Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df = pd.read_csv('train.csv')\n",
    "final_df = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
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       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
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       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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",
       "5             6         0       3   \n",
       "6             7         0       1   \n",
       "7             8         0       3   \n",
       "8             9         1       3   \n",
       "9            10         1       2   \n",
       "10           11         1       3   \n",
       "11           12         1       1   \n",
       "12           13         0       3   \n",
       "13           14         0       3   \n",
       "14           15         0       3   \n",
       "15           16         1       2   \n",
       "16           17         0       3   \n",
       "17           18         1       2   \n",
       "18           19         0       3   \n",
       "19           20         1       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",
       "5                                    Moran, Mr. James    male   NaN      0   \n",
       "6                             McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                      Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8   Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                 Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "10                    Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "12                     Saundercock, Mr. William Henry    male  20.0      0   \n",
       "13                        Andersson, Mr. Anders Johan    male  39.0      1   \n",
       "14               Vestrom, Miss. Hulda Amanda Adolfina  female  14.0      0   \n",
       "15                   Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "16                               Rice, Master. Eugene    male   2.0      4   \n",
       "17                       Williams, Mr. Charles Eugene    male   NaN      0   \n",
       "18  Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0      1   \n",
       "19                            Masselmani, Mrs. Fatima  female   NaN      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  \n",
       "5       0            330877   8.4583   NaN        Q  \n",
       "6       0             17463  51.8625   E46        S  \n",
       "7       1            349909  21.0750   NaN        S  \n",
       "8       2            347742  11.1333   NaN        S  \n",
       "9       0            237736  30.0708   NaN        C  \n",
       "10      1           PP 9549  16.7000    G6        S  \n",
       "11      0            113783  26.5500  C103        S  \n",
       "12      0         A/5. 2151   8.0500   NaN        S  \n",
       "13      5            347082  31.2750   NaN        S  \n",
       "14      0            350406   7.8542   NaN        S  \n",
       "15      0            248706  16.0000   NaN        S  \n",
       "16      1            382652  29.1250   NaN        Q  \n",
       "17      0            244373  13.0000   NaN        S  \n",
       "18      0            345763  18.0000   NaN        S  \n",
       "19      0              2649   7.2250   NaN        C  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "main_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Fare</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <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",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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>47.0</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",
       "    </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>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>3</td>\n",
       "      <td>Svensson, Mr. Johan Cervin</td>\n",
       "      <td>male</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7538</td>\n",
       "      <td>9.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>Connolly, Miss. Kate</td>\n",
       "      <td>female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330972</td>\n",
       "      <td>7.6292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>2</td>\n",
       "      <td>Caldwell, Mr. Albert Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>Abrahim, Mrs. Joseph (Sophie Halaut Easu)</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2657</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>Davies, Mr. John Samuel</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>A/4 48871</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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",
       "5          897       3                    Svensson, Mr. Johan Cervin    male   \n",
       "6          898       3                          Connolly, Miss. Kate  female   \n",
       "7          899       2                  Caldwell, Mr. Albert Francis    male   \n",
       "8          900       3     Abrahim, Mrs. Joseph (Sophie Halaut Easu)  female   \n",
       "9          901       3                       Davies, Mr. John Samuel    male   \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  \n",
       "5  14.0      0      0       7538   9.2250   NaN        S  \n",
       "6  30.0      0      0     330972   7.6292   NaN        Q  \n",
       "7  26.0      1      1     248738  29.0000   NaN        S  \n",
       "8  18.0      0      0       2657   7.2292   NaN        C  \n",
       "9  21.0      2      0  A/4 48871  24.1500   NaN        S  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "final_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>417.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>2.265550</td>\n",
