{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaggle Competition | Titanic: Machine Learning From a Disaster\n",
    "\n",
    "![Titanic](header.png)\n",
    "\n",
    "### Competition Description\n",
    "> The sinking of the RMS Titanic is one of the most infamous shipwrecks in history.  On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.\n",
    "\n",
    "> One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.\n",
    "\n",
    "> In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.\n",
    "\n",
    "> ** Goal **\n",
    "\n",
    ">It is your job to predict if a passenger survived the sinking of the Titanic or not. \n",
    "For each PassengerId in the test set, you must predict a 0 or 1 value for the Survived variable.\n",
    "\n",
    "> ** Metric**\n",
    "\n",
    ">Your score is the percentage of passengers you correctly predict. This is known simply as \"accuracy”.\n",
    "\n",
    "\n",
    " \n",
    "### Steps taken in this Notebook\n",
    "1. Data Wrangling\n",
    "    * Loading the datasets with Pandas\n",
    "    * Visualizing missing data with MissingNo\n",
    "    * Checking correlation between survival and individual factors  \n",
    "<br> \n",
    "2. Feature Engineering\n",
    "    * Feature engineering of new columns\n",
    "        * Passenger Title\n",
    "        * Family Size\n",
    "        * Travelling Alone?  \n",
    "<br> \n",
    "3. Imputing Missing Data Values\n",
    "    * Randomizing the two missing Embarked values\n",
    "    * Imputing the missing fare value using the average fare paid by each Pclass\n",
    "    * Testing various ML Regression algorithms to predict age\n",
    "        * Linear Regression\n",
    "        * Bayesian Ridge\n",
    "        * Multilayer Perceptron (MLP)\n",
    "        * Decision Tree\n",
    "        * Bagging\n",
    "    * Using Multilayer Perceptron (MLP) to predict the missing age values\n",
    "<br> <br>\n",
    "4. Predict Survival\n",
    "    * Test various ML Classifier algorithms to predict Survival\n",
    "        * Logistic Regression\n",
    "        * Support Vector \n",
    "        * Decision Tree \n",
    "        * Random Forest \n",
    "        * Adaptive Boosting \n",
    "        * Multilayer Perceptron (MLP) \n",
    "        * K-Nearest Neighbours (KNN) \n",
    "\n",
    "    * Using MLP to predict Survival\n",
    "    * Formatting output for submission\n",
    "\n",
    "<br> \n",
    "\n",
    "### Required Libraries\n",
    "* Numpy\n",
    "* Pandas\n",
    "* Missingno\n",
    "* Seaborn\n",
    "* Matplotlib\n",
    "* Random\n",
    "* SciKit-Learn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading the datasets with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 705,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import ks_2samp, randint\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.neural_network import MLPRegressor, MLPClassifier\n",
    "from sklearn.linear_model import LinearRegression, BayesianRidge, LogisticRegression\n",
    "from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingRegressor, RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, accuracy_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "train_dat = pd.read_csv('train.csv')\n",
    "test_dat = pd.read_csv('test.csv')\n",
    "all_dat = [train_dat, test_dat]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Training Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 651,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
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     "output_type": "display_data"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      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  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(train_dat.head(3))\n",
    "display(train_dat.info())\n",
    "display(train_dat.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Testing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 652,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <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",
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       "    <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",
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       "    <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                              Name     Sex   Age  SibSp  \\\n",
       "0          892       3                  Kelly, Mr. James    male  34.5      0   \n",
       "1          893       3  Wilkes, Mrs. James (Ellen Needs)  female  47.0      1   \n",
       "2          894       2         Myles, Mr. Thomas Francis    male  62.0      0   \n",
       "\n",
       "   Parch  Ticket    Fare Cabin Embarked  \n",
       "0      0  330911  7.8292   NaN        Q  \n",
       "1      0  363272  7.0000   NaN        S  \n",
       "2      0  240276  9.6875   NaN        Q  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "    <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",
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       "      <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"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(test_dat.head(3))\n",
