7333ovdbqdnz5489thjGAwG3nzzTc6cOUPt2rVp2rQpd999Nx06dOCJJ55gx44d9O3bl7CwMBwOh0ioNBgMpKam4nQ6Rfn82NhYatWqJX4fExOD1WoVAQ3AarWKzPy/ynMpz0uSJEkqrdKCS+3atTGbzRw7dozTp0+jKAoDBgxg/PjxHDlyhKSkJF544QUAfv/9d6pXr86hQ4dIT08Xy5P9/f0xm808+uijpa6dkJDAsWPHgJJSML///jsxMTF06NBBrCwrKioSczSekjB/JoOLJElSxai0YbGePXvSs2dPAKZMmULTpk2JjY0lICCAsLAwBg8eLCblu3btyq+//orb7SYsLIyCggJcLhdFRUVomsZHH30krmu1WkVFZIDAwECio6M5evQoK1asIDw8nLS0tFKrxX766Sd69ep1QRsbPyuHtyRJkipCpQWX1atXs2bNGmw2GwkJCaxatYpmzZpRVFSEqqosXLiQW2+9lZiYGEJCQujVqxc9e/ZkypQpfPvtt4SFhZGTk4PRaBT5LAaDAafTSa1atXC73WRkZFCjRg3y8vIAmDt3rqg15lm2rGkaXbt29drGM2+lej1e94Woy/NQJEmSrlKVFlw8UlJSRBXkxYsX43A4AHA4HOzatYuYmBjatm3LJ598Qnx8PNu3b0fTNPLz89E0jYiICDIySgpFevJdnE4nRUVF6LrOyZMnUVUVg8GA2WwWO1d6hsSAUtn95zOWMcR19k3vQedyqz1SBjVJkv6dKjW4uN1u7HY7mlayLOvbb78lJCSEgoICNE1j9erV9OnThzvvvJM9e/awbds2CgoKMJlMYu7EYrEAJSVh/Pz8yMvLw263i6KWFotFDJ+1bt2alJQUfv/9d7HRmKZpzJs3jwkTJlzQPoPmPbrUGl3jMj0RSZKkq1OlTegDFBQUiCAAJbkvnn92u92ltiQeO3Ys99xzD6qqEhERgdvtJiAgQMzbuN1uMUnvcrlo27YtZrO51F4tTZs2pVGjRphMJtxutwhMv//+u9f2GVTvL0mSJOnSVGrPpbCwEJfLVWopsNVqpaioCEVRxNCVZ7Owr7/+GoDk5GSMRiM2mw2z2UxwcDB5eXliSK1hw4Y8//zz9O3bt9TwV5s2bVi+fLkote95f+vWrb03UPGe6JLy+rly33ONsbXKfa4kSdK/VaWuFjObzYwbN47AwEBGjhzJpEmTuOmmm9i8eTP+/v4UFxcDJVWPV69eLfZoMZvNIkDMmzevVAAxGAwkJCQwZcqUCz5z4sSJTJ06ldGjR1NQUCCON27cuIxWlpFF+TdqjK1drvMkSZKuVpXac/nxxx/RNI2oqCj69evHwoUL+f777ykqKsLHx4d27doBJT2Xpk2bYrFYcDqdFwST83nKwhQWFor9WqAkGz8oKIiQkBBsNlup8318fLy2TwYJSZKkilHpq8UURRGT6QMGDGD69OkYjUaMRmOp3kfXrl3p1q0bnTp1wu12ExQURH5+PrVr1y5V5sUzeW+320Vgad68OcePHyc7O5shQ4aUKv+iaRq3336717alvhFfrnuKGt2wXOdJkiRdrSotuLhcLn799Vd0XWfu3LlERETw3nvvYbVa6dixI0ajkffee49JkyaJc6ZPny56LYWFhei6zpkzZ8TvPcf8/f157LHHeP311wFEPbEmTZpgMBj48ccfS7Vlzpw5vPzyyxc2sow5l7+TOjuuXOdFjSpreE6SJOnfrdJWi23evJmcnBz8/PzIz89n+vTpWCwWHA4HP/74I/v37xf5Kx6tWrUCIDw8XCRDNmnSRCwI8PRIAgIC6NSpkzjP19cXgF9++YX09HSxTbLHDz/8UEYrtQp/RY1qXOZLkiTpalVpPZfU1FRcLheRkZHYbDays7Px8/PDYrGgaRq5ublinxYPzx4uBQUFOJ1OFEXh1KlTYvjLw8fHh4kTJ4qfNU0T2yK73W4RhPz8/ERvx6ty9lyiRjYv13mSJElXq0oLLp5MeZPJRGpqKv7+/tSpU4epU6cyffp0EhIS2Lx5MydPnuTpp59m1KhRosClJ0DExMSQm5srgoeu6xQXF+N2u7nzzjvZtWsXgFh1ZjAYxCozt9sthtjuuOOOMlrpPehEjfS+/4skSZLkXaUFl2PHjomVXoqiYLfbmTdvHtu3bxe9iUcffZQ5c+agqiojR47krbfe4sSJEzz66KMsWLCAzMxMiouL0XUdo9EogkVycjJvvPEGANdddx15eXmcOnWKu+66Swy1nd9bee6558popfeeS+qbB//y3qJGtryURyFJknTVq7TgkpiYiKqqYnhM13UOHDjA5MmTycjIwNfXl4iICE6cOMGcOXMwGo20a9eOrKwsCgsLcTqdBAUFoaqqWJ5sMBhEKRnPMNiJEydEL6lx48YcPXr0graEh4d7baMMEpIkSRWjUofFNE0T5VmKi4t58cUXCQwMFHMqkyZNYunSpUyYMIH+/fuzdu1akpOTWbRoEbqu06xZMxITE8nJyaGwsFAEFqPRSJ06dYiPjy81p3LzzTezZMkS8R5N09B1nWPHjtG0adML2pg2Z3+F33fkiOsr/JqSJElXukoLLuHh4SQnJ9OkSROSk5NJS0sjOTkZk8mErusEBAQAMH78eFq2bElUVBTjxo3jzTffpFq1auzbt49Tp06Rn59PUVERgOi5qKoqSrtomobVasXtdpOfnw+U9GqsVivFxcW4XC4OHz7sNbiUN0M/csQN5TpPkiTpalVpwaVGjRocPXqUgwcP4nK5CAgIIDg4mNTUknL2DoeD2267jePHj7N161aOHj1KXl4eYWFhWK1WrFYrGRkZBAYG4uPjg7+/vygP07x5c/7v//6PWbNmoes6TqcTi8VCWFgYTZo0YdeuXei6Lib3PZuSXah8wSVtzu5ynRc54qZynSdJknSlq7Tgkp2djb+/P6GhobhcLs6cOUPTpk0JDg7mzJkzuFwu9uzZw7hx45g/fz5+fn5kZWVx+vRpxo4dS0ZGBseOHaOoqAi32027du1EcDl9+jTz588Xw2HVqlUjPT2d9evXi/d45mEAbrnlFu+NVMq3Z3Hkf24u13mSJElXq8uaRHngwAEGDhwIQNu2bTEYDGRkZGC32zGbzRw6dAhd1wkKCiIwMJC2bdsSFRVF69atxQZhAG+//TYnTpzAYrGI4bONGzeKzwkKChJDZWazmfz8fAwGA40bNyY7O7v0DRv+6pbLmywpSZIkne+yBZcFCxbw8ssvi5yTY8eOkZeXh67r5ObmiqKUxcXFFBUVkZ+fj6qq5OTk8N1335GVlUVAQAAul4tRo0ahaRphYWEi6fJ8Pj4+1KxZEygJNJ4aYocOHSo1PwMlczJ/rgTwB72cL0mSJOl8l21YLCYmhnnz5jF27FgAbr/9dpKTk8U2xNHR0cTHx3Prrbfy9ddf4+vry6+//sqgQYPQNE0Eifz8fMaPHw9Abm5uqex8z0qzxMREjEYjYWFhZGdnoygKiqLw8MMPs2bNGpxOZ6m2lVUVOfI/7S/T05AkSbq2XLbg0qVLF5KSksTPffv2pU6dOrz22mtij3s/Pz8CAwO55557+OKLLwgICKBFixaYzWZiY2PZunUrgEicVFVVlNg/fxdLX19fzGYz2dnZREZGkpmZSVRUFIcPH8bf3x+n01lq2XJYWJjXNqfN3VLhzyHyee8VmCVJkq5mly24HDhwgOnTpwMQHx/PxIkTKSgowG63ExQUhMFgQNd1PvroI5FU6evrS1hYGNOmTWPSpEliEj4iIoK8vDwUReHHH3/kxhtvRFEUsW1xvXr1sNlsFBQUkJmZKZI1jUajmLfx5LmoqkpWVhbVqlW7sNHlrC32V9Lm/Vjm7yKfu7PCP0+SJOlKcFmCy4IFC1ixYgVZWVk0atSIN998k5EjR/LLL7+wcOFCnnnmGVavXo3T6aROnTpomkZSUpIoXHn//fdjs9mYMmUKuq6Tn59PdHQ0SUlJ3HPPPQCiujLAmTNnRC/Gx8cHPz8/srOzOXjwoBhG03Vd1CPbsmULPXv2vKDdSjnnT6o/d1e5zpMkSbpaXZYJ/bi4ONFTgJKtiQMCAtiyZQu6rhMaGorb7cbtdpOamiqGz5KTk9F1neuvv5533nlHlHRJS0vDZrPRp08fEVDsdjtWqxWADz74gOuvL8mELywsJCcnB6PRyN69e1EUBaPRKIbTAHJycspouVwtJkmSVBEuS8+lU6dOtGnThmnTprFy5Up27NjBU089JVZxnTlzBpvNRnh4uKhqrKoqvr6+KIpCSEhIqXkSTy9lxYoVNGzYUOxEWVBQAMCQIUNKbYUMJT2Y9u3bk5SUhM1mK7VqrGPHjmW0vHw9l/R535f5u+rPdS7XNSVJkv7NLktwSUxM5OOPPxZzHNOmTaNGjRoEBwdz/PhxDh48yKOPPsr06dP5/vvveeaZZzhx4oTIoG/WrBk7d+4U1zs/u96TFGk0GgkNDSU7Oxtd13G5XFgsFlRVRdM0bDYbAwcO5KOPPirVNrPZTKNGjbw3vJxzLtWHdynXeZIkSVeryxJcjh07JnoV999/PydPnsTHx4esrCycTic7d+6kQ4cO+Pj48Omnn5KSkgKUTNwD3HXXXQwdOpQnnniCvLw8rFYrRUVFKIpCREQEiYmJKIpCbm4uvr6+zJo1ixdeeAFVVWnatCk5OTkkJycze/Zs0fvx1CHTNK3MwpUySEiSJFWMCg8uLpeLQ4cOierHzzzzDGPHjkVVVZFQWadOHTp37sz06dNFxWOj0UhQUBDx8fHs27eP++67j7y8PACysrKAkiXHbdu2JTExEbfbjcFgoLCwkIMHD2I2m3G73Rw7dgxVVQkKCiIjIwOj0Sh6M1DScykruKS/s75c91x9+H3lOk+SJOlqVeHB5auvvuKWW26hdevWTJgwgdmzZwPQv39/jh8/zt69ezl8+DDr168nMDBQBA5d13E4HJw8eZK1a9eyZcsWsbrLo1atWiL3xWAwYDabKS4uZunSpRQVFZXK3C8qKqJFixb8+uuvYsWYoigUFxcTG1vGzpLlHBZL/9/XZf6u+rP3l+uakiRJ/2YVHlzuvvtufvnlFxYvXgyU9DoURcFsNuPj44PZbAZg1qxZFBcXiyEvTdNITU2lS5curFy5ksmTJ5OVlYXBYBDbFZvNZgYMGMDbb7+Nj4+PGCqrWbMmp06dQtM0jEYjZrMZh8NBp06d+OSTTwBEoLJarWXPuZSx8qv6sw9W9GOSJEm6qlX4UuRNmzYRHh7O4MGD0XUdi8XCzTffzPr169mxYwdOpxOj0UhAQAAPPfSQCDaKotCjRw/i4+OZOnWq2NirSZMmGI1GgoODSUhIoEOHDgBi9Zeu69SuXVv0TjRNE5P/nrme89lstr9ovawdJkmSVBEqvOfStWtX6tSpw+OPP05YWBh+fn6MGzeOfv36oes6ZrOZsLAw7HY7DoeDl156iXHjxuHj48MNN9xASkoKZ86coWbNmhiNRo4ePYqiKPj5+aGqKjNmzABKglGjRo04ceIEW7duFT2W4uJiUaSyT58+vPbaawDifIfDIRIqL+S955L+v9UV/ZgAqP7shYmckiRJV4MKDy5FRUUMHToUt9tNXl4ehYWF9O7dm4YNGzJ8+HCef/55goODURSF/fv3s2PHDqBkDiU+Pp7hw4cTEhJCcnIygYGB5Obmouu62Dhs6dKl9O/fn4MHD3Lq1ClRst/lclFUVCSqLRsMBrKyskQP5/z9XIqKivDz87uw8eVdijysd7nOkyRJulpVeHCZP3++qBtWp04dfHx8iIuL48yZMzz33HNASa2xGTNmMGbMGKBkCXLv3r1Zs2YNLVq0oFWrVvz0009idZnJZBIrw7p3787p06eBkkTJsLAwMYTmcrkwGAzExMSQnJzMpEmTxBCZv7+/mNtZtWoVgwYNuqDtMkhIkiRVjAqfcxkyZAh+fn5ERkZSs2ZNgoODWb58Offeey/+/v5ASan85cuXo+s6JpOJevXqsXXrVrKzs2nYsCF9+/bF19cXq9VKs2bNePXVV3G5XLjdbiZPniyG22JjY4mMjMRut4seimfiPjg4mEOHDmEwGGjbti3NmzcnPDwcQBS8lCRJki6Py9JzKSwsFCVcHA4Ho0aN4tNPP2X//v2YzWaee+45HnroIQYNGkRBQQGffPIJjz76KPXq1cPtdjNq1CgeeOABNm/ejN1uZ8mSJei6Tt26dUlMTCQtLY3AwEB0XScxMZGQkBC++uorOnTogMvlIioqim7dujF//nzMZjPnzp2jdu3aGAwGjEYjPXr08Nr2ffv2VfTjuCrJ53Rx5HO6OPI5XZx/23NS9PMTSSqIpy6Yr68vAL1796ZRo0Zs3boVi8WCxWKhevXqvPrqq7z44oscP34cHx8f1qxZw759+3jhhRcwGo3UqVOH4OBgRo4cybx580hJSaFVq1YEBQXxzTffiJ0sv/nmG2rUqEHfvn1JSkqiSZMmdOjQge+//x5/f38KCgrIzs4mNTWVIUOGMHz48Iq+ZUmSJOk8lyW4LFu2jLi4OCZPnkxaWhqPPvoo69atw2QyMW/ePMLDw3nooYfQdZ0nn3ySm2++maefflqcP23aNL755hvq169PXFwcMTExzJkzhyFDhpCZmUnz5s1xuVzcdtttOJ1Otm7diqIoREdH06dPH8LDw5k5cyZNmjShT58+DB48GJvNxh133MHMmTMxGo0VfcuSJEnSeS5LbbHevXszYcIEHnroIRRFYcaMGaIczPl++OEHdu/ejdPp5OeffwZg5MiR9O/fX8yv7N+/n9dff53x48dTo0YNFi5cyIEDBxg5cmSpPVxGjhxJWFgY48aNA6B69eoMGzYMh8NBeno6zZs3JyUlhccee4x7772Xhx9++HLcuiRJksRl6rn8W2iaxuTJkzl+/DgWi4Vp06ZRp06dqm7WFeXAgQO88cYbLFmyhISEBMaPHy9yjCZNmiRyiq5lLpeLF198kXPnzuF0Ohk6dCgNGzaUz+pPVFXl5Zdf5vTp0xiNRmbOnImu6/I5lSErK4uePXuyaNEiTCbTv+45Xdmtu8x++OEHnE4nK1asYNSoUbz66qtV3aQryoIFC3j55ZfFkvCZM2cyYsQIli1bhq7rbNq0qYpbeGX46quvCAkJYdmyZSxYsICpU6fKZ+XFjz+WbPn92Wef8fzzzzNz5kz5nMrgcrl45ZVXxLz1v/E5XdPBZd++faKcTJs2bTh06FAVt+jKEhMTw7x588TPhw8f5qabbgKgY8eObN++vaqadkXp2rUr//nPf8TPRqNRPisvOnXqxNSpU4GSXWfDw8PlcyrDa6+9Rv/+/alevTrw7/xv75oOLjabjYCAAPGz0WgUSZcSdOnSpdRc2fllczyr8KSSZxEQEIDNZuP5559nxIgR8lmVwWQyMW7cOKZOnUqXLl3kc/Ji9erVhIWFiT984d/53941HVwCAgJKlYXRNM3rwgOpxPljvHa7naCgoCpszZUlJSWFQYMG8eCDD3L//ffLZ/UXXnvtNb799lsmTpwohlxBPiePL774gu3btzNw4ECOHj3KuHHjyM7OFr//tzynazq4XH/99WJ/mN9++43GjRtXcYuubM2bN2fXrl0AbN26lRtuuKGKW3RlyMzM5IknnmDMmDH07l1SQkg+qwt9+eWXvP/++wBYrVYURaFFixbyOf3Jp59+ytKlS1myZAnNmjXjtddeo2PHjv+65yRXi02eTFxcHLquM2PGDBo0aFDVzbqiJCUlMXLkSFauXMnp06eZOHEiLpeL+vXrM23aNJkzROm8LI+XXnqJadOmyWd1nsLCQiZMmEBmZiZut5unnnqKBg0ayH+n/sLAgQOZPHkyBoPhX/ecrungIkmSJF0e1/SwmCRJknR5yOAiSZIkVTgZXCRJkqQKJ4OLJEmSVOFkcJEkSZIqnAwuknQVyM3N5euvv67qZkiSIIOLJF0Fjh8/zubNm6u6GZIkyFonklTJHA4HEyZMIDk5WZTrX7FiBWfPnkVVVR5//HG6desmEugaNGjA8uXLyczMpEePHowaNYqoqCjOnj1Ly5YtmTJlCvPnz+fYsWOsWLGCfv36VfUtSpIMLpJU2T777DNq1arFW2+9RVxcHD/88AOhoaHMmjULm81Gz549adeuXZnnnzlzhoULF2K1WunUqRMZGRkMGTKEzz77TAYW6Yohh8UkqZKdOnWKNm3aANC4cWMyMjK48cYbgZJiqg0aNODs2bOlzjm/kEZMTAwBAQEYjUYiIiJKFX+UpCuFDC6SVMkaNGjAwYMHATh79izr169n7969QMk2EHFxcURHR2OxWMjIyADgyJEj4nxP6fXzGQwGNE2rhNZL0sWRwUWSKln//v1JSkpiwIABjB07lg8//JDc3FweeughBg0axPDhw6lWrRqDBg3iv//9L4MHD0ZV1b+8ZkxMDHFxcXz00UeVcxOS9Ddk4UpJkiSpwsmeiyRJklThZHCRJEmSKpwMLpIkSVKFk8FFkiRJqnAyuEiSJEkVTgYXSZIkqcLJ4CJJkiRVOBlcJEmSpAr3/wBV6NYfSBeArgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y= \"Fare\", data = train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train.Fare.unique()) #There are 248 unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_con[\"Fare\"] = train[\"Fare\"] \n",
    "\n",