       "      <td>30.272590</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>35.627188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>120.810458</td>\n",
       "      <td>0.841838</td>\n",
       "      <td>14.181209</td>\n",
       "      <td>0.896760</td>\n",
       "      <td>0.981429</td>\n",
       "      <td>55.907576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>892.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170000</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>996.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.000000</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>1204.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId      Pclass         Age       SibSp       Parch        Fare\n",
       "count   418.000000  418.000000  332.000000  418.000000  418.000000  417.000000\n",
       "mean   1100.500000    2.265550   30.272590    0.447368    0.392344   35.627188\n",
       "std     120.810458    0.841838   14.181209    0.896760    0.981429   55.907576\n",
       "min     892.000000    1.000000    0.170000    0.000000    0.000000    0.000000\n",
       "25%     996.250000    1.000000   21.000000    0.000000    0.000000    7.895800\n",
       "50%    1100.500000    3.000000   27.000000    0.000000    0.000000   14.454200\n",
       "75%    1204.750000    3.000000   39.000000    1.000000    0.000000   31.500000\n",
       "max    1309.000000    3.000000   76.000000    8.000000    9.000000  512.329200"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, 20, 40, 60, 80])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_bins = np.arange(0,90,20)\n",
    "age_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_age = pd.DataFrame()\n",
    "df_age['Survived'] = main_df['Survived']\n",
    "df_age['Age'] = main_df['Age']\n",
    "df_age['Sex'] = main_df['Sex']\n",
    "df_age['AgeGroup'] = pd.cut(main_df['Age'], bins=age_bins)\n",
    "\n",
    "df_male = df_age[df_age['Sex']=='male']\n",
    "df_male =df_male.rename(columns={'AgeGroup': 'Male Age'})\n",
    "\n",
    "df_female = df_age[df_age['Sex']=='female']\n",
    "df_female = df_female.rename(columns={'AgeGroup': 'Female Age'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1008 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=[10,14])\n",
    "\n",
    "fig.add_subplot(3,2,1)\n",
    "sns.countplot(x='Pclass',hue='Survived',data=main_df)\n",
    "\n",
    "fig.add_subplot(3,2,2)\n",
    "sns.countplot(x='Sex',hue='Survived',data=main_df)\n",
    "\n",
    "fig.add_subplot(3,2,3)\n",
    "sns.countplot(x='Embarked',hue='Survived',data=main_df)\n",
    "\n",
    "fig.add_subplot(3,2,4)\n",
    "sns.countplot(x='AgeGroup',hue='Survived',data=df_age)\n",
    "\n",
    "fig.add_subplot(3,2,5)\n",
    "sns.countplot(x='Male Age',hue='Survived',data=df_male)\n",
    "\n",
    "fig.add_subplot(3,2,6)\n",
    "sns.countplot(x='Female Age',hue='Survived',data=df_female)\n",
    "\n",
    "# # Also a good idea to visualize the Age correlation:\n",
    "# g = sns.FacetGrid(main_df, col='Survived')\n",
    "# g.map(plt.hist, 'Age', bins=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AoGgc89z66OzkxVIdlAb8g0B2mtNBEkELMqWUUqcsMye/j6fP0P/uNfXqS8TlSqXfKbf4/MEpzXfa61w+C/QA3LO3RMu2VcY3JT2dOlVnA792OkQipNJfHqWUUl1IZk5+mqRn/jB7xo3Xu7zpqfYUnAu4t6UDhSWRXcB87AH+DxeH58TiJpbEbKpjfnY6dl1qQaaUUqrdMnPyBeSL2TM+/5/urD79nc7Tik/6/MHPt3KsEIgCGZsOxauW74ktSWIu1TEerPUuT6uuSy3IlFJKnYpP9zrnqp+kDRg12ukgJ/FHnz+Y0XxnYUnkCPACMBjggWXhRaGIqUl2OHXKJgK/cjpEZ9KCTCmlVLtk5uSP7zH2vF9ljJl63BitFDQaaG1NxEXAfqBPdSPht0qj85MXS3UCP4HsiU6H6CxakCmllGqzzJz8Qd6Bvt/0nHTZRSLidJy28vv8wRHNdxaWRCJY02D0BeSpNZE1B+riu5OeTp0qD3CX0yE6ixZkSiml2iQzJz8Tt+fHvc8t+Iy4PcfNhp/CMoE/tHJsA7ASGGyAp9dEZhtjkhYsERqihumP1TL54VomPFjLbxY0ADB/W5Spj9Qy5eFaLvp7HVsOt76c587qOD3vPMJdHzQCcLAuzkV/r+PsB2t5bVPko/OufS7EnhpHlwW9nED2lU4G6CxakCmllDopaxA/N/WafMVn3Vl9Bjid5xR82ecPXtB8pz0NxgtYc1x5Fu6IlZdUxNcmPV0nSnfDOzdnsea7PVn9nSxmb42ytDzK/ws28M/P9WD1d3vy5YlefvdeY6v3uG1OA1fmeD56/+z6CN+d5mXZt7K4Z2kYgH+XRDhniIthvRwvJe4ikO12OkRHOf5fUSmlVJdwjnfA6KsyRk+Z7HSQDrjX5w8e189aWBLZC7yJPQ3Gg8vDb0diJtL8vK5CROiZZn3NSBwiMRBABI40Wq1/1Q2GYb1a7nJ+bVOEMX1cTBh4rETwuoRQBBqj4HZBNG64pyjMHRemJ/z7tEEe8C2nQ3SUFmRKKaVOKDMnvzfiuqX3tGvPE5erK7dETANubuXYW0A9kFlWZWo+2BVblLxYnS8WN0x5uJZBf6rhsrEe8kd4+Ns1GVz1r3pG/LmGp9dG8F90fDFVGzb84f0wv/n0x499eaKX10uiXPZ0Hb+4KJ0Hl4f52iQvmd6UGUf4WwLZvZ0O0RFakCmllGqV3VX5pZ6TP3ueO6vPEKfzdIL/8/mDvZrvLCyJ1AHPAYMAHioOL6lpNFXJDtdZ3C5h9Xd7Un5bL5btibH+QIy/LA3z5pd7UH5bL74xxcttcxqOuy7wbiM/mZH2UQvbUdkZQvDLmRR/uydTh7r59+YoN+R5+VZhPTe8EGLJrmiyvlprBgE/czpER2hBppRS6kQmuXsPvLxH15jioi2GAL9s5dgSYCfQLxQhWlgSmZe8WInRJ0O42OfhrdIoa/bHyB9hjQv7wtlePth1/OIERbtj3DGvAd89NdyzNMydixq5f1n4Y+f873uN/PIT6Ty7LsJFo9w8eV0PAgtbH4+WRN8nkJ3tdIhTlfCCTETcIrJKRN6w348RkSIR2SIiz4tImr0/3X6/xT7uS3Q2pZRSrcvMyc8Cbul97rUTxeXuSk9VnsyPff7gGc13FpZEYsAzQG9Anv8wumFPTbws2eE66mBdnKoGa6xYfcQwb1uUswa6qG6AzRVWETZvq7WvuUXfyKLsx70o+3EvfjwjjV98Ip3vTz82IX5pRYzyI3E+7fMQihhcYo1Nq0+NEXe9ge86HeJUJaOF7EfAxibv/wD8xRgzDqgEbrH33wJU2vv/QuuPKCullEqOqzN8U3K9/YblOB2kk6UDd7dyrBRYitWSxhOrIrPjXWwejL21houfrGPSQ7Wc91gdl431cPWZXh67JoP/eKGeyQ/X8vTaCH+6zFrAoLAkwq8XHN992ZJfvtPI7y+xxpd9aaKXh4ojnPdYHT/KT5lVjH5MIDslnjRoL0nknzMRGQE8CfweuA24BjgIDDHGREXkfCBgjPmsiMyxXy8REQ+wDxhoThBw2rRppri4uM15fP5gB76NSpSyWTOdjqC6EBFZYYyZ5nSO011mTv5I3N7fDbjyh1e70rP6OZ0nQT5TNmvmcbPzF+R6BwL/hzWLf+R3l6RfPWmw+7RbzPo09l0C1Y84HaK9Et1Cdg9wB3B01rj+QJUx5ujov3JguP16OLALwD5ebZ//MSLybREpFpHigwcPJjK7Ukp1S5k5+S7ga70mXX7maVyMAdzj8wePe2q0sCRyEHgdexqMB5aF32mMmrY1IalU8FMC2V1ujHzCAovI1cABY8yKzryvMeZRY8w0Y8y0gQMHduatlVJKWaaLJy0vY9TESU4HSbCzaX3M0dtADdBzb60JvbcjtjB5sVQHjQNucDpEeyWygrwQKBCRMqxHiS8B7gX62F2SACOAo+uG7QZGAtjHs4GKBOZTSinVTGZOfhrwpayzLx0qnrRMp/Mkwf/4/MHjWgELSyL1WOtcDgR4ZEV4WVWDOZTscOqUfc/pAO2VsILMGPNzY8wIY4wP+CLwjjHmK8ACjlWuN2M1CwMUcmzCvhvs87vUQEqllDoNnIfLnZ0xcmJ3GafXD/htK8dWApuBgeEY8Zc2ROYkL5bqoE8SyB7rdIj2cKKP9WfAbSKyBWuM2OP2/seB/vb+2wC/A9mUUqrbyszJ9wDXZ531ycGutIwuPet5O33X5w/mNd9ZWBKJA/8CsgBXYUl0y46qeGnS06lTIcDXnQ7RHkkpyIwx7xpjrrZfbzPGTDfGjDPG3GiMabT3N9jvx9nHtyUjm1JKqY9MRmRAD9/U7tI6dpQHa7ql4xSWRLYD72EP8P/byvCcWNzEWzpXpZybCGSnzNpOJ9PlnkJQSinV+ewnK6/PHDejvysja4DTeRxwuc8fLGjl2KuAAdLX7I9XrN4XX5bEXOrUjcYav94laEGmlFIKIA8Y0WPc9KlOB3HQ3T5/8LgZTgtLIpXAy9itZPcvC7/bEDWhZIdTp+QbTgdoKy3IlFKqm7MXEL82w3dOH3dm9jCn8zhoHPDjVo4tAA4BvSvqTeO8rdF3khdLdcDnCGQft5h8KtKCTCml1DggJ/PMCyY7HSQF/LfPHxzcfGdhSSSMNQ1Gf4DHV0VWVoTi+5IdTrVbD+AKp0O0hRZkSimlrkkbltvT06u/z+kgKaAXcGcrx9YC64FBcYN5dn1kdvJiqQ5obWxgStGCTCmlurHMnPzRwKTMseeNcjpLCvm6zx88bu3KwpKIAZ7FanVxz90a27HlcHxD0tOp9ppJINtz8tOcpQWZUkp1b5ch0ujpN/wsp4OkEBfWyjLHKSyJlANzgWEADxeH50bjH63PrFJTX+B8p0OcjBZkSinVTWXm5GcA0zNGT+7h8qb3dDpPirnQ5w9+qZVjQaARyNhcEa9etjv2QRJzqVPzWacDnIwWZEqlsIaGBqZPn87kyZOZMGECv/nNbz52/Ic//CE9ex77PfrnP/+ZvLw8Jk2axKWXXsqOHTuSHVl1LbmAJ2PE2eOdDpKi/uDzB49bz7OwJHIEeBEYAvDAsvDiurA5kuxwql1SfmC/FmRKpbD09HTeeecd1qxZw+rVq5k9ezZLly4FoLi4mMrKyo+df84551BcXMzatWu54YYbuOOOO5yIrbqOfO2uPKGRQGt/iRYDe4E+NWEib5ZG305eLHUKphLI7u90iBPRgkypFCYiH7WARSIRIpEIIkIsFuP222/nj3/848fOv/jii8nMtP5BP2PGDMrLy5OeWXUNdnfltIxRk7O0u/KE7vD5gyOb7ywsiUSwpsHoC8gzayPr9tfG9S9c6hJgutMhTkQLMqVSXCwWY8qUKQwaNIjLLruM/Px87r//fgoKChg6dGir1z3++ONceeWVSUyqupgzAU/GyAm5TgdJcT2AP7VybCOwAhhsgKfWRN4yxiQtmGo3LciUUqfO7XazevVqysvLWbZsGe+99x4vvvgiP/jBD1q95plnnqG4uJjbb789iUlVFzPD6q4cked0kC7gCz5/8KLmO+1pMF4A0gDPop2xPRsPxdckPZ1qq3ynA5yIFmRKdRF9+vTh4osvZsGCBWzZsoVx48bh8/kIhUKMGzfuo/Pefvttfv/731NYWEh6erqDiVWq0u7KU3Kvzx887ndmYUlkH9ZTl8MAHlwefjscM+Fkh1Ntcp7TAU5ECzKlUtjBgwepqqoCoL6+nnnz5nHuueeyb98+ysrKKCsrIzMzky1btgCwatUqvvOd71BYWMigQYOcjK5Sm3ZXtt9U4D9bOTYbCAGZO6tN7fs7Y4uSF0u1wwAC2Wc4HaI1WpAplcL27t3LxRdfzKRJkzjvvPO47LLLuPrqq1s9//bbb6e2tpYbb7yRKVOmUFDQJVYMUck3A2j09Buu0120z+99/mDv5jsLSyJ1wD+BwQAPF4eXHGk0lc3PUykhZbstU34pAaU6zZ9yoO6A0ynaZRKw6tomO+J3Q+Duj51T+1M3BLIBePsi4CKA7fbR7R8dS1lZg+D2UqdTdBuZOfnpwDRP9pB6lzfjuOJCndAg4FdAS4Mzl2FNPtq/PkrF65sic782Oe0LSU2n2mIq8C+nQ7REW8hU99HFirFuQ/93SbaxgDttyLjBTgfpon7o8wdzmu8sLInEgGewFieXFzdEN5UfiW8/7mrltHEnP8UZWpAppVT3MhLA03foMKeDdFFpwJ9bOlBYEtkCLAGGAvx9VXh2XOfBSDVjnQ7QGi3IlFKqezkLqPP0GqAF2am72ucPtrY24stYv1vTivfED6zdHy9OYi51clqQKaWUclZmTr4AOUCtOzNbC7KO+YvPHzxuHHZhSeQQ8Bp2K9mDy8MLGqOmIdnhVKuyCGQPcTpES7QgU0qp7qMv0MPbb0SmeNJ6OB2mizsL+F4rx94GqoCe+2pN/YKy2LtJS6XaIiVbybQgU0qp7mMYgHfwWG0d6xwBnz943ILVhSWRBqwB/gMAHlsRXl5Zbw4mO5xqVUrORaYFmVJKdR+jALx9dEB/J+kL/G8rx1YBm4GBkTjxFzdE5iQvljqJ0U4HaEmbCjIRubAt+5RSSqW08UCdWwf0d6Zv+/zBic13FpZE4ljzXWUBrjc2R7eWVcU3Jz2dakkfpwO0pK0tZH9t4z6llFIpyB7QPw6ocWf2Hup0ntOIG7inpQOFJZEyYAH2AP9HV4TnxOImlrxoqhUpOVv2CQsyETlfRP4LGCgitzXZAlh/CJVSSnUN/YEMb/+RPcXtzXA6zGnmEp8/eH0rx14H4kD6+gPxwyv3xouSmEu1rEu2kKUBPbGWWOrVZDsC3JDYaEoppTrRMMB4sgel5C+j08BdPn8wvfnOwpJIFfAidivZA8vD79VHTF2yw6mPSckWshOuZWmMWQgsFJF/GGN2tOfGIpIBvAek25/zkjHmNyIyBngO619rK4CvGWPCIpIOPAWcC1QAXzDGlLX3CymllGrRcABXj+yeTgc5TY0FbgP+r4Vj7wGXA9mH60313K3R+deO9xYkNZ1qKiULsraOIUsXkUdFZK6IvHN0O8k1jcAlxpjJwBTgChGZAfwB+IsxZhxQCdxin38LUGnv/4t9nlJKqc4xCAi7evTq5XSQ09gvfP7gcePzCksiYaxpMPoBPLE6svpQKL432eHUR7p0QfYi1iO8/421yv3RrVXGUmu/9dqbAS4BXrL3PwlcZ7++1n6PffxSEZE25lNKKXViA4FGV3qmFmSJ0xOY1cqxdcAaYHDcYP65NjI7ebFUMynZStzWgixqjHnIGLPMGLPi6Hayi0TELSKrgQPAPGArUGWMidqnlGM3o9s/dwHYx6uxujWVUkp1XD8g7ErTgizBvubzB6c331lYEjHA80AG4J6/PbaztCL2YdLTKbAeskg5bS3I/i0i3xORoSLS7+h2souMMTFjzBRgBDAdaw6cDhGRb4tIsYgUHzyoEx8rpVQb9QUaxZuR5XSQ05wA9/r8weN6eApLIruBOdgD/O9fFp6zuSK2PhIz4SRn7O6iJz8l+U44qL+Jm+2fTbspDW1cD8oYUyUiC4DzgT4i4rFbwUYAu+3TdgMjgXIR8WD18Va0cK9HgUcBpk2bZiXsPc8AACAASURBVNqYXymluq3MnPx0rAesYuLRKS+SYAbwFaxxY80FgU8CPbZXmZqfzm18OdOL58pxnjPyR7jzxvZ15aa55binNVWnSsmCrE0tZMaYMS1sJyzGRGSgiPSxX/cALgM2Yk2Sd3TKjJux5mgBKORY4XcD8I4xRgsupZTquCzsbhpxefSXfXLM8vmDx7VGFpZEarBmFOiLtZTVkFAEXt4YLbljXuOrX32l/k9PrAo/u+FgbE1j1DQkO3Q3kZIFWZtayETkppb2G2OeOsFlQ4EnRcSNVfi9YIx5Q0Q2AM+JyO+wHhR43D7/ceBpEdkCHAa+2MbvoJRS6sQ+KsLE7dEWsuQYDvwc62G4jyksiSwtyPWuAs4E8oHzsB58izREOfTqpujmVzdFN6e5cV0xzjN2xgh33rh+rvEZHumR1G9w+uq6BRnWH5ajMoBLgZVYVX6LjDFrgXNa2L8NazxZ8/0NwI1tzKOUUqrtjhVhbm0hS6L/8vmDfyubNbOs+YHCkkgj1pOX6wpyvU8BOVi/G/OxJmWPhmMcKiyJbiksiW7xunjjsjM8vgtGuvNy+rnG9/CKjgU8dRGnA7SkTQWZMeYHTd/bXZHPJSSRUkqpzpYOIGk9vCKutj7MpTouA/gTJ2lssOcp+xD4sCDX+wzWmqPTsMZdZwDRSJxDb5ZGt71ZGt3mEoKXjfWMvnCUO+/M/q6zMr2SktM4pLBGpwO0pK0tZM3VAWM6M4hSSqmEyQBEXG6d2zH5bvD5g58qmzVzYVtOLiyJRLDGW28syPU+C5zBseIsE4jFDYfmbI2WzdkaLXMJb10yxj3yolGevNz+rrOy0qR3wr7J6SMlp2ho6xiyf2M9VQnWouJnAS8kKpRSSqlOlQ5IvLEuJbtquoF7ff7gZWWzZrarECgsiUSBEqCkINf7PFZDyFTgImAwEI8bDr29Lbbz7W2xnQKzP+Vzj/jkaE/e+AGuvJ5pkpIz0qeA/U4HaElbW8juavI6CuwwxpQnII9SSqlEMcaYeDwmLpfb6SjdzGRgr88fXIS1Es3LZbNm7mvPDezirBQoLcj1vsTxxVnMQMW7ZbHyd8ti5cDcT4xyD/uUz503foA7r3e69O3Ub9S1dd2CzBizUEQGc2xwf2niIimllOpkEY72csRjYVwufVov+dzAp+3tPp8/+AFWcfZS2ayZu09w3XEKSyIxYAuwxS7OfFgP0X0Ca4msOFCxaGdsz6KdsT3A2+ePcA/5tM+dlzfQnZedId19FZx2FcPJ0tYuy89jDUx8F2sW4r+KyO3GmJdOeKFSSqlU8FFBZuKxiODVgsxZLqyWrYuAv/j8waUcK852tudGhSWROLAN2FaQ630Va26zKVjF2Sis/90rlpTH9i0pj+0D3jlvmGvQJWM8EyYMcuf1yZABnfe1uoyu20IG/BI4zxhzAKxJX4G3ObZIuFJKqdR1bGmeeEzHkaUWwRqwfz5wt88fXM6x4mxbe25kF2dlQFlBrvd1rNVvJgOf4lhxdnj5nviB5XvCB4AFU4e6Bl46xpM3YZA7r18PGdRp3yq1demCzHW0GLNV0PZ1MJVSSjnroyLMxGO6bmJqO8/e/uDzB1dhFWcvls2a2a6hQvZi5juBnQW53jewJqqdjLVs09HirHLl3vjBlXvDC4GFkwa7+n9mrCfv7EGuvAGZriGd+J1SzR6nA7SkrQXZbBGZAzxrv/8C8GZiIimllOpkTQqyqLaQdR3n2Nvvff7gOo4VZxvbcxO7OCsHygtyvW9iraQzCas4G41dnK3dH69Yuz+8CFh01gBX38+O8+RNHOTKG5jlGtaJ38lpjVitiCnnhAWZiIwDBhtjbheRz2H1dwMsAf6Z6HBKKaU6hXZZdn0T7e23Pn9wA8e6Nde15yZ2cbYH2FOQ652D9YTmRKxuzaPFWdXGQ/HKjYfC7wPvn9nflf3ZMzx5kwa78gZlyQiRLj2d3SYC1TGnQ7TkZC1k92CtxYUx5hXgFQARmWgfuyah6ZRSSnWGYy1ksah2WXZ9ecCvgV/7/MES4GWs4mxVe25iF2f7gH0Fud63sZ7QnMixBwIAqjdXxI9srggvAZac0Vd6XzHOe9akwa68wT1lpKvrVWcfOh2gNScryAYbY46rvo0x60TEl5BESimlOluEo+N+tcvydJML/AL4hc8f3Mqx4mx5e25iF2cHgPnA/IJc70BgAla3ps8+7cjWSlP9wPJwEVA0Olt6XpnjOWvKEHfekJ4yuosUZ122IOtzgmP62LRSSnUNYayn+YhHw/UOZ1GJcwZwB3CHzx8sw+rVeglYWjZrpjnRhc0VlkQOYk119W5Brrc/cDZWy9kZWN2aNTuqTdXDxZHlEFk+ordkXZXjGT9liDtvWC/xuURS9cG/LluQFYvIt4wxjzXdKSLfBFYkLpZSSqlO9FGrWKz28CEGn+FkFpUcPuA2eyv3+YMvYxVn759CcVYBLAQWFuR6+2K1nH0CyLFPqS0/YiofXRFZAZEVQ3tK5swzreJseC8Z43alVHHWZQuyHwOvishXOFaATQPSgOsTGUwppVTnCJUWxTNz8kOAN1q1LyUXVlYJNQL4kb3t8fmDr2IVZ++VzZoZb8+NCksilcBiYHFBrrcP1trWnwDGY7XC1u6tNZV/WxlZCZGVg7IkY2aOZ/w5Q915I3vLWLdLnFy2qw5rEt2UdMKCzBizH7hARC7Gaq4ECBpj3kl4MqWUUp2pHBgUObRTC7LubRhwq73t9/mDr2EVZwvKZs1s19OHhSWRKqxZF5YU5Hp7YxVnF2G1oAGEDtSZw0+sjqx+YnVkdf8ekj7zTE/uuUPdeSOz5QyPS9o69VZneZ9AdbsK0GRq61qWC4AFCc6ilFIqcbYDY2K1FXtMNBwST1qm04GU4wYD37G3Qz5/8HWs4mx+2ayZ7Xr4o7AkcgQoAooKcr29sFrMLsR6atMFhCrqTcVTayJrn1oTWZudTtrVZ3rPnDbMnTe6j4zzuMTbmV+sFQuT8BmnLNnVqVJKKWfsxP7//FhD7UFPz36jHc6jUssA4BZ7q2xSnM0rmzWzXVOlFJZEaoDlwPKCXG8WVnF2AdZKAW6gvrqRin+ui6z/57rI+l5peK/K8eRMH+7O8/Vx5XjdktaZX6wJLciUUko57iAQB4iHqg+iBZlqXV/g6/ZW7fMH/41VnM0pmzWzoT03KiyJ1GGNQV9RkOvNxJqm43ysFQjcQGNNmEPPfxjd8PyH0Q090/BcOc4zbvpwd96Yvq4z09yS3hlfyBhTLyLtmgok2bQgU0qp7uEg9tQXsdrDBxk0xuE4qovIBr5qbzU+fzCIVZy9WTZrZrumUCksiYSAVcCqglxvD+BMYAZwLlY90lgbpuLFDdFNL26IburhwX1ljueM/OHuvLF9XbnpHsk41S8hIksIVKf0pMhakCmlVPdQjTX9hSdavV8H9qtT0Qv4or3V+fzBt7CKszfKZs2sa8+NCksi9cAaYE1Brjcdq+UsH2thdQ8Qro9S8crG6OZXNkY3p7lxXTHOM/b8Ee68M/q5xmd4pL1zoaZ0dyVoQaaUUt1CqLTIZObklwP9wvqkpeq4LOAGe6v3+YNzgBeBf5fNmlnTnhsVlkQagbXA2oJc71NY85tNxyrQvEAkHONQYUl0S2FJdIvXxRuXn+HxXTDSnZfT33VWhkfa8oBKys8OoQWZUkp1H9uBEbEjB/aZaKRBPN5T7gJSqokewHX21ujzB+ditZy9XjZrZnV7bmQXZ+uB9QW53meAcVitZjOAdCAaiXMoWBrdFiyNbvO4CH5mrGf0hSPdE3L6u8ZneqVn83saYw6JyAcd+4qJpwWZUkp1HzuASwFiDTX79UlLlQDpwDX2Fvb5g/OxirPXymbNPNyeGxWWRMLABmBDQa73X1jLNk3DemIzA4hG41TM3hItm70lWuYS3rx0jHvURaM8eWf2d52VlSa9AETk9VSef+woLciUUqr7+OhJy2jlnm1akKkESwOutLdHfP7gAqzi7NWyWTPb1W1eWBKJAJuATQW53ueAsVgPA1yINZ9aLG44NG9bbMe8bbEdAm/NGOGe9oWzveGxfV3PdOaXShQtyJRSqvvYjzVJJ43lGzZnjDz7YofzqO7DA1xmbw/6/MH3sIqzV8pmzdzXnhsVlkSiwGZgc0Gu9wVgDDAVa5WAwUDMQMWS8tiBJeWxXxeWRMo784skihZkSinVTYRKi6oyc/L3Aj0b92zaF4801Li8Gb2czqW6HTdwsb391ecPLsYqzl4umzVzT3tuVFgSiQFbgC0Fud6XgdFYc5x9Athtb12CFmRKKdW9LMUafF0brdq/JW3g6HOcDqS6NRfwSXu71+cPLsEqzl4qmzVzV3tuZBdn24BtBbneVwFPYUnEdHbgRNGCTCmlupcNWAUZ4QPbSrUgUylEsAbsXwDc7fMHl3OsONvenhsVlkTiQEpPBNucK1E3FpGRIrJARDaIyIci8iN7fz8RmScipfbPvvZ+EZH7RGSLiKwVkamJyqaUUt3YDqwJYr0NZau2mng85nQgpVogWHOR/RHY5vMHV/j8wZ/7/MFxDudKmIQVZEAU+C9jTB7W/CG3ikge4AfmG2NygPn2e7Cewsixt28DDyUwm1JKdUuh0qIo1tqC/eINteFYXeVOpzMp1QZTgTuBUp8/uNrnD/63zx/MdTpUZ0pYQWaM2WuMWWm/rgE2AsOBa4En7dOexG46t/c/ZSxLgT4iMjRR+ZRSqhtbiTVfFJGKnaUOZ1GqvSYD/wts8vmD633+YMDnD57tdKiOSmQL2UdExIf11EMRMNgYs9c+tA/rEVWwirWmA/jK7X3N7/VtESkWkeKDB3X1D6WUOgVHizBp2PWhFmSqK5sA/AZY5/MHu/S8egkvyESkJ/Ay8GNjzJGmx4wxBmjXExDGmEeNMdOMMdMGDhzYiUmVUqp7CJUWHQHKgN6RA9sOxRtDlQ5HUqqjispmzdzhdIiOSGhBJiJerGLsn8aYV+zd+492Rdo/D9j7dwMjm1w+gi40f4hSSnUxS4HeAJHK3SUOZ1Gqo551OkBHJfIpSwEeBzYaY/7c5FAhcLP9+mbg9Sb7b7KftpwBVDfp2lRKKdW5NmE9yUb9lmUrHc6i1CkzxsSA553O0VGJnIfsQuBrwDoRWW3v+wUwC3hBRG7Bevz68/axN4GrsGbcDQHfSGA2pZTq7sqBI0CP8P6tB2O1h3e6e/Yb5XQopdrNmIVlf7i6XcsvpaKEFWTGmMXY//pqwaUtnG+AWxOVRyml1DGh0qJ4Zk7+HOAGYFdD+YbirPEXaUGmuhxxuf7hdIbOkJSnLJVSSqWkIqx/OLtCJYs3mGi43ulASrWHicf2Ac85naMzaEGmlFLdVKi06DCwChhgouFY+GDZKqczKdUuxvypbNbMiNMxOoMWZEop1b3NB3oA1G1ctMwePqJUyjPx2BFxex5xOkdn0YJMKaW6txLgMJAVrdxdHa3cs8HpQEq1STz2QNmsmXVOx+gsiXzKUimlVIoLlRbFMnPy3wBuAupCmz94P3vGjROcznVUvKGWirfuI3zIWnJzwFU/IlpTQfXifxGp2MWQm/5M+tCcNl+bPvwsKt99gvptK0gbNIYBV/8XALUfLiAeOkLv865NzhdTHWLi8QbxpN3tdI7OpC1kSimlioBGIK1x98a90drDKTPj+eH5j5Ix9lyGf+thhv3nX/H2H0nagNEMvP4XpI88cd3Y0rXxxjrC+7Yy7D/vR9xewgfLiEcaqVs3j15TZybpW6mOMrHIE2WzZlY4naMzaUGmlFLdXKi0KATMxl5buGH7ig+cTWSJN9bRsOtDek66HABxe3Fl9MQ7YCTe/iNO6VoQTDyKMYZ4pBFxuTmy7BV6Tb0GcWunUVdgTDzm8qbf6XSOzqYFmVJKKYD37J/u0OYlm2N1leWOpgGiVftxZ/am4s172PPED6l46z7i4YYOXetKz6THGdPY+48f4u7ZF0nPIrx3M5lnnp/gb6M6i4lGXiybNdPxP5+dTQsypZRSR6fAWIzdSla7fv5bTj9waeIxwvu20uucqxj2jfsQbzpHlr7Y4Wuz829g2Df+Sr9Lvkn1omfIvugr1KyZw8HXZlH1wWkxpdVpyxhjXN703zqdIxG0IFNKKXXUm4Ab8DSWb9gTqdi1+mQXJJKn1wDcvQaQPiwXgMzcCwnv39pp14b3b8UYg7ffCEKbFjPwOj/Ryn1EDu/u3C+iOo2JhueUzZq5yekciaAFmVJKKQBCpUX7gSAwDKBm5Rtvm1i00ak87p598fQeQKTC6p1q2LEG74C2re7UlmurFj1Dn098FeJRMHFrpwjGua+sTsDEYxFxuU/bJRa1IFNKKdXUbKAWyIrVHKpr2LX+vZNdkEj9PvNdDr1xF3v+/n3CB7bT+/zPE9r8AeUP3Ezjnk0ceOm37H/+VwBEayrY/+JvTnjtUaHNS0gbMg5Pr/64MnqSNmgsex6/FRMLkzZobNK/pzq5WF3VX3f86dptTudIFHF6jEBHTJs2zRQXF7f5fJ8/mMA06lSVzUrSo+aB7OR8jmq/QHWbTxWRFcaYaQlM0+1l5uTPAP4fsB23xzXgyh99z5We1d/pXKr7ijfW7Yk3hnzlD379tFgmqSXaQqaUUqq5ZcAWYACxaLyu5IM5TgdS3Zcxhlio+pbTuRgDLciUUko1EyotigP/BHoCrvrSJaXR6gNbHI6luqlY7eF/737kW7OdzpFoWpAppZQ6Tqi0aBuwCBgKULNmzmxj4nFnU6nuJh5prAW+7nSOZNCCTCmlVGteBQyQHjm4vSK8b2uR04FU9xKrq/x5+QM3HXY6RzJoQaaUUqpF9mSxLwNDAI4sf+WdWP2R/c6mUt1FLFS9evfDt9zvdI5k0YJMKaXUiSwADgJ9TKQxemTZqy+YWDTsdCh1ejOxaDTeGPqi0zmSSQsypZRSrQqVFoWBR4FsIC1yaMfhUMn7hQ7HUqe5aM2hu3Y/8s0Sp3MkkxZkSimlTihUWrQFeBYYAUjdxoUfhg9sW+5wLHWailTuLaqc/+gvnM6RbFqQKaWUaou3geXAcIDqJS/MiYWq9zobSZ1uYrWVB+o2vXd9qLSo685af4q0IFNKKXVS9txk/wCqgb4mGo4dWfbKiyYW0YUfVaeIhxsaQluXfb5q4VPdstDXgkwppVSbhEqLaoAHgF5AWqRiV2XdpsWvOxxLnQZMPBav377ijoq37lvodBanaEGmlFKqzewJY/+JPZ4stGnRxsZ9W3R+MtUhDbs+fLpu/fxuM8VFS7QgU0op1V4LgKVYRRnVS1+YG6ur2u1sJNVVhQ+WLaspfu073XHcWFNakCmllGoXezzZk8BhoB+xaLxq8TP/ijXUHnQ4mupiojWH9tSun39NqLSo249F1IJMKaVUu4VKi+qwxpNlAT1itYdDVYuefireWNctlrlRHRdvDNWFSouuO1L0ygGns6QCLciUUkqdklBpURlWUTYEyIgdOVhbtfhfT8bD9VXOJlOpLh5pDIe2LL318NwHdT47W8IKMhH5u4gcEJH1Tfb1E5F5IlJq/+xr7xcRuU9EtojIWhGZmqhcSimlOk+otGgl8BAwFEiPVu09Uv3+s0/GIw01DkdTKSoebWysXTcvENq0+Cmns6SSRLaQ/QO4otk+PzDfGJMDzLffA1wJ5Njbt7H+ciullOoCQqVFS4G/YU0amxY5XF5VveTFJ+ORxjqHo6kUY6LhcM3K4P0N21fe1d0H8TeXsILMGPMe1oDPpq7FGgiK/fO6JvufMpalQB8RGZqobEoppTpXqLRoEfAE1pOX3sjB7RVHlr38lImG6x2OplKEiYbDR1YFH2zctf6XodKiiNN5Uk2yx5ANNsYcnYF3HzDYfj0c2NXkvHJ733FE5NsiUiwixQcP6gM9SimVQt7FmqNsJOAJ79ty4Mjy154ysUiDs7GU0+KRxsYjq4IPNu5c93N9orJljg3qN8YYoN3NlcaYR40x04wx0wYOHJiAZEoppU6F3QU1F3gRGAW4G/ds2ndkxb+fMbFo2Nl0yinxxlDdkWWv3GcXY1qctyLZBdn+o12R9s+jj7ruxvoX1VEj7H1KKaW6ELsoewN4DRgNuBt3rd99ZPmr/9CB/t1PrL7mSPWS5+8J7yv9tRZjJ5bsgqwQuNl+fTPwepP9N9lPW84Aqpt0bSqllOpC7KLsVSDI0aJs98a9VQufeixWf2Sfs+lUssTqqiqr33/2zkjFrv/RYuzkEjntxbPAEiBXRMpF5BZgFnCZiJQCn7HfA7wJbAO2AI8B30tULqWUUolnF2UvYv3DezSQEa3eV3P47Uf/HqnaX+JsOpVo4UO7dlYtevqX0ep9d4dKi7S7ug08ibqxMeZLrRy6tIVzDXBrorIopZRKvlBpUTwzJ/8VrIe4vglUmnDoSOU7jz6fff4XPpM+9MwLHI6oOpmJx+P1W4pW1q6bdxfwor3MlmoDnalfKaVUwoRKi0yotOh9rB6RDGAAxpjqD56bV7dx4Us62P/0EQ/X11QvfWF27bp5vwJe0GKsfbQgU0oplXCh0qIS4H+AWqwHt6Ruw8IPq5c8/7d4Q12Fs+lUR0Uq9+w6PP/RZ8N7N/8kVFo0Wyd9bT8tyJRSSiVFqLRoH/A7YA0wBvCG9289eHj+I49GKvdudDadOhXGxE1oa/HKyncevzceqv5ZqLRos9OZuiotyJRSKsFExIjI3U3e/1REAie55joRyWvlWK6IvCsiq0Vko4g82olZ3xSRPp1wn4CI/LT5/lBpUS3WguTPYU0A3iveUBuufOexF0JblgVNLKKThnYR8UhD3ZGiV+bWrn4zAOaeUGmRLirfAQkb1K+UUuojjcDnROT/jDGH2njNdVjzeW1o4dh9wF+MMa8DiMjE9oQREbcxJtbSMWPMVe2516mwxxa9mZmTX4b1QFcWsK92zezi+u0rN/WeVnClt++wFotRlRqi1ft3Vy95YXasrvLuUGmRtm52Am0hU0qpxIsCjwI/aX5ARHwi8o6IrBWR+SIySkQuAAqAP9mtYGc0u2wo1hJzABhj1tn3+rqI3N/k3m+IyKft17UicreIrAF+LiIvNjnv0yLyhv26TEQGiMgsEbm1yTkftXiJyO0istzO/Nsm5/xSRDaLyGIg92T/UUKlRRuA32BNezQGyIwdOVBb+c7fXqxZO+9f8XB99cnuoZIrHg2H6jYsXHJ4/mMPxOoq79BirPNoQaaUUsnxAPAVEclutv+vwJPGmElY60DeZ4z5AGvC7NuNMVOMMVubXfMX4B0ReUtEftLGLsYsoMgYMxnricd8Ecmyj30BqwuxqeeBzzd5/3ngeRG5HMgBpgNTgHNF5JMici7wRXvfVcB5bchEqLToEHAX8AjQE6sb01VfuqS0Ys4DDzTuLV1iT42kHGRMPN64Z9PKitl/fb1u48K7MPE/hUqLDjud63SiXZZKKZUExpgjIvIU8EOgvsmh84HP2a+fBv7Yhns9ISJzgCuAa4HviMjkk1wWA162r4+KyGzgGhF5CZgJ3NHsM1aJyCARGQYMBCqNMbtE5EfA5cAq+9SeWAVaL+BVY0wIQEQKT/Y9jrK7MD/IzMn/ELgR+ARw2IRD1dUfPDs3ffhZ63pOuvwad2b20LbeU3WeaPWBbTUr31gbOVy+GXjSbtlUnUwLMqWUSp57gJXAEx29kTFmD/B34O8ish44G6trtGnPR0aT1w3Nxo09B3wfOAwUG2NaWmfyReAGYAhWixmAAP9njHmk6Yki8uMOfB0AQqVF1Zk5+Y8D7wPfwJrhf3fj7o17G/eUPNbrnKvyM0ZNuljcnrSOfpY6uXhjqLJuw7vF9duKt2P9WVgYKi2KOJ3rdKVdlkoplSTGmMPAC8AtTXZ/gNXVB/AVYJH9ugar1ek4InKFiHjt10OA/sBuoAyYIiIuERmJ1a3YmoXAVOBbHN9dedTzdrYbsH4hA8wB/lNEetqfP1xEBgHvAdeJSA8R6QVcc4LPbpU9kexG4NdYyy5ZLXQmbmpWvrG08t2/PxA+sL3YxGPRU7m/OjkTi4brt69Yeuite9+o31b8L+BnodKit7UYSyxtIVNKqeS6G6tl6qgfAE+IyO3AQayWIbCKpMdE5IfADc3GkV0O3CsiRxdsvt0Ys09E9gPbsZ7M3IjVGtciY0zMHsj/deDmVs750C6udhtj9tr75orIWcASEQFrotevGmNWisjzWHOMHQCWt+0/R8vsxahfy8zJL7bznQnsj1btO1K16Omgu9eAd3uefemMtMFnnCduT3pHPktZjInHI4d2baxZ+caGWG3FOuCZUGnRdqdzdRfSlcdKTps2zRQXF7f5fJ8/mMA06lSVzZqZnA8KNB9LrVJGoO0P04nICmPMtASmUSkmMyffjTXW7kagN1Y3aw2Aq0ev9J4TLj0vbVjuDJc3PesEt1GtMNFwqHH/1lV1Hy4oj9UcOoD1cMlyXfooubSFTCmlVEoLlRbFgMWZOfnLgHOB6wEfUBWvr6k6UvzaYvGmL82acMk5GSPPvsCV1qPDE9t2B7H6I/sbdq5fEdr03kETDUeBN4HZodKikNPZuiMtyJRSSnUJodKiMLDELswmYU2e6wNqTaTxUO3qt5bXrp27IuusT03I8E2+yJ3Ra5CTeVORMcZEq/Ztqt9StL5h59o6rCd+5wCLQ6VFuqaog7QgU0op1aXYLWarMnPyVwPjsab+GA80EI/tr/vwnXV1H76zLmP0lFHpIydM8PYbfpbLm9HiAxLdhYlGGsIHtq2s2/Du9mj1/ijWxML/BlaHSot0uaoUoAWZUkqpLilUWmSAjZk5+ZuwZvq/CqtLMwpUNOxYvbNhx+qdwFsZoyePTB8xYYK3/4izXN6M3g7GThoTj0ai1QdKw/u2lIZKl1SaSKOB/9/evcXYVdVxHP/+5tKZnrYZLqUDtIMQMgELrq5uvQAACTNJREFUhUrRCVd5MASQWxRRgkaUSEwAkQSMhhdIjGAMQRAw8ADBRqVcJJBGLkZEuTa0UCgFSqEXpi0D09KW0unMufTvw1oDh2HaTumUM535fZKdsy9r77P27JOc36y1z14sAP4FLM1/PxshHMjMzGyPloPFMuDWQnvHVOAE4ERgCumBuOt6V77S2bvylU7gseaDZkxrajvyiMZ92qbXjRtd4SzKxZ7Shq63iu+99eaWZfPXRrk4EeglhbCne5bO665xFW0bHMjMzGzU6Fk6bzVwX6G940HS/WVfA06mOpy9u2hV77uLVgGPN7UdObW5bcYRDXu1HlLXPKlV+Vkee4rYurVS2by+s7x+zbK+95Ys71v9Ri8RE4AAuoD/AC/lx4jYCOZAZmZmo06+z+wd4J1Ce8c/+DScnUQKZ1uBdX2dr63u63xtNYCaJoxrOvDwA8dNPqitoWXKtPoJe09Tw7hCjU7hcyIiotjzYWXLpu7K5vXdpXWrOntXLuyK4pZJfPp9/jbwPPBmz9J5Hpx9D+JAZmZmo1p+ntYyYFmhveMh0pBMx5DC2WTScFDl6Nu8sXf5gpW9yxes6N+3oaV1UuN+B7c27LV/a8PEfVvrCi2tdU2FvVVX37i76huxdWv0bVlf2fLRB5WeDd2Vj7q7S+vXdJe6V6yNcrGe9Cy2/qBYTxolYRHpvjA/smIP5UBmZmZjRg5ny4HlOZztB7SRBkg/Is8HKaT1lTe+v7m88f1lpJanT6ixuaF+wl6FukJLob55UqGueWKhrqlQ0Ljx4+samwtqbC6osamg+oam2Lq1TKVcjK3lYlTKJSqlYlTKpaiUilEuFaNSLEW5VIxyX7G88YP1pe4V66JSqgcmkIJXPdAITCONjLCEdHP+MuB935w/OjiQmZnZmJTD2ft5mg9QaO8oAFOBVuDQPE0lhTRIY0ArSr2V8oauEhu6isBmoJSnCttWn/evr5r6l5vza1N+v83ASlJ4XEUajqob2OwANjo5kJmZmWW5y29pnp4BKLR3NJC6NvcFxpNarVqAvfNrC6kbcTIpWEXVVJcPLaCYp96qaUt+7SIFr25S+Opx8BpbHMjMzMy2o2fpvDIpMHXtqGwOb+PzVCYFsBJQ8tiQtj0OZGZmZsMkh7dNeTIbsrodFzEzMzOz3cmBzMzMzKzGHMjMzMzMasyBzMzMzKzGRlQgk3SapCWS3pb061rXx8zMzOzLMGICmaR64DbgdGA6cIGk6bWtlZmZmdnuN2ICGfAN4O2IWBYRReBe4Jwa18nMzMxstxtJzyGbCnRWLa8COgYWknQJcEle/FjSki+hbiPRZGBtrSsxHPT7WtdgjzRqrj8A12lnSn9ld1XDzKxWRlIgG5KIuBO4s9b1qDVJ8yPi2FrXw2rD19/MbHQZSV2Wq4G2quVpeZ2ZmZnZqDaSAtmLQLukQySNA34APFLjOpmZmZntdiOmyzIiypIuAx4H6oG7ImJxjas1ko35btsxztffzGwUUUTUug5mZmZmY9pI6rI0MzMzG5McyMzMzMxqzIFsF0gKSTdWLV8l6dod7HPutkYgkHSYpKckLZT0hqRhu09I0j8l7TUMx7lW0lXDUaexRNI1khZLejVf3889Y+8LHPPs4RpiTNLHw3EcMzP7YkbMTf17qD7gO5Kuj4ihPqTzXGAu8Pog224BboqIhwEkzdiZykiqj4jKYNsi4oydOZYNH0nHAWcCx0REn6TJwLgh7tsQEeXBtkXEI/iXyGZmo4JbyHZNmfRrtysHbpB0sKQnc4vIvyUdJOl44GzgD7mV5NABux1AGqEAgIhYlI91kaRbq449V9Ipef5jSTdKegX4jaT7q8qdImlunl8habKkGyRdWlXmkxYvSVdLejHX+bqqMtdIekvSM8BhX/ivNXYdAKyNiD6AiFgbEWv6rwmApGMlPZXnr5U0W9KzwGxJL0g6ov9guRX12P7PhaQWSSsl1eXtEyR1SmqUdKikxyQtkPS0pMNzmUMkPS9pkaTffsl/DzMzG8CBbNfdBlwoqWXA+j8B90TEUcBfgVsi4jlSi8bVETEzIt4ZsM9NwJOSHpV05RC7GCcA8yLiaOAGoEPShLzt+6QxQavNAc6vWj4fmCPpVKCdNKboTGCWpJMlzSI9E24mcAbw9SHUyT7rCaAth9rbJX1zCPtMB74VERdQdc0kHQAcEBHz+wtGxEZgIdB/3DOBxyOiRPqH4fKImAVcBdyey9wM/DkiZgDv7fIZmpnZLnEg20UR8RHwF+AXAzYdB/wtz88GThzCse4GvgrcD5wCvCCpaQe7VYAH8/5l4DHgLEkNwLeBhwe8x8vAFEkHSjoaWB8RncCpeXoZeAk4nBTQTgIeioiefK7uIttJEfExMIs0Bms3KQBftIPdHomILXn+PuC8PH8+8MAg5eeQAjikAD1H0kTgeOB+SQuBO0itdQAnAH/P87N36oTMzGzY+R6y4fFHUoi5e1cPFBFrgLuAuyS9BhxJ6hqtDs/NVfO9A+4buxe4DPgQmB8RmwZ5m/tJX/D7k77IAQRcHxF3VBeU9MtdOB3L8jV6CnhK0iLgx3z2ujYP2GVz1b6rJa2TdBQpdP18kLd4BPidpH1I4e9JUuvphoiYua1qfcHTMTOzYeYWsmEQER+SWjEurlr9HKmlAuBC4Ok8vwmYNNhxJJ0mqTHP7w/sSxrPcwUwU1KdpDZSt+K2/Bc4BvgZn++u7Dcn1+08UjiDNELCT3OrCpKmSpoC/A84V9J4SZOAs7bz3jaI/OvZ9qpVM4GVpOs6K6/77g4OMwf4FdASEa8O3Jhb4V4kdUXOjYhKbtFcLul7uR7KraIAz/LZz6eZmdWQA9nwuRGYXLV8OfATSa8CPwKuyOvvBa6W9PIgN/WfCryWb9B/nHSvWRfpy3M56ZeZt5Ba4waVW2LmAqfn18HKLCaFwtUR8V5e9wSpi/X53ILzADApIl4ihYFXgEdJX/q2cyYC90h6PX8epgPXAtcBN0uaT+p63p4HSAHqvu2UmQP8kE9bPSGFrYvzZ2oxcE5efwVwab7WU3fudMzMbLh56CQzMzOzGnMLmZmZmVmNOZCZmZmZ1ZgDmZmZmVmNOZCZmZmZ1ZgDmZmZmVmNOZCZmZmZ1ZgDmZmZmVmN/R+RpG2WMoCfZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['Not Survived','Survived']\n",