    "display(test_dat.info())\n",
    "display(test_dat.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's examine the data briefly:\n",
    "The Name, Sex, Ticket, Cabin, and Embarked are listed as strings\n",
    "The remaining lists (PassengerId, Age, SibSp, Parch, and Fare) are reported as integers, but some of them represent categories of values. These nominal values must be converted to dummy variables when creating models\n",
    "\n",
    "Many of the columns have some missing values, and are difficult to conceptualize when reported in a table form (using `info()`). We will use the `missingno` package to make it easier to visualize where the missing values lie, and what proportions are the data are missing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing missing data with missingno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10dafd31b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10dabdd2c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TwmMAAAAAACaExwAAAAAATAiPAQAAAACYEB4DAAAAADAhPAYAAAAAYEJ4DAAA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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10daa9e5240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SJEmDafPNN+/3EGa4ww8/vN9DkAaGmceSJEmSJEmSpB4GjyVJkiRJ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\n8FiSJEmSJEmStA3DY0mSJEmSJEnSNn4LHuGGMpxNpq0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10daaa98198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import missingno as msno\n",
    "display(msno.bar(train_dat))\n",
    "display(msno.bar(test_dat))\n",
    "train_dat.drop(['Cabin', 'Ticket'], axis=1, inplace=True)\n",
    "test_dat.drop(['Cabin', 'Ticket'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the Cabin column, we only have approximately 20% of values present in both datasets. There are too many missing values to attempt to impute the values. As such, we will drop it from the analysis\n",
    "\n",
    "The Age column is missing approximately 20% of the values, but there are enough values present to impute the missing values. We will create an entire machine learning regression model for the missing age values later on\n",
    "\n",
    "The training dataset contains two missing values for the Embarked data. Since there are only three possible options, we will randomly select values to fill the missing values\n",
    "\n",
    "The test dataset contains one missing value for the Fare, which we will impute later based on the Pclass\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking correlation between survival and categorical factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 654,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.096067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.036847 -0.057527 -0.001652   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629   \n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.369226  0.083081  0.018443   \n",
       "Age             0.036847 -0.077221 -0.369226  1.000000 -0.308247 -0.189119   \n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.308247  1.000000  0.414838   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.189119  0.414838  1.000000   \n",
       "Fare            0.012658  0.257307 -0.549500  0.096067  0.159651  0.216225   \n",
       "\n",
       "                 Fare  \n",
       "PassengerId  0.012658  \n",
       "Survived     0.257307  \n",
       "Pclass      -0.549500  \n",
       "Age          0.096067  \n",
       "SibSp        0.159651  \n",
       "Parch        0.216225  \n",
       "Fare         1.000000  "
      ]
     },
     "execution_count": 654,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dat.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 655,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Pclass          \n",
       "1       0.629630\n",
       "2       0.472826\n",
       "3       0.242363"
      ]
     },
     "execution_count": 655,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dat[['Pclass', 'Survived']].groupby('Pclass').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 656,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Sex             \n",
       "female  0.742038\n",
       "male    0.188908"
      ]
     },
     "execution_count": 656,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dat[['Sex', 'Survived']].groupby('Sex').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 657,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ks_2sampResult(statistic=0.0887768379960963, pvalue=0.12555581240058969)"
      ]
     },
     "execution_count": 657,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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VE5V9ME8+M5J+2R2N1jA2uEEZB1XB2NjAwP/ClVjY/OmNy54uDoxoU58lu6O5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10daf476860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_survived = train_dat.loc[train_dat['Survived'] == 1]['Age'].dropna()\n",
    "age_dead = train_dat.loc[train_dat['Survived'] == 0]['Age'].dropna()\n",
    "\n",
    "fig = sns.kdeplot(age_survived, label='Survived', clip=[0,80])\n",