    "df_bin[\"Fare\"] = pd.cut(train[\"Fare\"], bins=5) #discretised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(-0.512, 102.466]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(-0.512, 102.466]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-0.512, 102.466]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(-0.512, 102.466]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-0.512, 102.466]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch               Fare\n",
       "0         0       3    0  22.0      1      0  (-0.512, 102.466]\n",
       "1         1       1    1  38.0      1      0  (-0.512, 102.466]\n",
       "2         1       3    1  26.0      0      0  (-0.512, 102.466]\n",
       "3         1       1    1  35.0      1      0  (-0.512, 102.466]\n",
       "4         0       3    0  35.0      0      0  (-0.512, 102.466]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>(16.336, 24.294]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>(32.252, 40.21]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>(24.294, 32.252]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>(32.252, 40.21]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>(32.252, 40.21]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex               Age  SibSp  Parch     Fare\n",
       "0         0       3    male  (16.336, 24.294]      1      0   7.2500\n",
       "1         1       1  female   (32.252, 40.21]      1      0  71.2833\n",
       "2         1       3  female  (24.294, 32.252]      0      0   7.9250\n",
       "3         1       1  female   (32.252, 40.21]      1      0  53.1000\n",
       "4         0       3    male   (32.252, 40.21]      0      0   8.0500"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_con.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.512, 102.466]     838\n",
       "(102.466, 204.932]     33\n",
       "(204.932, 307.398]     17\n",
       "(409.863, 512.329]      3\n",
       "(307.398, 409.863]      0\n",
       "Name: Fare, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bin.Fare.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_count_dist(data= train, bin_df = df_bin,label_column = \"Survived\", target_column = \"Fare\", figsize =(20,10),use_bin_df= True )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature : Cabin\n",
    "\n",
    "Description : The cabin number where the passenger was staying "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "AGE              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will not be using Cabin feature as it has so many missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature : Embarked\n",
    "\n",
    "Description: The port where the passenger boarded the Titanic \n",
    "\n",
    "C: Cherbourg Q = Queenstown S = Southampton "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "AGE              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Embarked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='Embarked'>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y= \"Embarked\", data =train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding Embarked feature to sub dataframes\n",
    "\n",
    "df_bin[\"Embarked\"] = train[\"Embarked\"]  \n",
    "df_con[\"Embarked\"] = train[\"Embarked\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "891\n",
      "889\n"
     ]
    }
   ],
   "source": [
    "#Removing rows from Embarked feature having missing values\n",
    "print(len(df_con))\n",
    "df_con = df_con.dropna(subset=[\"Embarked\"])\n",
    "df_bin= df_bin.dropna(subset=[\"Embarked\"])\n",
    "print(len(df_con))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(-0.512, 102.466]</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(-0.512, 102.466]</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-0.512, 102.466]</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(-0.512, 102.466]</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-0.512, 102.466]</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch               Fare Embarked\n",
       "0         0       3    0  22.0      1      0  (-0.512, 102.466]        S\n",
       "1         1       1    1  38.0      1      0  (-0.512, 102.466]        C\n",
       "2         1       3    1  26.0      0      0  (-0.512, 102.466]        S\n",
       "3         1       1    1  35.0      1      0  (-0.512, 102.466]        S\n",
       "4         0       3    0  35.0      0      0  (-0.512, 102.466]        S"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>(16.336, 24.294]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>(32.252, 40.21]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>(24.294, 32.252]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>(32.252, 40.21]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>(32.252, 40.21]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex               Age  SibSp  Parch     Fare Embarked\n",
       "0         0       3    male  (16.336, 24.294]      1      0   7.2500        S\n",
       "1         1       1  female   (32.252, 40.21]      1      0  71.2833        C\n",
       "2         1       3  female  (24.294, 32.252]      0      0   7.9250        S\n",
       "3         1       1  female   (32.252, 40.21]      1      0  53.1000        S\n",
       "4         0       3    male   (32.252, 40.21]      0      0   8.0500        S"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_con.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>AGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>35.0</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  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    male      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female      1      0   \n",
       "2                             Heikkinen, Miss. Laina  female      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female      1      0   \n",
       "4                           Allen, Mr. William Henry    male      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked   AGE  \n",
       "0         A/5 21171   7.2500   NaN        S  22.0  \n",
       "1          PC 17599  71.2833   C85        C  38.0  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S  26.0  \n",
       "3            113803  53.1000  C123        S  35.0  \n",
       "4            373450   8.0500   NaN        S  35.0  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Encoding\n",
    "\n",
    "Now we have our two sub dataframes ready. We can encode the features so they're ready to be used with our machine learning models.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Sex_0</th>\n",
       "      <th>Sex_1</th>\n",
       "      <th>Age_0.42</th>\n",
       "      <th>Age_0.67</th>\n",
       "      <th>Age_0.75</th>\n",
       "      <th>Age_0.83</th>\n",
       "      <th>...</th>\n",
       "      <th>Parch_5</th>\n",
       "      <th>Parch_6</th>\n",
       "      <th>Fare_(-0.512, 102.466]</th>\n",
       "      <th>Fare_(102.466, 204.932]</th>\n",
       "      <th>Fare_(204.932, 307.398]</th>\n",
       "      <th>Fare_(307.398, 409.863]</th>\n",
       "      <th>Fare_(409.863, 512.329]</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
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       "</table>\n",
       "<p>5 rows × 116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass_1  Pclass_2  Pclass_3  Sex_0  Sex_1  Age_0.42  Age_0.67  \\\n",
       "0         0         0         0         1      1      0         0         0   \n",
       "1         1         1         0         0      0      1         0         0   \n",
       "2         1         0         0         1      0      1         0         0   \n",
       "3         1         1         0         0      0      1         0         0   \n",
       "4         0         0         0         1      1      0         0         0   \n",
       "\n",
       "   Age_0.75  Age_0.83  ...  Parch_5  Parch_6  Fare_(-0.512, 102.466]  \\\n",
       "0         0         0  ...        0        0                       1   \n",
       "1         0         0  ...        0        0                       1   \n",
       "2         0         0  ...        0        0                       1   \n",
       "3         0         0  ...        0        0                       1   \n",
       "4         0         0  ...        0        0                       1   \n",
       "\n",
       "   Fare_(102.466, 204.932]  Fare_(204.932, 307.398]  Fare_(307.398, 409.863]  \\\n",
       "0                        0                        0                        0   \n",
       "1                        0                        0                        0   \n",
       "2                        0                        0                        0   \n",
       "3                        0                        0                        0   \n",