    "explode = (0, 0.1)\n",
    "survivors_data = [main_df[main_df['Survived']==0].count()[0],main_df[main_df['Survived']==1].count()[0]]\n",
    "\n",
    "fig = plt.figure(figsize=[10,4])\n",
    "\n",
    "ax1 = fig.add_subplot(1,2,1)\n",
    "barplot = plt.bar(labels,\n",
    "                  survivors_data,\n",
    "                  align='center')\n",
    "barplot[1].set_color('C1')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "for i, v in enumerate(survivors_data): #display numbers above bars\n",
    "    ax1.text(i-0.05, v+2, str(v))\n",
    "\n",
    "ax2 = fig.add_subplot(1,2,2)\n",
    "ax2.pie(survivors_data,\n",
    "        labels=labels,\n",
    "        explode = explode,\n",
    "        autopct='%1.1f%%',\n",
    "        shadow=True, \n",
    "        startangle=120)\n",
    "ax2.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with Missing and Categorical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      "Age            1046 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1307 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Name           1309 non-null object\n",
      "Parch          1309 non-null int64\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Sex            1309 non-null object\n",
      "SibSp          1309 non-null int64\n",
      "Survived       891 non-null float64\n",
      "Ticket         1309 non-null object\n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 132.9+ KB\n"
     ]
    }
   ],
   "source": [
    "both_df = pd.concat([main_df, final_df], sort=True)\n",
    "both_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Missing values on features: Age, Cabin, Embarked and Fare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'male': 31.0, 'female': 29.0}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_age_mean = round(both_df[both_df['Sex']=='male']['Age'].mean())\n",
    "female_age_mean = round(both_df[both_df['Sex']=='female']['Age'].mean())\n",
    "ages_mean = {'male':male_age_mean, 'female':female_age_mean}\n",
    "ages_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Mr': 757,\n",
       " 'Mrs': 197,\n",
       " 'Miss': 260,\n",
       " 'Master': 61,\n",
       " 'Don': 1,\n",
       " 'Rev': 8,\n",
       " 'Dr': 8,\n",
       " 'Mme': 1,\n",
       " 'Ms': 2,\n",
       " 'Major': 2,\n",
       " 'Lady': 1,\n",
       " 'Sir': 1,\n",
       " 'Mlle': 2,\n",
       " 'Col': 4,\n",
       " 'Capt': 1,\n",
       " 'the Countess': 1,\n",
       " 'Jonkheer': 1,\n",
       " 'Dona': 1}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_names = {}\n",
    "\n",
    "for name in both_df['Name']:\n",
    "    title = name.split(',')[1].split('.')[0].strip(' ')\n",
    "    if title in title_names:\n",
    "        title_names[name.split(',')[1].split('.')[0].strip(' ')] += 1\n",
    "    else:\n",
    "        title_names[name.split(',')[1].split('.')[0].strip(' ')] = 1\n",
    "        \n",
    "title_names        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Mr': 1, 'Mrs': 2, 'Miss': 3, '*Rare*': 0}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counter = 1\n",
    "names = {}\n",
    "removal_list = []\n",
    "\n",
    "for name in title_names:\n",
    "    if title_names[name] > 100:\n",
    "        names[name] = counter\n",
    "        counter += 1\n",
    "        removal_list.append(name)\n",
    "\n",
    "names['*Rare*'] = 0 # '*' to avoid an unintended match\n",
    "names    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 87.50899164086688, 2: 21.1791963898917, 3: 13.302888700564973}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fare_by_class = {}\n",
    "\n",
    "for pclass in [1,2,3]:\n",
    "    fare_by_class[pclass] = both_df[both_df['Pclass']==pclass]['Fare'].mean()\n",
    "    \n",
    "fare_by_class    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 94, 'E': 41, 'G': 5, 'D': 46, 'A': 22, 'B': 65, 'F': 21, 'T': 1}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabins_number = {}\n",
    "\n",
    "for cabin in both_df['Cabin']:\n",
    "    if pd.notna(cabin):\n",
    "        if cabin[0] in cabins_number:\n",
    "            cabins_number[cabin[0]] += 1\n",
    "        else:\n",
    "            cabins_number[cabin[0]] = 1\n",
    "        \n",
    "cabins_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0, 'E': 1, 'G': 2, 'D': 3, 'A': 4, 'B': 5, 'F': 6, 'T': 7}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabins = {}\n",
    "\n",
    "for n, letter in enumerate(list(cabins_number)):\n",
    "    cabins[letter] = n\n",
    "    \n",
    "cabins    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: [('C', 94)], 2: [('F', 13)], 3: [('F', 8)]}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabins_list = []\n",
    "cabins_by_class = {}\n",
    "\n",
    "for pclass in [1,2,3]:\n",
    "    for cabin in both_df[both_df['Pclass']==pclass]['Cabin']:\n",
    "        if pd.notna(cabin):\n",
    "            cabins_list.append(cabin[0])\n",
    "    most_common = Counter(cabins_list).most_common(1)\n",
    "    cabins_by_class[pclass]=most_common\n",
    "    cabins_list = []\n",