    "fig.set(xlim=(0, 80))\n",
    "sns.kdeplot(age_dead, label='Did Not Survive')\n",
    "\n",
    "ks_2samp(age_survived, age_dead)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sibsp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 658,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived\n",
       "Parch          \n",
       "0      0.343658\n",
       "1      0.550847\n",
       "2      0.500000\n",
       "3      0.600000\n",
       "4      0.000000\n",
       "5      0.200000\n",
       "6      0.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qDAVJUmMoSJKaXkMhyeok25PsSHLViNe/P8knu9e/mOSsPuuRJB1ab6GQZBmwHngZcC5w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\nzOpE+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dafd8c7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(train_dat['SibSp'], train_dat['Survived'])\n",
    "display(train_dat[['Parch', 'Survived']].groupby('Parch').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 659,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived\n",
       "Parch          \n",
       "0      0.343658\n",
       "1      0.550847\n",
       "2      0.500000\n",
       "3      0.600000\n",
       "4      0.000000\n",
       "5      0.200000\n",
       "6      0.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7kudfvN97gKU/aF7c/EtvNfDAf12EHb37M8v7z/5hPH+cQX2WwiSwftr2OuCew4yZTLIK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dab682160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(train_dat['Parch'], train_dat['Survived'])\n",
    "display(train_dat[['Parch', 'Survived']].groupby('Parch').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 660,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ks_2sampResult(statistic=0.30236794171220405, pvalue=1.8678359776799793e-17)"
      ]
     },
     "execution_count": 660,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fZIzht5WIiWByl2YgIYTTGHwJ4PBWaKjseu3f7nL1gIhEmRAmhHAagy8B5FvG6o842/b7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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dafde01d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fare_survived = train_dat.loc[train_dat['Survived'] == 1]['Fare'].dropna()\n",
    "fare_dead = train_dat.loc[train_dat['Survived'] == 0]['Fare'].dropna()\n",
    "\n",
    "fig = sns.kdeplot(fare_survived, label='Survived', clip=[0,80])\n",
    "fig.set(xlim=(0, 80))\n",
    "sns.kdeplot(fare_dead, label='Did Not Survive')\n",
    "ks_2samp(fare_survived, fare_dead)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 661,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10dabfba860>"
      ]
     },
     "execution_count": 661,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/lHMvqqr/uJLzAcwrcQ2wOt3fWju5tfaTSf4lyW9NPRAA4hpgFvxtkoUkqapX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\nEdcAANDJ/wf7t1vIbHYcnwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10daad34c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "sns.boxplot(train_dat['Pclass'], train_dat['Fare'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract Passenger Title\n",
    "We will attempt to extract titles from the name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 662,
   "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>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt.</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col.</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess.</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don.</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr.</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer.</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady.</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major.</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master.</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss.</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle.</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme.</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr.</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs.</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms.</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev.</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir.</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex        female  male\n",
       "Title                  \n",
       "Capt.           0     1\n",
       "Col.            0     2\n",
       "Countess.       1     0\n",
       "Don.            0     1\n",
       "Dr.             1     6\n",
       "Jonkheer.       0     1\n",
       "Lady.           1     0\n",
       "Major.          0     2\n",
       "Master.         0    40\n",
       "Miss.         182     0\n",
       "Mlle.           2     0\n",
       "Mme.            1     0\n",
       "Mr.             0   517\n",
       "Mrs.          125     0\n",
       "Ms.             1     0\n",
       "Rev.            0     6\n",
       "Sir.            0     1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Col.</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dona.</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr.</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master.</th>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss.</th>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr.</th>\n",