       "4                        0                        0                        0   \n",
       "\n",
       "   Fare_(409.863, 512.329]  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0                        0           0           0           1  \n",
       "1                        0           1           0           0  \n",
       "2                        0           0           0           1  \n",
       "3                        0           0           0           1  \n",
       "4                        0           0           0           1  \n",
       "\n",
       "[5 rows x 116 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One-hot encode binned variables\n",
    "one_hot_cols = df_bin.columns.tolist()\n",
    "one_hot_cols.remove('Survived')\n",
    "df_bin_enc = pd.get_dummies(df_bin, columns=one_hot_cols)\n",
    "\n",
    "df_bin_enc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# One hot encode the categorical columns\n",
    "df_embarked_one_hot = pd.get_dummies(df_con['Embarked'], \n",
    "                                     prefix='embarked')\n",
    "\n",
    "df_sex_one_hot = pd.get_dummies(df_con['Sex'], \n",
    "                                prefix='sex')\n",
    "\n",
    "df_plcass_one_hot = pd.get_dummies(df_con['Pclass'], \n",
    "                                   prefix='pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine the one hot encoded columns with df_con_enc\n",
    "df_con_enc = pd.concat([df_con, \n",
    "                        df_embarked_one_hot, \n",
    "                        df_sex_one_hot, \n",
    "                        df_plcass_one_hot], axis=1)\n",
    "\n",
    "# Drop the original categorical columns (because now they've been one hot encoded)\n",
    "df_con_enc = df_con_enc.drop(['Pclass', 'Sex', 'Embarked'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>(48.168, 56.126]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>(24.294, 32.252]</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>(8.378, 16.336]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>(0.34, 8.378]</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>(56.126, 64.084]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>(16.336, 24.294]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>(32.252, 40.21]</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>(8.378, 16.336]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>(48.168, 56.126]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>(0.34, 8.378]</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>(24.294, 32.252]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>(24.294, 32.252]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>(24.294, 32.252]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Survived               Age  SibSp  Parch     Fare  embarked_C  embarked_Q  \\\n",
       "0          0  (16.336, 24.294]      1      0   7.2500           0           0   \n",
       "1          1   (32.252, 40.21]      1      0  71.2833           1           0   \n",
       "2          1  (24.294, 32.252]      0      0   7.9250           0           0   \n",
       "3          1   (32.252, 40.21]      1      0  53.1000           0           0   \n",
       "4          0   (32.252, 40.21]      0      0   8.0500           0           0   \n",
       "5          0  (24.294, 32.252]      0      0   8.4583           0           1   \n",
       "6          0  (48.168, 56.126]      0      0  51.8625           0           0   \n",
       "7          0     (0.34, 8.378]      3      1  21.0750           0           0   \n",
       "8          1  (24.294, 32.252]      0      2  11.1333           0           0   \n",
       "9          1   (8.378, 16.336]      1      0  30.0708           1           0   \n",
       "10         1     (0.34, 8.378]      1      1  16.7000           0           0   \n",
       "11         1  (56.126, 64.084]      0      0  26.5500           0           0   \n",
       "12         0  (16.336, 24.294]      0      0   8.0500           0           0   \n",
       "13         0   (32.252, 40.21]      1      5  31.2750           0           0   \n",
       "14         0   (8.378, 16.336]      0      0   7.8542           0           0   \n",
       "15         1  (48.168, 56.126]      0      0  16.0000           0           0   \n",
       "16         0     (0.34, 8.378]      4      1  29.1250           0           1   \n",
       "17         1  (24.294, 32.252]      0      0  13.0000           0           0   \n",
       "18         0  (24.294, 32.252]      1      0  18.0000           0           0   \n",
       "19         1  (24.294, 32.252]      0      0   7.2250           1           0   \n",
       "\n",
       "    embarked_S  sex_female  sex_male  pclass_1  pclass_2  pclass_3  \n",
       "0            1           0         1         0         0         1  \n",
       "1            0           1         0         1         0         0  \n",
       "2            1           1         0         0         0         1  \n",
       "3            1           1         0         1         0         0  \n",
       "4            1           0         1         0         0         1  \n",
       "5            0           0         1         0         0         1  \n",
       "6            1           0         1         1         0         0  \n",
       "7            1           0         1         0         0         1  \n",
       "8            1           1         0         0         0         1  \n",
       "9            0           1         0         0         1         0  \n",
       "10           1           1         0         0         0         1  \n",
       "11           1           1         0         1         0         0  \n",
       "12           1           0         1         0         0         1  \n",
       "13           1           0         1         0         0         1  \n",
       "14           1           1         0         0         0         1  \n",
       "15           1           1         0         0         1         0  \n",
       "16           0           0         1         0         0         1  \n",
       "17           1           0         1         0         1         0  \n",
       "18           1           1         0         0         0         1  \n",
       "19           0           1         0         0         0         1  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at df_con_enc\n",
    "df_con_enc.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building ML models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separating the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Select the dataframe we want to use\n",
    "\n",
    "selected_df = df_con_enc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_con_enc = df_con_enc.drop(\"Age\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_con_enc[\"Age\"] = train[\"AGE\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>Survived</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>pclass_1</th>\n",
       "      <th>pclass_2</th>\n",
       "      <th>pclass_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>889 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  SibSp  Parch     Fare  embarked_C  embarked_Q  embarked_S  \\\n",
       "0           0      1      0   7.2500           0           0           1   \n",
       "1           1      1      0  71.2833           1           0           0   \n",
       "2           1      0      0   7.9250           0           0           1   \n",
       "3           1      1      0  53.1000           0           0           1   \n",
       "4           0      0      0   8.0500           0           0           1   \n",
       "..        ...    ...    ...      ...         ...         ...         ...   \n",
       "886         0      0      0  13.0000           0           0           1   \n",
       "887         1      0      0  30.0000           0           0           1   \n",
       "888         0      1      2  23.4500           0           0           1   \n",
       "889         1      0      0  30.0000           1           0           0   \n",
       "890         0      0      0   7.7500           0           1           0   \n",
       "\n",
       "     sex_female  sex_male  pclass_1  pclass_2  pclass_3  \n",
       "0             0         1         0         0         1  \n",
       "1             1         0         1         0         0  \n",
       "2             1         0         0         0         1  \n",
       "3             1         0         1         0         0  \n",
       "4             0         1         0         0         1  \n",
       "..          ...       ...       ...       ...       ...  \n",