    "    \n",
    "cabins_by_class #use indexes [0][0] to acess the cabin letter, i.e. 'cabins_by_class[1][0][0]' for First Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "genders = {'male':0, 'female':1}\n",
    "ports = {'C':0, 'Q':1, 'S':2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(df):\n",
    "    \n",
    "    df['Family'] = df['SibSp'] + df['Parch']\n",
    "    \n",
    "    for n,age in enumerate(df['Age']):\n",
    "        if pd.isna(age):\n",
    "            df.at[n,'Age'] = ages_mean[both_df.iloc[n]['Sex']]\n",
    "            \n",
    "    df['Sex'] = df['Sex'].map(genders)\n",
    "    \n",
    "    for n,cabin in enumerate(df['Cabin']):\n",
    "        if pd.notna(cabin):\n",
    "            df.at[n,'Cabin'] = cabins[cabin[0]]\n",
    "        else:\n",
    "            cabin_letter = cabins_by_class[df.iloc[n]['Pclass']][0][0] # Fill with the most common by 'Pclass'\n",
    "            df.at[n,'Cabin'] = cabins[cabin_letter]\n",
    "    \n",
    "    df['Embarked'].fillna('S', inplace=True) # Fill with the most common\n",
    "    df['Embarked'] = df['Embarked'].map(ports)\n",
    "    \n",
    "    for n,fare in enumerate(df['Fare']):\n",
    "        if pd.isna(fare):\n",
    "            df.at[n,'Fare'] = fare_by_class[df.iloc[n]['Pclass']]\n",
    "    \n",
    "    for n,name in enumerate(df['Name']):\n",
    "        if name.split(',')[1].split('.')[0].strip(' ') in names:\n",
    "            df.at[n,'Name'] = names[name.split(',')[1].split('.')[0].strip(' ')]\n",
    "        else: # Just in case\n",
    "            df.at[n,'Name'] = names['*Rare*']\n",
    "    \n",
    "    df = df.drop(['PassengerId','Ticket','SibSp','Parch'], axis=1)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Family</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass Name  Sex   Age     Fare Cabin  Embarked  Family\n",
       "0         0       3    1    0  22.0   7.2500     6         2       1\n",
       "1         1       1    2    1  38.0  71.2833     0         0       1\n",
       "2         1       3    3    1  26.0   7.9250     6         2       0\n",
       "3         1       1    2    1  35.0  53.1000     0         2       1\n",
       "4         0       3    1    0  35.0   8.0500     6         2       0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df = preprocess(main_df)\n",
    "main_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 9 columns):\n",
      "Survived    891 non-null int64\n",
      "Pclass      891 non-null int64\n",
      "Name        891 non-null object\n",
      "Sex         891 non-null int64\n",
      "Age         891 non-null float64\n",
      "Fare        891 non-null float64\n",
      "Cabin       891 non-null object\n",
      "Embarked    891 non-null int64\n",
      "Family      891 non-null int64\n",
      "dtypes: float64(2), int64(5), object(2)\n",
      "memory usage: 62.7+ KB\n"
     ]
    }
   ],
   "source": [
    "main_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train/Test Split"
   ]
  },
  {
   "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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Family</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6.9500</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>59.4000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass Name  Sex   Age      Fare Cabin  Embarked  Family\n",
       "825       3    1    0  31.0    6.9500     6         1       0\n",
       "8         3    2    1  27.0   11.1333     6         2       2\n",
       "689       1    3    1  15.0  211.3375     5         2       1\n",
       "513       1    2    1  54.0   59.4000     0         0       1\n",
       "729       3    3    1  25.0    7.9250     6         2       1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = main_df.drop(['Survived'], axis=1)\n",
    "y = main_df['Survived']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=101)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "825    0\n",
       "8      1\n",
       "689    1\n",
       "513    1\n",
       "729    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Balancing Data (Over-sampling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3782771535580524"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = SMOTE(random_state=101)\n",
    "X_train, y_train = sm.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "996"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV score: 0.7981927710843374\n"
     ]
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier(random_state=101)\n",
    "\n",
    "print('CV score:', cross_val_score(dt, X_train, y_train, cv = 3).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV score: 0.8283132530120483\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=100, random_state=101)\n",
    "\n",
    "print('CV score:', cross_val_score(rf, X_train, y_train, cv = 3).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV score: 0.7730923694779116\n"
     ]
    }
   ],
   "source": [
    "svm = SVC(gamma='auto',random_state=101)\n",
    "\n",
    "print('CV score:', cross_val_score(svm, X_train, y_train, cv = 3).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV score: 0.8072289156626505\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1, leaf_size=1000, p=1)\n",
    "\n",
    "print('CV score:', cross_val_score(knn, X_train, y_train, cv = 3).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV score: 0.7901606425702812\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(random_state=101, solver='lbfgs', max_iter=500) #Parameters added to disable warnings\n",
    "\n",