       "      <td>0</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs.</th>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms.</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev.</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex      female  male\n",
       "Title                \n",
       "Col.          0     2\n",
       "Dona.         1     0\n",
       "Dr.           0     1\n",
       "Master.       0    21\n",
       "Miss.        78     0\n",
       "Mr.           0   240\n",
       "Mrs.         72     0\n",
       "Ms.           1     0\n",
       "Rev.          0     2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import re as re\n",
    "def title_extract_function(string):\n",
    "    title = re.search('([A-Za-z]+)\\.', string)[0]\n",
    "    return title\n",
    "\n",
    "for df in all_dat:\n",
    "    df['Title'] = df['Name'].apply(title_extract_function)\n",
    "\n",
    "display(pd.crosstab(all_dat[0]['Title'], all_dat[0]['Sex']))\n",
    "display(pd.crosstab(all_dat[1]['Title'], all_dat[1]['Sex']))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some titles are held by a select few, and will be grouped into an \"Other\" category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 663,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Master.</th>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss.</th>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr.</th>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs.</th>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Survived\n",
       "Title            \n",
       "Master.  0.575000\n",
       "Miss.    0.702703\n",
       "Mr.      0.156673\n",
       "Mrs.     0.793651\n",
       "Other    0.347826"
      ]
     },
     "execution_count": 663,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for df in all_dat:\n",
    "    df['Title'] = df['Title'].replace(['Mlle.', \"Ms.\"], 'Miss.')\n",
    "    df['Title'] = df['Title'].replace(['Mme.'], 'Mrs.')\n",
    "    df['Title'] = df['Title'].replace(['Capt.','Col.','Countess.','Don.', 'Dona.','Dr.','Jonkheer.','Lady.', 'Major.', 'Rev.',\n",
    "                                       'Sir.'],'Other')\n",
    "\n",
    "train_dat[['Title', 'Survived']].groupby('Title').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Family Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family Size</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived\n",
       "Family Size          \n",
       "1            0.303538\n",
       "2            0.552795\n",
       "3            0.578431\n",
       "4            0.724138\n",
       "5            0.200000\n",
       "6            0.136364\n",
       "7            0.333333\n",
       "8            0.000000\n",
       "11           0.000000"
      ]
     },
     "execution_count": 664,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for df in all_dat:\n",
    "    df['Family Size'] = df['Parch'] + df['SibSp'] +1\n",
    "\n",
    "train_dat[['Family Size', 'Survived']].groupby('Family Size').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Travelling Alone?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 665,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alone</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.505650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived\n",
       "Alone          \n",
       "0      0.505650\n",
       "1      0.303538"
      ]
     },
     "execution_count": 665,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for df in all_dat:\n",
    "    df['Alone'] = 0\n",
    "    df.loc[df['Family Size'] == 1, 'Alone'] = 1\n",
    "train_dat[['Alone', 'Survived']].groupby('Alone').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing Missing Data Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 666,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.556213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>0.337984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Survived\n",
       "Embarked          \n",
       "C         0.556213\n",
       "Q         0.389610\n",
       "S         0.337984"
      ]
     },
     "execution_count": 666,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.seed(1234)\n",
    "while train_dat['Embarked'].isna().sum() > 0:\n",
    "    train_dat['Embarked'].fillna(random.randint(1,3), limit=1, inplace=True)\n",
    "train_dat['Embarked'].replace({1:\"S\", 2:\"C\", 3:\"Q\"}, inplace=True)\n",
    "\n",
    "train_dat[['Embarked', 'Survived']].groupby('Embarked').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fare Class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to fill in the missing Fare value, we use the most correlated factor(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 667,
   "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>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alone</th>\n",
       "      <td>-0.271832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family Size</th>\n",