       "886           0         1         0         1         0  \n",
       "887           1         0         1         0         0  \n",
       "888           1         0         0         0         1  \n",
       "889           0         1         1         0         0  \n",
       "890           0         1         0         0         1  \n",
       "\n",
       "[889 rows x 12 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_df.drop(\"Age\", axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_df[\"Age\"] =  df_con_enc[\"Age\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = selected_df.drop(\"Survived\" , axis =1)\n",
    "\n",
    "y_train = selected_df[\"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(889, 12)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(889,)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_ml_algo(algo, X_train, y_train, cv):\n",
    "    \n",
    "    model = algo.fit(X_train, y_train)\n",
    "    acc = round(model.score(X_train,y_train) *100,2)\n",
    "    \n",
    "    train_pred = model_selection.cross_val_predict(algo, X_train, y_train, cv=cv, n_jobs = -1)\n",
    "    \n",
    "    acc_cv = round(metrics.accuracy_score(y_train, train_pred)*100,2)\n",
    "    \n",
    "    return train_pred, acc ,acc_cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 80.54\n",
      "Accuracy CV 10-Fold: 79.75\n",
      "Running Time: 0:00:03.055220\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "start_time = time.time()\n",
    "train_pred_log, acc_log, acc_cv_log = fit_ml_algo(LogisticRegression(), \n",
    "                                                               X_train, \n",
    "                                                               y_train, \n",
    "                                                                    10)\n",
    "log_time = (time.time() - start_time)\n",
    "print(\"Accuracy: %s\" % acc_log)\n",
    "print(\"Accuracy CV 10-Fold: %s\" % acc_cv_log)\n",
    "print(\"Running Time: %s\" % datetime.timedelta(seconds=log_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 82.45\n",
      "Accuracy CV 10-Fold: 71.54\n",
      "Running Time: 0:00:00.191516\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# k-Nearest Neighbours\n",
    "start_time = time.time()\n",
    "train_pred_knn, acc_knn, acc_cv_knn = fit_ml_algo(KNeighborsClassifier(), \n",
    "                                                  X_train, \n",
    "                                                  y_train, \n",
    "                                                  10)\n",
    "knn_time = (time.time() - start_time)\n",
    "print(\"Accuracy: %s\" % acc_knn)\n",
    "print(\"Accuracy CV 10-Fold: %s\" % acc_cv_knn)\n",
    "print(\"Running Time: %s\" % datetime.timedelta(seconds=knn_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 78.52\n",
      "Accuracy CV 10-Fold: 78.29\n",
      "Running Time: 0:00:00.039893\n"
     ]
    }
   ],
   "source": [
    "# Gaussian Naive Bayes\n",
    "start_time = time.time()\n",
    "train_pred_gaussian, acc_gaussian, acc_cv_gaussian = fit_ml_algo(GaussianNB(), \n",
    "                                                                      X_train, \n",
    "                                                                      y_train, \n",
    "                                                                           10)\n",
    "gaussian_time = (time.time() - start_time)\n",
    "print(\"Accuracy: %s\" % acc_gaussian)\n",
    "print(\"Accuracy CV 10-Fold: %s\" % acc_cv_gaussian)\n",
    "print(\"Running Time: %s\" % datetime.timedelta(seconds=gaussian_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 68.84\n",
      "Accuracy CV 10-Fold: 73.12\n",
      "Running Time: 0:00:00.210436\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Linear SVC\n",
    "start_time = time.time()\n",
    "train_pred_svc, acc_linear_svc, acc_cv_linear_svc = fit_ml_algo(LinearSVC(),\n",
    "                                                                X_train, \n",
    "                                                                y_train, \n",
    "                                                                10)\n",
    "linear_svc_time = (time.time() - start_time)\n",
    "print(\"Accuracy: %s\" % acc_linear_svc)\n",
    "print(\"Accuracy CV 10-Fold: %s\" % acc_cv_linear_svc)\n",
    "print(\"Running Time: %s\" % datetime.timedelta(seconds=linear_svc_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 78.63\n",
      "Accuracy CV 10-Fold: 72.33\n",
      "Running Time: 0:00:00.781418\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Stochastic Gradient Descent\n",
    "start_time = time.time()\n",
    "train_pred_sgd, acc_sgd, acc_cv_sgd = fit_ml_algo(SGDClassifier(), \n",
    "                                                  X_train, \n",
    "                                                  y_train,\n",
    "                                                  10)\n",
    "sgd_time = (time.time() - start_time)\n",
    "print(\"Accuracy: %s\" % acc_sgd)\n",
    "print(\"Accuracy CV 10-Fold: %s\" % acc_cv_sgd)\n",
    "print(\"Running Time: %s\" % datetime.timedelta(seconds=sgd_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 97.98\n",
      "Accuracy CV 10-Fold: 78.97\n",
      "Running Time: 0:00:00.143158\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Decision Tree Classifier\n",
    "start_time = time.time()\n",
    "train_pred_dt, acc_dt, acc_cv_dt = fit_ml_algo(DecisionTreeClassifier(), \n",
    "                                                                X_train, \n",
    "                                                                y_train,\n",
    "                                                                10)\n",
    "dt_time = (time.time() - start_time)\n",
    "print(\"Accuracy: %s\" % acc_dt)\n",
    "print(\"Accuracy CV 10-Fold: %s\" % acc_cv_dt)\n",
    "print(\"Running Time: %s\" % datetime.timedelta(seconds=dt_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 89.43\n",
      "Accuracy CV 10-Fold: 82.79\n",
      "Running Time: 0:00:00.365023\n"
     ]
    }
   ],
   "source": [
    "# Gradient Boosting Trees\n",
    "start_time = time.time()\n",
    "train_pred_gbt, acc_gbt, acc_cv_gbt = fit_ml_algo(GradientBoostingClassifier(), \n",
    "                                                                       X_train, \n",
    "                                                                       y_train,\n",
    "                                                                       10)\n",
    "gbt_time = (time.time() - start_time)\n",
    "print(\"Accuracy: %s\" % acc_gbt)\n",
    "print(\"Accuracy CV 10-Fold: %s\" % acc_cv_gbt)\n",
    "print(\"Running Time: %s\" % datetime.timedelta(seconds=gbt_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Catboost Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>pclass_1</th>\n",
       "      <th>pclass_2</th>\n",
       "      <th>pclass_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  SibSp  Parch     Fare  embarked_C  embarked_Q  embarked_S  \\\n",
       "0  22.0      1      0   7.2500           0           0           1   \n",
       "1  38.0      1      0  71.2833           1           0           0   \n",
       "2  26.0      0      0   7.9250           0           0           1   \n",
       "3  35.0      1      0  53.1000           0           0           1   \n",
       "4  35.0      0      0   8.0500           0           0           1   \n",
       "\n",
       "   sex_female  sex_male  pclass_1  pclass_2  pclass_3  \n",
       "0           0         1         0         0         1  \n",
       "1           1         0         1         0         0  \n",
       "2           1         0         0         0         1  \n",
       "3           1         0         1         0         0  \n",
       "4           0         1         0         0         1  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>pclass_1</th>\n",
       "      <th>pclass_2</th>\n",
       "      <th>pclass_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  SibSp  Parch     Fare  embarked_C  embarked_Q  embarked_S  \\\n",
       "0  22.0      1      0   7.2500           0           0           1   \n",
       "1  38.0      1      0  71.2833           1           0           0   \n",
       "2  26.0      0      0   7.9250           0           0           1   \n",
       "3  35.0      1      0  53.1000           0           0           1   \n",
       "4  35.0      0      0   8.0500           0           0           1   \n",
       "\n",
       "   sex_female  sex_male  pclass_1  pclass_2  pclass_3  \n",
       "0           0         1         0         0         1  \n",
       "1           1         0         1         0         0  \n",
       "2           1         0         0         0         1  \n",