    "print('CV score:', cross_val_score(lr, X_train, y_train, cv = 3).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameters tuning (Random Forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'auto',\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': 101,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_grid = RandomForestClassifier(random_state=101) # creates a new estimator\n",
    "\n",
    "# Create the parameter grid based on the results of random search \n",
    "param_grid = {'criterion' : ['gini', 'entropy'],\n",
    "              'min_samples_leaf': [1, 2, 3],\n",
    "              'min_samples_split': [2, 3, 5, 10],\n",
    "              'n_estimators': [100, 300, 500]}\n",
    "\n",
    "# Instantiate the grid search model (n_jobs = -1 sets to use the max number of processors)\n",
    "grid_search = GridSearchCV(estimator=rf_grid, param_grid=param_grid, cv=3, scoring='precision', n_jobs=-1, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 72 candidates, totalling 216 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    7.5s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:   30.5s\n",
      "[Parallel(n_jobs=-1)]: Done 216 out of 216 | elapsed:   43.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy',\n",
       " 'min_samples_leaf': 3,\n",
       " 'min_samples_split': 10,\n",
       " 'n_estimators': 500}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the grid search to the data and show the best parameters\n",
    "grid_search.fit(X_train, y_train)\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=3, min_samples_split=10,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=None,\n",
       "            oob_score=False, random_state=101, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_grid = grid_search.best_estimator_\n",
    "best_grid # Note that it's a classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV score: 0.8413654618473895\n",
      "RF Test Score: 0.8\n"
     ]
    }
   ],
   "source": [
    "print('CV score:', cross_val_score(best_grid, X_train, y_train, cv=3).mean())\n",
    "print('RF Test Score:', best_grid.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.09612896, 0.21377634, 0.22423742, 0.14147417, 0.16879934,\n",
       "       0.05283777, 0.03223055, 0.07051545])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_grid.feature_importances_"
   ]
  },
  {
   "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>importance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>0.224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>0.214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>0.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>0.071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          importance\n",
       "feature             \n",
       "Sex            0.224\n",
       "Name           0.214\n",
       "Fare           0.169\n",
       "Age            0.141\n",
       "Pclass         0.096\n",
       "Family         0.071\n",
       "Cabin          0.053\n",
       "Embarked       0.032"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances = pd.DataFrame({'feature':X.columns,'importance':np.round(best_grid.feature_importances_,3)})\n",
    "importances = importances.sort_values('importance',ascending=False).set_index('feature')\n",
    "importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances.plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Family</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass Name  Sex   Age     Fare Cabin  Embarked  Family\n",
       "0       3    1    0  34.5   7.8292     6         1       0\n",
       "1       3    2    1  47.0   7.0000     6         2       1\n",
       "2       2    1    0  62.0   9.6875     6         1       0\n",
       "3       3    1    0  27.0   8.6625     6         2       0\n",
       "4       3    2    1  22.0  12.2875     6         2       2"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission_df = preprocess(final_df)\n",
    "submission_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 8 columns):\n",
      "Pclass      418 non-null int64\n",
      "Name        418 non-null object\n",
      "Sex         418 non-null int64\n",
      "Age         418 non-null float64\n",
      "Fare        418 non-null float64\n",
      "Cabin       418 non-null object\n",
      "Embarked    418 non-null int64\n",
      "Family      418 non-null int64\n",
      "dtypes: float64(2), int64(4), object(2)\n",
      "memory usage: 26.2+ KB\n"
     ]
    }
   ],
   "source": [
    "submission_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1,\n",
       "       1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n",
       "       1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0,\n",
       "       1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1,\n",
       "       0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1,\n",
       "       0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n",
       "       1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_prediction = best_grid.predict(submission_df)\n",
    "final_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>1</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         1"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final = pd.concat([final_df['PassengerId'],pd.DataFrame(final_prediction, columns=['Survived'])], axis=1)\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing on CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('submission.csv','w') as csv_file:\n",
    "    csv_file.write('PassengerId,Survived\\n')\n",
    "    \n",
    "    for _,row in final.iterrows():\n",
    "        text = str(row['PassengerId'])+','+str(row['Survived'])\n",
    "        csv_file.write(text+'\\n')"
   ]
  }
 ],
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