       "      <td>0.217138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.096067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>0.012658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Fare\n",
       "Pclass      -0.549500\n",
       "Alone       -0.271832\n",
       "Survived     0.257307\n",
       "Family Size  0.217138\n",
       "Parch        0.216225\n",
       "SibSp        0.159651\n",
       "Age          0.096067\n",
       "PassengerId  0.012658"
      ]
     },
     "execution_count": 667,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fare_corr = pd.DataFrame(train_dat.corr()['Fare'].drop(['Fare'],axis=0))\n",
    "Fare_corr.reindex(Fare_corr.Fare.abs().sort_values(inplace=False, ascending=False).index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most significant factor that is correlated with Fare is the **Pclass**. We will impute the missing fare value based average value of the passenger's class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 668,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+z3+HtNdcEUi7dx3wR0kOB0hy2C6OeQmwubsk9dlP7Uzy61W1pqouArYAxyQ5Hrivqj4P\nXAO8uve/QBqDKwJpN6rqziSfAr6X5EngFuDdTzvsLxl9Mtn9wO2MwgDwme7J4DAKyq3AhcA7kzwB\nzACf7P2PkMbgk8WS1DhPDUlS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXu/wD0Z0oR\nL5q+dwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10da23aa748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=train_dat['Pclass'], y=train_dat['Fare'])\n",
    "\n",
    "average_fare_by_class = test_dat.groupby(by=['Pclass'], as_index=False)['Fare'].mean()\n",
    "\n",
    "if (test_dat.loc[test_dat['Fare'].isnull(),'Pclass'] == 3).values[0] == True:\n",
    "    test_dat.loc[test_dat['Fare'].isnull(),'Fare'] = average_fare_by_class[average_fare_by_class['Pclass'] == 1]['Fare'][0]\n",
    "elif (test_dat.loc[test_dat['Fare'].isnull(),'Pclass'] == 2).values[0] == True:\n",
    "    test_dat.loc[test_dat['Fare'].isnull(),'Fare'] = average_fare_by_class[average_fare_by_class['Pclass'] == 2]['Fare'][0]\n",
    "else:\n",
    "    test_dat.loc[test_dat['Fare'].isnull(),'Fare'] = average_fare_by_class[average_fare_by_class['Pclass'] == 3]['Fare'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have filled in the missing fare value, we can cut the range of Fares into distinct categorical groups. An easy way to visualize the distribution is through a violin plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 669,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10da5694b00>]"
      ]
     },
     "execution_count": 669,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10da853c6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(train_dat['Fare'])\n",
    "max_fare = train_dat['Fare'].max()\n",
    "plt.plot([60, 60], [-1, 1], linewidth=2)\n",
    "plt.plot([110, 110], [-1, 1], linewidth=2)\n",
    "plt.plot([180, 180], [-1, 1], linewidth=2)\n",
    "plt.plot([max_fare, max_fare], [-1, 1], linewidth=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the plot to divide the Fares into four groups:\n",
    "1. Fare Type 1 = \\$0 - \\$60    \n",
    "2. Fare Type 2 = \\$61 - \\$110    \n",
    "3. Fare Type 3 = \\$111 - \\$180    \n",
    "4. Fare Type 4 = \\$181+\n",
    "\n",
    "Next, we need to test if the correlation between **Fare Type** and **Survival**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 670,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare Type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.343501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.616438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.793103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Survived\n",
       "Fare Type          \n",
       "1          0.343501\n",
       "2          0.616438\n",
       "3          0.793103\n",
       "4          0.700000"
      ]
     },
     "execution_count": 670,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for df in all_dat:\n",
    "    df['Fare Type'] = pd.cut(df['Fare'], [0, 60, 110,180, 1000], labels=['1', '2', '3', \"4\"])\n",
    "\n",
    "train_dat[['Fare Type', 'Survived']].groupby('Fare Type').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting the missing age values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 671,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>Family Size</th>\n",
       "      <th>Alone</th>\n",
       "      <th>Fare Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>Mr.</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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",
       "\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",
       "\n",
       "   Parch     Fare Embarked Title  Family Size  Alone Fare Type  \n",
       "0      0   7.2500        S   Mr.            2      0         1  \n",
       "1      0  71.2833        C  Mrs.            2      0         2  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
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       "      <td>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>7.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                              Name     Sex   Age  SibSp  \\\n",