       "3           1         0         1         0         0  \n",
       "4           0         1         0         0         1  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train[\"Age\"] = X_train[\"Age\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  4,  5,  6,  7,  8,  9, 10, 11], dtype=int64)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the categorical features for the CatBoost model\n",
    "cat_features = np.where(X_train.dtypes != np.float)[0]\n",
    "cat_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the CatBoost Pool() function to pool together the training data and categorical feature labels\n",
    "train_pool = Pool(X_train, \n",
    "                  y_train,\n",
    "                  cat_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7032c2f235364053b8ef4e82625f4e9a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.009797\n",
      "0:\tlearn: 0.6865149\ttotal: 68.9ms\tremaining: 1m 8s\n",
      "1:\tlearn: 0.6808094\ttotal: 84.3ms\tremaining: 42.1s\n",
      "2:\tlearn: 0.6755095\ttotal: 101ms\tremaining: 33.6s\n",
      "3:\tlearn: 0.6720507\ttotal: 106ms\tremaining: 26.4s\n",
      "4:\tlearn: 0.6667084\ttotal: 119ms\tremaining: 23.7s\n",
      "5:\tlearn: 0.6623282\ttotal: 137ms\tremaining: 22.7s\n",
      "6:\tlearn: 0.6574390\ttotal: 153ms\tremaining: 21.8s\n",
      "7:\tlearn: 0.6540734\ttotal: 164ms\tremaining: 20.4s\n",
      "8:\tlearn: 0.6493973\ttotal: 173ms\tremaining: 19.1s\n",
      "9:\tlearn: 0.6443956\ttotal: 195ms\tremaining: 19.3s\n",
      "10:\tlearn: 0.6397858\ttotal: 218ms\tremaining: 19.6s\n",
      "11:\tlearn: 0.6350111\ttotal: 232ms\tremaining: 19.1s\n",
      "12:\tlearn: 0.6298100\ttotal: 247ms\tremaining: 18.8s\n",
      "13:\tlearn: 0.6247848\ttotal: 260ms\tremaining: 18.3s\n",
      "14:\tlearn: 0.6197236\ttotal: 272ms\tremaining: 17.9s\n",
      "15:\tlearn: 0.6160961\ttotal: 284ms\tremaining: 17.5s\n",
      "16:\tlearn: 0.6115428\ttotal: 297ms\tremaining: 17.2s\n",
      "17:\tlearn: 0.6076905\ttotal: 306ms\tremaining: 16.7s\n",
      "18:\tlearn: 0.6047138\ttotal: 319ms\tremaining: 16.5s\n",
      "19:\tlearn: 0.6012084\ttotal: 332ms\tremaining: 16.3s\n",
      "20:\tlearn: 0.5971689\ttotal: 344ms\tremaining: 16s\n",
      "21:\tlearn: 0.5937087\ttotal: 352ms\tremaining: 15.6s\n",
      "22:\tlearn: 0.5896614\ttotal: 371ms\tremaining: 15.7s\n",
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      "826:\tlearn: 0.3154687\ttotal: 12.6s\tremaining: 2.63s\n",
      "Collecting package metadata (current_repodata.json): ...working... done\n",
      "Solving environment: ...working... done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: E:\\Anaconda3\n",
      "\n",
      "  added / updated specs:\n",
      "    - catboost\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    _anaconda_depends-2020.07  |           py38_0           6 KB\n",
      "    anaconda-custom            |           py38_1          36 KB\n",
      "    ca-certificates-2021.5.30  |       h5b45459_0         171 KB  conda-forge\n",
      "    catboost-0.26.1            |   py38haa244fe_0        52.5 MB  conda-forge\n",
      "    certifi-2021.5.30          |   py38haa244fe_0         142 KB  conda-forge\n",
      "    conda-4.10.3               |   py38haa244fe_0         3.1 MB  conda-forge\n",
      "    gmpy2-2.1.0b5              |   py38hb63f22f_0         190 KB  conda-forge\n",
      "    libllvm9-9.0.1             |       hab3b255_3          48 KB  conda-forge\n",
      "    mpc-1.1.0                  |    h7edee0f_1009         322 KB  conda-forge\n",
      "    mpfr-4.1.0                 |       h8d14728_0         502 KB  conda-forge\n",
      "    mpir-3.0.0                 |    he025d50_1002         3.0 MB  conda-forge\n",
      "    openssl-1.1.1k             |       h8ffe710_1         5.8 MB  conda-forge\n",
      "    pathtools-0.1.2            |             py_1           8 KB  conda-forge\n",
      "    pytest-6.2.4               |   py38haa244fe_0         464 KB  conda-forge\n",
      "    python_abi-3.8             |           2_cp38           4 KB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:        66.1 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "  _anaconda_depends  pkgs/main/win-64::_anaconda_depends-2020.07-py38_0\n",
      "  catboost           conda-forge/win-64::catboost-0.26.1-py38haa244fe_0\n",
      "  gmpy2              conda-forge/win-64::gmpy2-2.1.0b5-py38hb63f22f_0\n",
      "  libllvm9           conda-forge/win-64::libllvm9-9.0.1-hab3b255_3\n",
      "  mpc                conda-forge/win-64::mpc-1.1.0-h7edee0f_1009\n",
      "  mpfr               conda-forge/win-64::mpfr-4.1.0-h8d14728_0\n",
      "  mpir               conda-forge/win-64::mpir-3.0.0-he025d50_1002\n",
      "  pathtools          conda-forge/noarch::pathtools-0.1.2-py_1\n",
      "  pytest             conda-forge/win-64::pytest-6.2.4-py38haa244fe_0\n",
      "  python_abi         conda-forge/win-64::python_abi-3.8-2_cp38\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "  ca-certificates    pkgs/main::ca-certificates-2021.4.13-~ --> conda-forge::ca-certificates-2021.5.30-h5b45459_0\n",
      "  certifi            pkgs/main::certifi-2020.12.5-py38haa9~ --> conda-forge::certifi-2021.5.30-py38haa244fe_0\n",
      "  conda              pkgs/main::conda-4.10.1-py38haa95532_1 --> conda-forge::conda-4.10.3-py38haa244fe_0\n",
      "  openssl              pkgs/main::openssl-1.1.1k-h2bbff1b_0 --> conda-forge::openssl-1.1.1k-h8ffe710_1\n",
      "\n",
      "The following packages will be DOWNGRADED:\n",
      "\n",
      "  anaconda                                   2021.05-py38_0 --> custom-py38_1\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "\n",
      "ca-certificates-2021 | 171 KB    |            |   0% \n",
      "ca-certificates-2021 | 171 KB    | 9          |   9% \n",
      "ca-certificates-2021 | 171 KB    | ########## | 100% \n",
      "ca-certificates-2021 | 171 KB    | ########## | 100% \n",
      "\n",
      "_anaconda_depends-20 | 6 KB      |            |   0% \n",
      "_anaconda_depends-20 | 6 KB      | ########## | 100% \n",
      "_anaconda_depends-20 | 6 KB      | ########## | 100% \n",
      "\n",
      "gmpy2-2.1.0b5        | 190 KB    |            |   0% \n",
      "gmpy2-2.1.0b5        | 190 KB    | ########## | 100% \n",
      "gmpy2-2.1.0b5        | 190 KB    | ########## | 100% \n",
      "\n",
      "libllvm9-9.0.1       | 48 KB     |            |   0% \n",
      "libllvm9-9.0.1       | 48 KB     | ########## | 100% \n",
      "\n",
      "openssl-1.1.1k       | 5.8 MB    |            |   0% \n",
      "openssl-1.1.1k       | 5.8 MB    | #3         |  14% \n",
      "openssl-1.1.1k       | 5.8 MB    | #######    |  70% \n",
      "openssl-1.1.1k       | 5.8 MB    | #########2 |  93% \n",
      "openssl-1.1.1k       | 5.8 MB    | ########## | 100% \n",
      "\n",
      "pathtools-0.1.2      | 8 KB      |            |   0% \n",
      "pathtools-0.1.2      | 8 KB      | ########## | 100% \n",
      "\n",
      "python_abi-3.8       | 4 KB      |            |   0% \n",
      "python_abi-3.8       | 4 KB      | ########## | 100% \n",
      "\n",
      "conda-4.10.3         | 3.1 MB    |            |   0% \n",
      "conda-4.10.3         | 3.1 MB    | ###6       |  36% \n",
      "conda-4.10.3         | 3.1 MB    | ######1    |  61% \n",
      "conda-4.10.3         | 3.1 MB    | #######1   |  71% \n",
      "conda-4.10.3         | 3.1 MB    | #######9   |  79% \n",
      "conda-4.10.3         | 3.1 MB    | ########5  |  86% \n",
      "conda-4.10.3         | 3.1 MB    | #########2 |  92% \n",
      "conda-4.10.3         | 3.1 MB    | #########8 |  99% \n",
      "conda-4.10.3         | 3.1 MB    | ########## | 100% \n",
      "\n",
      "catboost-0.26.1      | 52.5 MB   |            |   0% \n",
      "catboost-0.26.1      | 52.5 MB   |            |   0% \n",
      "catboost-0.26.1      | 52.5 MB   |            |   1% \n",
      "catboost-0.26.1      | 52.5 MB   | 1          |   2% \n",
      "catboost-0.26.1      | 52.5 MB   | 3          |   4% \n",
      "catboost-0.26.1      | 52.5 MB   | 6          |   7% \n",
      "catboost-0.26.1      | 52.5 MB   | #1         |  11% \n",
      "catboost-0.26.1      | 52.5 MB   | #6         |  16% \n",
      "catboost-0.26.1      | 52.5 MB   | #9         |  19% \n",
      "catboost-0.26.1      | 52.5 MB   | ##2        |  23% \n",
      "catboost-0.26.1      | 52.5 MB   | ##5        |  26% \n",
      "catboost-0.26.1      | 52.5 MB   | ##9        |  29% \n",
      "catboost-0.26.1      | 52.5 MB   | ###2       |  33% \n",
      "catboost-0.26.1      | 52.5 MB   | ###5       |  35% \n",
      "catboost-0.26.1      | 52.5 MB   | ###7       |  38% \n",
      "catboost-0.26.1      | 52.5 MB   | ####       |  40% \n",