       "0          892       3                  Kelly, Mr. James    male  34.5      0   \n",
       "1          893       3  Wilkes, Mrs. James (Ellen Needs)  female  47.0      1   \n",
       "\n",
       "   Parch    Fare Embarked Title  Family Size  Alone Fare Type  \n",
       "0      0  7.8292        Q   Mr.            1      1         1  \n",
       "1      0  7.0000        S  Mrs.            2      0         1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(all_dat[0].head(2))\n",
    "display(all_dat[1].head(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert Categorical Data into Dummy Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 672,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "categorical_df = []\n",
    "for df in all_dat:\n",
    "    sex = pd.get_dummies(df['Sex'],drop_first=True)\n",
    "    embark = pd.get_dummies(df['Embarked'],drop_first=True)\n",
    "    title = pd.get_dummies(df['Title'], drop_first=True)\n",
    "    fare = pd.get_dummies(df['Fare Type'], drop_first=True)\n",
    "    dummies = pd.concat([sex, embark, title, fare], axis=1)\n",
    "    df2 = df.drop(['Sex','Embarked','Name','Fare','Fare Type','Title'], axis=1, inplace=True)\n",
    "    df3 = pd.concat([df, sex, embark, title, fare], axis=1)\n",
    "    categorical_df.append(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Separate the missing data from the complete data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 695,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "missing_age = categorical_df[0][pd.isnull(categorical_df[0]).any(axis=1)].drop(['PassengerId'], axis=1)\n",
    "complete_age = categorical_df[0].dropna().drop(['PassengerId'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find the optimal machine learning regression algorithm\n",
    "Since we are predicting the numeric values of age, we calculated the accuracy of the model using the RMSE (root-mean-square error) and the MAE (mean absolute error).\n",
    "\n",
    "The ML regression algorithms tested are:\n",
    "    1. Linear Regression\n",
    "    2. Bayesian Ridge\n",
    "    3. Multilayer Perceptron (MLP)\n",
    "    4. Decision Tree\n",
    "    5. Bagging Regressor\n",
    "\n",
    "We repeated the testing of each algorithm 1,000 times with differen splits for the training and testing splits. We used the resulting averages of RMSE and MAE to determine the best algorithm to use to predict the missing age values. **The resulting algorithm with the lowest RMSE/MAE values will be used**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 698,
   "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>Linear Reg</th>\n",
       "      <th>Bayesian Ridge</th>\n",
       "      <th>MLP</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <th>Bagging Reg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>1.190776e+24</td>\n",
       "      <td>126.890969</td>\n",
       "      <td>128.980617</td>\n",
       "      <td>163.670386</td>\n",
       "      <td>139.088605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>1.289010e+10</td>\n",
       "      <td>8.889148</td>\n",
       "      <td>8.681706</td>\n",
       "      <td>9.596950</td>\n",
       "      <td>8.988091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Linear Reg  Bayesian Ridge         MLP  Decision Tree  Bagging Reg\n",
       "RMSE  1.190776e+24      126.890969  128.980617     163.670386   139.088605\n",
       "MAE   1.289010e+10        8.889148    8.681706       9.596950     8.988091"
      ]
     },
     "execution_count": 698,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_RMSE = pd.DataFrame(columns=[\"LM RMSE\", \"BRR RMSE\", \"NNR RMSE\", \"DTR RMSE\", \"BR RMSE\"])\n",
    "total_MAE = pd.DataFrame(columns=[\"LM MAE\", \"BRR MAE\", \"NNR MAE\", \"DTR MAE\", \"BR MAE\"])\n",
    "\n",
    "for i in range(0, 100):\n",
    "    x = complete_age.drop('Age',axis=1)\n",
    "    y = complete_age['Age']\n",
    "    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.30)\n",
    "    scaler = StandardScaler()  \n",
    "    scaler.fit(x_train)\n",
    "    x_train = scaler.transform(x_train)  \n",
    "    x_test = scaler.transform(x_test)\n",
    "    \n",
    "    lm = LinearRegression()\n",
    "    lm.fit(x_train, y_train)\n",
    "    lm_pred = lm.predict(x_test)\n",
    "\n",
    "    brr = BayesianRidge()\n",
    "    brr.fit(x_train, y_train)\n",
    "    brr_pred = brr.predict(x_test)\n",
    "\n",
    "    nnr = MLPRegressor(hidden_layer_sizes=(100,), activation='relu', solver='adam',    alpha=0.001,batch_size='auto',\n",
    "                   learning_rate='constant', learning_rate_init=0.01, power_t=0.5, max_iter=1000, shuffle=True,\n",
    "                   random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9,\n",
    "                   nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999,\n",
    "                   epsilon=1e-08)\n",
    "    nnr.fit(x_train, y_train)\n",
    "    nnr_pred = nnr.predict(x_test)\n",
    "\n",
    "    dtr = DecisionTreeRegressor()\n",
    "    dtr.fit(x_train,y_train)\n",
    "    dtr_pred = dtr.predict(x_test)\n",
    "\n",
    "    br = BaggingRegressor()\n",
    "    br.fit(x_train, y_train)\n",
    "    br_pred = br.predict(x_test)\n",
    "\n",
    "    RMSEs = [mean_squared_error(y_test, lm_pred), mean_squared_error(y_test, brr_pred), mean_squared_error(y_test, nnr_pred),\n",
    "            mean_squared_error(y_test, dtr_pred), mean_squared_error(y_test, br_pred)]\n",
    "    total_RMSE.loc[i] = RMSEs\n",
    "    MAEs = [mean_absolute_error(y_test, lm_pred), mean_absolute_error(y_test, brr_pred), mean_absolute_error(y_test, nnr_pred),\n",
    "            mean_absolute_error(y_test, dtr_pred), mean_absolute_error(y_test, br_pred)]\n",
    "    total_MAE.loc[i] = MAEs\n",
    "\n",
    "average_RMSE = total_RMSE.mean(axis=0).values\n",
    "average_MAE = total_MAE.mean(axis=0).values\n",
    "\n",
    "Age_ML_Summary = pd.DataFrame(average_RMSE, columns=[\"RMSE\"], index=['Linear Reg', \"Bayesian Ridge\", 'MLP', \n",
    "                                                                     'Decision Tree', 'Bagging Reg'])\n",
    "Age_ML_Summary['MAE'] = average_MAE\n",
    "Age_ML_Summary.transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Multilayer Perceptron algorithm produced the lowest RMSE and MAE values, and will be used to predict the missing age values. But first, we need to tune the hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Multilayer Perceptron (MLP) to predict the missing age values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predict missing age values using MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 700,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict_age(df):\n",
    "    if (df['PassengerId'].max()== 891):\n",
    "        df = df.drop('Survived', axis=1)\n",
    "    missing_age = df[pd.isnull(df).any(axis=1)]\n",
    "    complete_age = df.dropna()\n",
    "    \n",
    "    x_train = complete_age.drop(['Age', 'PassengerId'],axis=1)\n",
    "    y_train = complete_age['Age']\n",
    "    x_test = missing_age.drop(['Age', 'PassengerId'], axis=1)\n",
    "    \n",
    "    scaler = StandardScaler()  \n",
    "    scaler.fit(x_train)\n",
    "    x_train = scaler.transform(x_train)  \n",
    "    x_test = scaler.transform(x_test)\n",
    "    \n",
    "    nnr = MLPRegressor(hidden_layer_sizes=(100,), activation='relu', solver='adam', alpha=0.001,batch_size='auto',\n",
    "                   learning_rate='constant', learning_rate_init=0.01, power_t=0.5, max_iter=1000, shuffle=True,\n",
    "                   random_state=1234, tol=0.0001, verbose=False, warm_start=False, momentum=0.9,\n",
    "                   nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999,\n",
    "                   epsilon=1e-08)\n",
    "    nnr.fit(x_train, y_train)\n",
    "    predicted_age = nnr.predict(x_test)\n",
    "    \n",
    "    missing_age = df[pd.isnull(df).any(axis=1)].drop('Age',axis=1)\n",
    "    age_df = missing_age[['PassengerId']]\n",
    "    age_df = age_df.assign(Age = predicted_age)\n",
    "    age_df.sort_values(by=['PassengerId'])\n",
    "    return age_df\n",
    "\n",
    "complete_data = []\n",
    "for df in categorical_df:\n",
    "    pred_age = predict_age(df)\n",
    "    complete_df = df.combine_first(pred_age)\n",
    "    complete_data.append(complete_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict Survival"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding the optimal machine learning classifier algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 704,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = complete_age.drop('Survived',axis=1)\n",
    "y = complete_age['Survived']\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.30)\n",
    "scaler = StandardScaler()  \n",
    "scaler.fit(x_train)\n",
    "x_train = scaler.transform(x_train)  \n",
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Randomized Search of Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 711,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "244.51487812747249\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Logistic Regression</th>\n",
       "      <th>SVC</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <th>Random Forest</th>\n",
       "      <th>Adaboost</th>\n",
       "      <th>MLP</th>\n",
       "      <th>Kneighbours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.832558</td>\n",
       "      <td>0.824186</td>\n",
       "      <td>0.79907</td>\n",
       "      <td>0.817674</td>\n",
       "      <td>0.803721</td>\n",
       "      <td>0.826047</td>\n",
       "      <td>0.791628</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Logistic Regression       SVC  Decision Tree  Random Forest  \\\n",
       "Accuracy             0.832558  0.824186        0.79907       0.817674   \n",
       "\n",
       "          Adaboost       MLP  Kneighbours  \n",
       "Accuracy  0.803721  0.826047     0.791628  "
      ]
     },
     "execution_count": 711,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import timeit\n",
    "#start = timeit.default_timer()\n",
    "\n",
    "total_accuracy = pd.DataFrame(columns=[\"Logistic Regression\", \"SVC\", \"Decision Tree\", \n",