      "catboost-0.26.1      | 52.5 MB   | ####2      |  43% \n",
      "catboost-0.26.1      | 52.5 MB   | ####5      |  45% \n",
      "catboost-0.26.1      | 52.5 MB   | ####7      |  48% \n",
      "catboost-0.26.1      | 52.5 MB   | ####9      |  50% \n",
      "catboost-0.26.1      | 52.5 MB   | #####2     |  52% \n",
      "catboost-0.26.1      | 52.5 MB   | #####4     |  55% \n",
      "catboost-0.26.1      | 52.5 MB   | #####6     |  57% \n",
      "catboost-0.26.1      | 52.5 MB   | #####9     |  59% \n",
      "catboost-0.26.1      | 52.5 MB   | ######1    |  62% \n",
      "catboost-0.26.1      | 52.5 MB   | ######3    |  64% \n",
      "catboost-0.26.1      | 52.5 MB   | ######7    |  68% \n",
      "catboost-0.26.1      | 52.5 MB   | #######2   |  72% \n",
      "catboost-0.26.1      | 52.5 MB   | #######6   |  77% \n",
      "catboost-0.26.1      | 52.5 MB   | ########   |  80% \n",
      "catboost-0.26.1      | 52.5 MB   | ########3  |  84% \n",
      "catboost-0.26.1      | 52.5 MB   | ########7  |  87% \n",
      "catboost-0.26.1      | 52.5 MB   | #########  |  91% \n",
      "catboost-0.26.1      | 52.5 MB   | #########4 |  94% \n",
      "catboost-0.26.1      | 52.5 MB   | #########7 |  97% \n",
      "catboost-0.26.1      | 52.5 MB   | ########## | 100% \n",
      "\n",
      "mpir-3.0.0           | 3.0 MB    |            |   0% \n",
      "mpir-3.0.0           | 3.0 MB    | ###2       |  33% \n",
      "mpir-3.0.0           | 3.0 MB    | ########## | 100% \n",
      "mpir-3.0.0           | 3.0 MB    | ########## | 100% \n",
      "\n",
      "anaconda-custom      | 36 KB     |            |   0% \n",
      "anaconda-custom      | 36 KB     | ########## | 100% \n",
      "anaconda-custom      | 36 KB     | ########## | 100% \n",
      "\n",
      "certifi-2021.5.30    | 142 KB    |            |   0% \n",
      "certifi-2021.5.30    | 142 KB    | ########## | 100% \n",
      "\n",
      "pytest-6.2.4         | 464 KB    |            |   0% \n",
      "pytest-6.2.4         | 464 KB    | ########## | 100% \n",
      "pytest-6.2.4         | 464 KB    | ########## | 100% \n",
      "\n",
      "mpc-1.1.0            | 322 KB    |            |   0% \n",
      "mpc-1.1.0            | 322 KB    | ########## | 100% \n",
      "mpc-1.1.0            | 322 KB    | ########## | 100% \n",
      "\n",
      "mpfr-4.1.0           | 502 KB    |            |   0% \n",
      "mpfr-4.1.0           | 502 KB    | ########## | 100% \n",
      "mpfr-4.1.0           | 502 KB    | ########## | 100% \n",
      "Preparing transaction: ...working... done\n",
      "Verifying transaction: ...working... done\n",
      "Executing transaction: ...working... done\n",
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      "838:\tlearn: 0.3142422\ttotal: 12.7s\tremaining: 2.44s\n",
      "839:\tlearn: 0.3142234\ttotal: 12.7s\tremaining: 2.43s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "The environment is inconsistent, please check the package plan carefully\n",
      "The following packages are causing the inconsistency:\n",
      "\n",
      "  - defaults/win-64::anaconda==2021.05=py38_0\n",
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.10.1\n",
      "  latest version: 4.10.3\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "#Catboost model definition \n",
    "\n",
    "catboost_model = CatBoostClassifier(iterations = 1000, custom_loss= [\"Accuracy\"], loss_function = \"Logloss\")\n",
    "\n",
    "#Fit Catboost model \n",
    "\n",
    "catboost_model.fit(train_pool,plot = True)\n",
    "\n",
    "#Catboost accuracy\n",
    "\n",
    "acc_catboost = round(catboost_model.score(X_train,y_train)*100,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "73d6701ab1264b638720cac8501f2fdb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "1:\tlearn: 0.6641740\ttest: 0.6645792\tbest: 0.6645792 (1)\n",
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    {
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      "932:\tlearn: 0.1699547\ttest: 0.4396646\tbest: 0.4324854 (313)\n",
      "933:\tlearn: 0.1698069\ttest: 0.4396463\tbest: 0.4324854 (313)\n",
      "934:\tlearn: 0.1696554\ttest: 0.4395371\tbest: 0.4324854 (313)\n",
      "935:\tlearn: 0.1695397\ttest: 0.4394427\tbest: 0.4324854 (313)\n",
      "936:\tlearn: 0.1694044\ttest: 0.4393302\tbest: 0.4324854 (313)\n",
      "937:\tlearn: 0.1692748\ttest: 0.4393027\tbest: 0.4324854 (313)\n",
      "938:\tlearn: 0.1691494\ttest: 0.4392393\tbest: 0.4324854 (313)\n",
      "939:\tlearn: 0.1690113\ttest: 0.4392971\tbest: 0.4324854 (313)\n",
      "940:\tlearn: 0.1688708\ttest: 0.4393353\tbest: 0.4324854 (313)\n",
      "941:\tlearn: 0.1687367\ttest: 0.4392912\tbest: 0.4324854 (313)\n",
      "942:\tlearn: 0.1685979\ttest: 0.4392899\tbest: 0.4324854 (313)\n",
      "943:\tlearn: 0.1684757\ttest: 0.4391245\tbest: 0.4324854 (313)\n",
      "944:\tlearn: 0.1683649\ttest: 0.4392103\tbest: 0.4324854 (313)\n",
      "945:\tlearn: 0.1682526\ttest: 0.4392458\tbest: 0.4324854 (313)\n",
      "946:\tlearn: 0.1681169\ttest: 0.4392433\tbest: 0.4324854 (313)\n",
      "947:\tlearn: 0.1680206\ttest: 0.4394497\tbest: 0.4324854 (313)\n",
      "948:\tlearn: 0.1679352\ttest: 0.4393814\tbest: 0.4324854 (313)\n",
      "949:\tlearn: 0.1677997\ttest: 0.4393910\tbest: 0.4324854 (313)\ttotal: 2m 4s\tremaining: 6.56s\n",
      "950:\tlearn: 0.1676552\ttest: 0.4394640\tbest: 0.4324854 (313)\n",
      "951:\tlearn: 0.1675261\ttest: 0.4394289\tbest: 0.4324854 (313)\n",
      "952:\tlearn: 0.1673947\ttest: 0.4394190\tbest: 0.4324854 (313)\n",
      "953:\tlearn: 0.1672766\ttest: 0.4394387\tbest: 0.4324854 (313)\n",
      "954:\tlearn: 0.1671744\ttest: 0.4394637\tbest: 0.4324854 (313)\n",
      "955:\tlearn: 0.1670384\ttest: 0.4395662\tbest: 0.4324854 (313)\n",
      "956:\tlearn: 0.1668888\ttest: 0.4395834\tbest: 0.4324854 (313)\n",
      "957:\tlearn: 0.1667655\ttest: 0.4396918\tbest: 0.4324854 (313)\n",
      "958:\tlearn: 0.1666360\ttest: 0.4397784\tbest: 0.4324854 (313)\n",
      "959:\tlearn: 0.1664808\ttest: 0.4399088\tbest: 0.4324854 (313)\n",
      "960:\tlearn: 0.1663219\ttest: 0.4399159\tbest: 0.4324854 (313)\n",
      "961:\tlearn: 0.1661711\ttest: 0.4398065\tbest: 0.4324854 (313)\n",
      "962:\tlearn: 0.1660315\ttest: 0.4400172\tbest: 0.4324854 (313)\n",
      "963:\tlearn: 0.1659346\ttest: 0.4399701\tbest: 0.4324854 (313)\n",
      "964:\tlearn: 0.1657910\ttest: 0.4399750\tbest: 0.4324854 (313)\n",
      "965:\tlearn: 0.1657031\ttest: 0.4400323\tbest: 0.4324854 (313)\n",
      "966:\tlearn: 0.1656241\ttest: 0.4401495\tbest: 0.4324854 (313)\n",
      "967:\tlearn: 0.1655018\ttest: 0.4400759\tbest: 0.4324854 (313)\n",
      "968:\tlearn: 0.1654023\ttest: 0.4400479\tbest: 0.4324854 (313)\n",
      "969:\tlearn: 0.1652873\ttest: 0.4402244\tbest: 0.4324854 (313)\n",
      "970:\tlearn: 0.1651172\ttest: 0.4402324\tbest: 0.4324854 (313)\n",
      "971:\tlearn: 0.1649554\ttest: 0.4402387\tbest: 0.4324854 (313)\n",
      "972:\tlearn: 0.1647622\ttest: 0.4402428\tbest: 0.4324854 (313)\n",
      "973:\tlearn: 0.1646593\ttest: 0.4402917\tbest: 0.4324854 (313)\n",
      "974:\tlearn: 0.1645660\ttest: 0.4403502\tbest: 0.4324854 (313)\n",
      "975:\tlearn: 0.1644345\ttest: 0.4403234\tbest: 0.4324854 (313)\n",
      "976:\tlearn: 0.1642626\ttest: 0.4402571\tbest: 0.4324854 (313)\n",
      "977:\tlearn: 0.1641384\ttest: 0.4401168\tbest: 0.4324854 (313)\n",
      "978:\tlearn: 0.1640283\ttest: 0.4401722\tbest: 0.4324854 (313)\n",
      "979:\tlearn: 0.1638673\ttest: 0.4401709\tbest: 0.4324854 (313)\n",
      "980:\tlearn: 0.1637755\ttest: 0.4400154\tbest: 0.4324854 (313)\n",
      "981:\tlearn: 0.1636259\ttest: 0.4400689\tbest: 0.4324854 (313)\n",
      "982:\tlearn: 0.1635106\ttest: 0.4399464\tbest: 0.4324854 (313)\n",
      "983:\tlearn: 0.1633649\ttest: 0.4400207\tbest: 0.4324854 (313)\n",
      "984:\tlearn: 0.1632443\ttest: 0.4400469\tbest: 0.4324854 (313)\n",
      "985:\tlearn: 0.1631031\ttest: 0.4400217\tbest: 0.4324854 (313)\n",
      "986:\tlearn: 0.1629466\ttest: 0.4400443\tbest: 0.4324854 (313)\ttotal: 2m 9s\tremaining: 1.71s\n",
      "987:\tlearn: 0.1628180\ttest: 0.4400462\tbest: 0.4324854 (313)\n",
      "988:\tlearn: 0.1627238\ttest: 0.4400897\tbest: 0.4324854 (313)\n",
      "989:\tlearn: 0.1626193\ttest: 0.4400760\tbest: 0.4324854 (313)\n",
      "990:\tlearn: 0.1624566\ttest: 0.4401696\tbest: 0.4324854 (313)\n",
      "991:\tlearn: 0.1623628\ttest: 0.4401092\tbest: 0.4324854 (313)\n",
      "992:\tlearn: 0.1622515\ttest: 0.4401152\tbest: 0.4324854 (313)\n",
      "993:\tlearn: 0.1621435\ttest: 0.4401691\tbest: 0.4324854 (313)\n",
      "994:\tlearn: 0.1620170\ttest: 0.4400194\tbest: 0.4324854 (313)\n",
      "995:\tlearn: 0.1618642\ttest: 0.4401489\tbest: 0.4324854 (313)\n",
      "996:\tlearn: 0.1617751\ttest: 0.4402738\tbest: 0.4324854 (313)\n",