    "                                      \"Random Forest\", \"Adaboost\", \"MLP\", \"Kneighbours\"])\n",
    "\n",
    "for i in range(0, 5):\n",
    "    x = complete_age.drop('Survived',axis=1)\n",
    "    y = complete_age['Survived']\n",
    "    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.30)\n",
    "    scaler = StandardScaler()  \n",
    "    scaler.fit(x_train)\n",
    "    x_train = scaler.transform(x_train)  \n",
    "    x_test = scaler.transform(x_test)\n",
    "    \n",
    "    params = {'C': np.arange(0.001, 100, 0.5)}\n",
    "    logreg_grid = RandomizedSearchCV(LogisticRegression(), params)\n",
    "    logreg_grid.fit(x_train, y_train)\n",
    "    logreg_pred = logreg_grid.predict(x_test)\n",
    "    \n",
    "    params = {'C': np.arange(0.1, 5, 0.1), 'gamma': np.arange(0.00001, 1, 0.05), 'kernel': ['rbf', 'linear', 'sigmoid']}\n",
    "    svc_grid = RandomizedSearchCV(SVC(), params)\n",
    "    svc_grid.fit(x_train, y_train)\n",
    "    svc_predictions = svc_grid.predict(x_test)\n",
    "    \n",
    "    params = {'criterion': ['gini', 'entropy'], 'max_depth': range(1,50)}\n",
    "    decisiontree_grid = RandomizedSearchCV(DecisionTreeClassifier(), params)\n",
    "    decisiontree_grid.fit(x_train, y_train)\n",
    "    DT_predictions = decisiontree_grid.predict(x_test)\n",
    "    \n",
    "    params = {'criterion': ['gini', 'entropy'], 'n_estimators': range(5, 100), 'max_depth': range(1,50)}\n",
    "    rf_grid = RandomizedSearchCV(RandomForestClassifier(), params)\n",
    "    rf_grid.fit(x_train, y_train)\n",
    "    rf_predictions = rf_grid.predict(x_test)\n",
    "    \n",
    "    params = {'learning_rate': np.arange(0.01, 1.1, 0.1), 'n_estimators': np.arange(50,200, 25)}\n",
    "    adaboost_grid = RandomizedSearchCV(AdaBoostClassifier(),params)\n",
    "    adaboost_grid.fit(x_train, y_train)\n",
    "    adaboost_predictions = adaboost_grid.predict(x_test)\n",
    "    \n",
    "    params = {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'],\n",
    "         'alpha': [0.000001, 0.00001, 0.0001, 0.001, 0.01, 0.1]}\n",
    "    mlp_grid = RandomizedSearchCV(MLPClassifier(max_iter=1000),params)\n",
    "    mlp_grid.fit(x_train, y_train)\n",
    "    mlp_predictions = mlp_grid.predict(x_test)\n",
    "\n",
    "    params = {'n_neighbors': np.arange(5,50, 2), 'weights': ['uniform', 'distance']}\n",
    "    KN_grid = RandomizedSearchCV(KNeighborsClassifier(),params)\n",
    "    KN_grid.fit(x_train, y_train)\n",
    "    KN_predictions = KN_grid.predict(x_test)\n",
    "    \n",
    "    accuracies = [accuracy_score(y_test, logreg_pred),accuracy_score(y_test, svc_predictions), \n",
    "                  accuracy_score(y_test, DT_predictions), accuracy_score(y_test, rf_predictions),\n",
    "                  accuracy_score(y_test, adaboost_predictions), accuracy_score(y_test, mlp_predictions),\n",
    "                  accuracy_score(y_test, KN_predictions)]\n",
    "    \n",
    "    total_accuracy.loc[i] = accuracies\n",
    "\n",
    "average_accuracy = pd.DataFrame(total_accuracy.mean(axis=0), columns=['Accuracy'])\n",
    "average_accuracy.transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the Logistic Regression algorithm resulted in the highest average accuracy over 100 runs, we will proceed with an MLP classifer anyways ¯\\\\\\_(ツ)_/¯"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using an MLP Classifier for final survival predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 720,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train = complete_data[0].drop(['Survived', 'PassengerId'], axis=1)\n",
    "y_train = complete_data[0]['Survived']\n",
    "x_test = complete_data[1].drop(['PassengerId'], axis=1)\n",
    "\n",
    "scaler = StandardScaler()  \n",
    "scaler.fit(x_train)\n",
    "x_train = scaler.transform(x_train)  \n",
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 763,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mlp = MLPClassifier(max_iter=2000, random_state=1234)\n",
    "mlp.fit(x_train, y_train)\n",
    "final_mlp = mlp.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Formatting output for submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 764,
   "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"
      ]
     },
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "      <td>267</td>\n",
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       "          PassengerId\n",
       "Survived             \n",
       "0                 267\n",
       "1                 151"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_submission = complete_data[1].copy()\n",
    "final_submission['Survived'] = final_mlp\n",
    "submission_df = pd.DataFrame()\n",
    "submission_df[['PassengerId', 'Survived']] = final_submission[['PassengerId', 'Survived']]\n",
    "display(submission_df.head())\n",
    "display(submission_df.groupby('Survived').count())\n",
    "\n",
    "submission_df.to_csv(\"Titanic Submission.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## **Final Score**: 0.78947 (Top 31%)"
   ]
  }
 ],
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