      "997:\tlearn: 0.1616577\ttest: 0.4402036\tbest: 0.4324854 (313)\n",
      "998:\tlearn: 0.1615517\ttest: 0.4401960\tbest: 0.4324854 (313)\n",
      "999:\tlearn: 0.1614014\ttest: 0.4404296\tbest: 0.4324854 (313)\ttotal: 2m 11s\tremaining: 0us\n"
     ]
    }
   ],
   "source": [
    "# How long will this take?\n",
    "start_time = time.time()\n",
    "\n",
    "# Set params for cross-validation as same as initial model\n",
    "cv_params = catboost_model.get_params()\n",
    "\n",
    "# Run the cross-validation for 10-folds (same as the other models)\n",
    "cv_data = cv(train_pool,\n",
    "             cv_params,\n",
    "             fold_count=10,\n",
    "             plot=True)\n",
    "\n",
    "# How long did it take?\n",
    "catboost_time = (time.time() - start_time)\n",
    "\n",
    "# CatBoost CV results save into a dataframe (cv_data), let's withdraw the maximum accuracy score\n",
    "acc_cv_catboost = round(np.max(cv_data['test-Accuracy-mean']) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---CatBoost Metrics---\n",
      "Accuracy: 85.49\n",
      "Accuracy cross-validation 10-Fold: 81.77\n",
      "Running Time: 0:02:12.874115\n"
     ]
    }
   ],
   "source": [
    "# Print out the CatBoost model metrics\n",
    "print(\"---CatBoost Metrics---\")\n",
    "print(\"Accuracy: {}\".format(acc_catboost))\n",
    "print(\"Accuracy cross-validation 10-Fold: {}\".format(acc_cv_catboost))\n",
    "print(\"Running Time: {}\".format(datetime.timedelta(seconds=catboost_time)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---Reuglar Accuracy Scores---\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>97.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gradient Boosting Trees</td>\n",
       "      <td>89.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CatBoost</td>\n",
       "      <td>85.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN</td>\n",
       "      <td>82.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>80.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stochastic Gradient Decent</td>\n",
       "      <td>78.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>78.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>68.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model  Score\n",
       "5               Decision Tree  97.98\n",
       "6     Gradient Boosting Trees  89.43\n",
       "7                    CatBoost  85.49\n",
       "0                         KNN  82.45\n",
       "1         Logistic Regression  80.54\n",
       "3  Stochastic Gradient Decent  78.63\n",
       "2                 Naive Bayes  78.52\n",
       "4                  Linear SVC  68.84"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['KNN', 'Logistic Regression', 'Naive Bayes', \n",
    "              'Stochastic Gradient Decent', 'Linear SVC', \n",
    "              'Decision Tree', 'Gradient Boosting Trees',\n",
    "              'CatBoost'],\n",
    "    'Score': [\n",
    "        acc_knn, \n",
    "        acc_log,  \n",
    "        acc_gaussian, \n",
    "        acc_sgd, \n",
    "        acc_linear_svc, \n",
    "        acc_dt,\n",
    "        acc_gbt,\n",
    "        acc_catboost\n",
    "    ]})\n",
    "print(\"---Reuglar Accuracy Scores---\")\n",
    "models.sort_values(by='Score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---Cross-validation Accuracy Scores---\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gradient Boosting Trees</td>\n",
       "      <td>82.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CatBoost</td>\n",
       "      <td>81.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>79.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>78.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>78.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>73.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stochastic Gradient Decent</td>\n",
       "      <td>72.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN</td>\n",
       "      <td>71.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model  Score\n",
       "6     Gradient Boosting Trees  82.79\n",
       "7                    CatBoost  81.77\n",
       "1         Logistic Regression  79.75\n",
       "5               Decision Tree  78.97\n",
       "2                 Naive Bayes  78.29\n",
       "4                  Linear SVC  73.12\n",
       "3  Stochastic Gradient Decent  72.33\n",
       "0                         KNN  71.54"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "cv_models = pd.DataFrame({\n",
    "    'Model': ['KNN', 'Logistic Regression', 'Naive Bayes', \n",
    "              'Stochastic Gradient Decent', 'Linear SVC', \n",
    "              'Decision Tree', 'Gradient Boosting Trees',\n",
    "              'CatBoost'],\n",
    "    'Score': [\n",
    "        acc_cv_knn, \n",
    "        acc_cv_log,      \n",
    "        acc_cv_gaussian, \n",
    "        acc_cv_sgd, \n",
    "        acc_cv_linear_svc, \n",
    "        acc_cv_dt,\n",
    "        acc_cv_gbt,\n",
    "        acc_cv_catboost\n",
    "    ]})\n",
    "print('---Cross-validation Accuracy Scores---')\n",
    "cv_models.sort_values(by='Score', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Importance\n",
    "def feature_importance(model, data):\n",
    "    \"\"\"\n",
    "    Function to show which features are most important in the model.\n",
    "    ::param_model:: Which model to use?\n",
    "    ::param_data:: What data to use?\n",
    "    \"\"\"\n",
    "    fea_imp = pd.DataFrame({'imp': model.feature_importances_, 'col': data.columns})\n",
    "    fea_imp = fea_imp.sort_values(['imp', 'col'], ascending=[True, False]).iloc[-30:]\n",
    "    _ = fea_imp.plot(kind='barh', x='col', y='imp', figsize=(20, 10))\n",
    "    return fea_imp\n",
    "    #plt.savefig('catboost_feature_importance.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>imp</th>\n",
       "      <th>col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.571310</td>\n",
       "      <td>embarked_Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.144747</td>\n",
       "      <td>embarked_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.768155</td>\n",
       "      <td>pclass_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.874346</td>\n",
       "      <td>embarked_S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.990734</td>\n",
       "      <td>pclass_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9.046338</td>\n",
       "      <td>pclass_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11.745562</td>\n",
       "      <td>sex_male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.853853</td>\n",
       "      <td>Fare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.921846</td>\n",
       "      <td>SibSp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.143386</td>\n",
       "      <td>Parch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.646657</td>\n",
       "      <td>Age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16.293067</td>\n",
       "      <td>sex_female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          imp         col\n",
       "5    0.571310  embarked_Q\n",
       "4    1.144747  embarked_C\n",
       "10   1.768155    pclass_2\n",
       "6    1.874346  embarked_S\n",
       "9    2.990734    pclass_1\n",
       "11   9.046338    pclass_3\n",
       "8   11.745562    sex_male\n",
       "3   11.853853        Fare\n",
       "1   12.921846       SibSp\n",
       "2   14.143386       Parch\n",
       "0   15.646657         Age\n",
       "7   16.293067  sex_female"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the feature importance scores\n",
    "feature_importance(catboost_model, X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "712c072d0ba04efab8f3eb568f020900",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.8454199564239424\n",
      "Recall: 0.6954617647058824\n",
      "F1: 0.7630903187112227\n",
      "AUC: 0.8946746571306118\n"
     ]
    }
   ],
   "source": [
    "\n",
    "metrics = ['Precision', 'Recall', 'F1', 'AUC']\n",
    "\n",
    "eval_metrics = catboost_model.eval_metrics(train_pool,\n",
    "                                           metrics=metrics,\n",
    "                                           plot=True)\n",
    "\n",
    "for metric in metrics:\n",
    "    print(str(metric)+\": {}\".format(np.mean(eval_metrics[metric])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>pclass_1</th>\n",
       "      <th>pclass_2</th>\n",
       "      <th>pclass_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>22</td>\n",
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       "      <th>1</th>\n",
       "      <td>38</td>\n",
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       "      <td>71.2833</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
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       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
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       "      <td>53.1000</td>\n",
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       "      <td>35</td>\n",
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       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  SibSp  Parch     Fare  embarked_C  embarked_Q  embarked_S  sex_female  \\\n",
       "0   22      1      0   7.2500           0           0           1           0   \n",
       "1   38      1      0  71.2833           1           0           0           1   \n",
       "2   26      0      0   7.9250           0           0           1           1   \n",
       "3   35      1      0  53.1000           0           0           1           1   \n",
       "4   35      0      0   8.0500           0           0           1           0   \n",
       "\n",
       "   sex_male  pclass_1  pclass_2  pclass_3  \n",
       "0         1         0         0         1  \n",
       "1         0         1         0         0  \n",
       "2         0         0         0         1  \n",
       "3         0         1         0         0  \n",
       "4         1         0         0         1  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# We need our test dataframe to look like this one\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
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       "      <td>27.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
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       "      <td>22.0</td>\n",
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      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "   SibSp  Parch   Ticket     Fare Cabin Embarked  Age_test  \n",
       "0      0      0   330911   7.8292   NaN        Q      34.5  \n",
       "1      1      0   363272   7.0000   NaN        S      47.0  \n",
       "2      0      0   240276   9.6875   NaN        Q      62.0  \n",
       "3      0      0   315154   8.6625   NaN        S      27.0  \n",
       "4      1      1  3101298  12.2875   NaN        S      22.0  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our test dataframe has some columns our model hasn't been trained on\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One hot encode the columns in the test data frame (like X_train)\n",
    "test_embarked_one_hot = pd.get_dummies(test['Embarked'], \n",
    "                                       prefix='embarked')\n",
    "\n",
    "test_sex_one_hot = pd.get_dummies(test['Sex'], \n",
    "                                prefix='sex')\n",
    "\n",
    "test_plcass_one_hot = pd.get_dummies(test['Pclass'], \n",
    "                                   prefix='pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine the test one hot encoded columns with test\n",
    "test = pd.concat([test, \n",
    "                  test_embarked_one_hot, \n",
    "                  test_sex_one_hot, \n",
    "                  test_plcass_one_hot], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>895</td>\n",
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       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
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       "      <td>8.6625</td>\n",
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       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
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      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "   SibSp  Parch   Ticket     Fare Cabin Embarked  Age_test  embarked_C  \\\n",
       "0      0      0   330911   7.8292   NaN        Q      34.5           0   \n",
       "1      1      0   363272   7.0000   NaN        S      47.0           0   \n",
       "2      0      0   240276   9.6875   NaN        Q      62.0           0   \n",
       "3      0      0   315154   8.6625   NaN        S      27.0           0   \n",
       "4      1      1  3101298  12.2875   NaN        S      22.0           0   \n",
       "\n",
       "   embarked_Q  embarked_S  sex_female  sex_male  pclass_1  pclass_2  pclass_3  \n",
       "0           1           0           0         1         0         0         1  \n",
       "1           0           1           1         0         0         0         1  \n",
       "2           1           0           0         1         0         1         0  \n",
       "3           0           1           0         1         0         0         1  \n",
       "4           0           1           1         0         0         0         1  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'SibSp', 'Parch', 'Fare', 'embarked_C', 'embarked_Q',\n",
       "       'embarked_S', 'sex_female', 'sex_male', 'pclass_1', 'pclass_2',\n",
       "       'pclass_3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list of columns to be used for the predictions\n",
    "wanted_test_columns = X_train.columns\n",
    "wanted_test_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.rename(columns = {\"Age_test\": \"Age\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[\"Age\"] = test[\"Age\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Age</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>pclass_1</th>\n",
       "      <th>pclass_2</th>\n",
       "      <th>pclass_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</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",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "   SibSp  Parch   Ticket     Fare Cabin Embarked  Age  embarked_C  embarked_Q  \\\n",
       "0      0      0   330911   7.8292   NaN        Q   34           0           1   \n",
       "1      1      0   363272   7.0000   NaN        S   47           0           0   \n",
       "2      0      0   240276   9.6875   NaN        Q   62           0           1   \n",
       "3      0      0   315154   8.6625   NaN        S   27           0           0   \n",
       "4      1      1  3101298  12.2875   NaN        S   22           0           0   \n",
       "\n",
       "   embarked_S  sex_female  sex_male  pclass_1  pclass_2  pclass_3  \n",
       "0           0           0         1         0         0         1  \n",
       "1           1           1         0         0         0         1  \n",
       "2           0           0         1         0         1         0  \n",
       "3           1           0         1         0         0         1  \n",
       "4           1           1         0         0         0         1  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a prediction using the CatBoost model on the wanted columns\n",
    "predictions = catboost_model.predict(test[wanted_test_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our predictions array is comprised of 0's and 1's (Survived or Did Not Survive)\n",
    "predictions[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         0"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.DataFrame()\n",
    "submission['PassengerId'] = test['PassengerId']\n",
    "submission['Survived'] = predictions # our model predictions on the test dataset\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         1\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What does our submission have to look like?\n",
    "gender_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submission dataframe is the same length as test (418 rows).\n"
     ]
    }
   ],
   "source": [
    "# Are our test and submission dataframes the same length?\n",
    "if len(submission) == len(test):\n",
    "    print(\"Submission dataframe is the same length as test ({} rows).\".format(len(submission)))\n",
    "else:\n",
    "    print(\"Dataframes mismatched, won't be able to submit to Kaggle.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submission CSV is ready!\n"
     ]
    }
   ],
   "source": [
    "# Convert submisison dataframe to csv for submission to csv \n",
    "# for Kaggle submisison\n",
    "submission.to_csv('../kaggle_submission.csv', index=False)\n",
    "print('Submission CSV is ready!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         0"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the submission csv to make sure it's in the right format\n",
    "submissions_check = pd.read_csv(\"../kaggle_submission.csv\")\n",
    "submissions_check.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(catboost_model, open(\"model.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pickle.load(open(\"model.pkl\", 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "print(model.predict([[52,0,0,9.8,0,1,1,1,0,1,0,0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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