Titanic-Surviving-Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kaggle Competition | Titanic Machine Learning from Disaster\n",
    "\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 contest, 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",
    ">This Kaggle Getting Started Competition provides an ideal starting place for people who may not have a lot of experience in data science and machine learning.\"\n",
    "\n",
    "From the competition [homepage](http://www.kaggle.com/c/titanic-gettingStarted).\n",
    "\n",
    "\n",
    "### Goal for this Notebook:\n",
    "Show a simple example of an analysis of the Titanic disaster in Python. This is aimed for those looking to get into the field or those who are already in the field and looking to see an example of an analysis done with Python.\n",
    "\n",
    "#### This Notebook will show basic examples of: \n",
    "#### Data Handling\n",
    "*   Importing Data with Pandas\n",
    "*   Cleaning Data\n",
    "*   Exploring Data through Visualizations with Matplotlib and seaborn\n",
    "\n",
    "#### Data Analysis\n",
    "*    Supervised Machine learning Techniques:\n",
    "    +   K Nearest Neighbour \n",
    "    +   Gaussian Naive Bayes\n",
    "    +   Support Vector Machine (SVM)\n",
    "    +   Decision Tree Classifier\n",
    "    +   Plotting results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sc\n",
    "import sys\n",
    "import IPython\n",
    "from IPython import display \n",
    "import sklearn \n",
    "import random\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Visualization\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pylab as pylab\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Configure Visualization Defaults\n",
    "%matplotlib inline\n",
    "mpl.style.use('ggplot')\n",
    "sns.set_style('white')\n",
    "pylab.rcParams['figure.figsize'] = 12,8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the model. \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Common Model Helpers\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
    "from sklearn import feature_selection\n",
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import GridSearchCV, StratifiedKFold, StratifiedShuffleSplit, cross_val_score\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score,classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Describing and analysing the given data sets for better understanding of the data provided"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_raw = pd.read_csv(\"train.csv\")\n",
    "data_test = pd.read_csv(\"test.csv\")\n",
    "\n",
    "data_train = data_raw.copy(deep = True)\n",
    "# combining the two datasets for cleaning\n",
    "data_to_be_cleaned = [data_train, data_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n",
      "*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data_raw.info()\n",
    "print('*-'*25)\n",
    "data_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train columns with null values:\n",
      " PassengerId      0\n",
      "Survived         0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age            177\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             0\n",
      "Cabin          687\n",
      "Embarked         2\n",
      "dtype: int64\n",
      "*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-\n",
      "Test/Validation columns with null values:\n",
      " PassengerId      0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age             86\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             1\n",
      "Cabin          327\n",
      "Embarked         0\n",
      "dtype: int64\n",
      "*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-\n"
     ]
    }
   ],
   "source": [
    "print('Train columns with null values:\\n', data_train.isnull().sum())\n",
    "print(\"*-\"*25)\n",
    "\n",
    "print('Test/Validation columns with null values:\\n', data_test.isnull().sum())\n",
    "print(\"*-\"*25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
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       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ryerson, Miss. Susan Parker \"Suzette\"</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1601</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>644</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId    Survived      Pclass  \\\n",
       "count    891.000000  891.000000  891.000000   \n",
       "unique          NaN         NaN         NaN   \n",
       "top             NaN         NaN         NaN   \n",
       "freq            NaN         NaN         NaN   \n",
       "mean     446.000000    0.383838    2.308642   \n",
       "std      257.353842    0.486592    0.836071   \n",
       "min        1.000000    0.000000    1.000000   \n",
       "25%      223.500000    0.000000    2.000000   \n",
       "50%      446.000000    0.000000    3.000000   \n",
       "75%      668.500000    1.000000    3.000000   \n",
       "max      891.000000    1.000000    3.000000   \n",
       "\n",
       "                                         Name   Sex         Age       SibSp  \\\n",
       "count                                     891   891  714.000000  891.000000   \n",
       "unique                                    891     2         NaN         NaN   \n",
       "top     Ryerson, Miss. Susan Parker \"Suzette\"  male         NaN         NaN   \n",
       "freq                                        1   577         NaN         NaN   \n",
       "mean                                      NaN   NaN   29.699118    0.523008   \n",
       "std                                       NaN   NaN   14.526497    1.102743   \n",
       "min                                       NaN   NaN    0.420000    0.000000   \n",
       "25%                                       NaN   NaN   20.125000    0.000000   \n",
       "50%                                       NaN   NaN   28.000000    0.000000   \n",
       "75%                                       NaN   NaN   38.000000    1.000000   \n",
       "max                                       NaN   NaN   80.000000    8.000000   \n",
       "\n",
       "             Parch Ticket        Fare        Cabin Embarked  \n",
       "count   891.000000    891  891.000000          204      889  \n",
       "unique         NaN    681         NaN          147        3  \n",
       "top            NaN   1601         NaN  C23 C25 C27        S  \n",
       "freq           NaN      7         NaN            4      644  \n",
       "mean      0.381594    NaN   32.204208          NaN      NaN  \n",
       "std       0.806057    NaN   49.693429          NaN      NaN  \n",
       "min       0.000000    NaN    0.000000          NaN      NaN  \n",
       "25%       0.000000    NaN    7.910400          NaN      NaN  \n",
       "50%       0.000000    NaN   14.454200          NaN      NaN  \n",
       "75%       0.000000    NaN   31.000000          NaN      NaN  \n",
       "max       6.000000    NaN  512.329200          NaN      NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "###COMPLETING: complete or delete missing values in train and test/validation dataset\n",
    "for dataset in data_to_be_cleaned:    \n",
    "    #complete missing age with median\n",
    "    dataset['Age'].fillna(dataset['Age'].median(), inplace = True)\n",
    "\n",
    "    #complete embarked with mode\n",
    "    dataset['Embarked'].fillna(dataset['Embarked'].mode()[0], inplace = True)\n",
    "\n",
    "    #complete missing fare with median\n",
    "    dataset['Fare'].fillna(dataset['Fare'].median(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[     PassengerId  Survived  Pclass  \\\n",
       " 0              1         0       3   \n",
       " 1              2         1       1   \n",
       " 2              3         1       3   \n",
       " 3              4         1       1   \n",
       " 4              5         0       3   \n",
       " ..           ...       ...     ...   \n",
       " 886          887         0       2   \n",
       " 887          888         1       1   \n",
       " 888          889         0       3   \n",
       " 889          890         1       1   \n",
       " 890          891         0       3   \n",
       " \n",
       "                                                   Name     Sex   Age  SibSp  \\\n",
       " 0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       " 1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       " 2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       " 3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       " 4                             Allen, Mr. William Henry    male  35.0      0   \n",
       " ..                                                 ...     ...   ...    ...   \n",
       " 886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       " 887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       " 888           Johnston, Miss. Catherine Helen \"Carrie\"  female  28.0      1   \n",
       " 889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       " 890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       " \n",
       "      Parch            Ticket     Fare Cabin Embarked  \n",
       " 0        0         A/5 21171   7.2500   NaN        S  \n",
       " 1        0          PC 17599  71.2833   C85        C  \n",
       " 2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       " 3        0            113803  53.1000  C123        S  \n",
       " 4        0            373450   8.0500   NaN        S  \n",
       " ..     ...               ...      ...   ...      ...  \n",
       " 886      0            211536  13.0000   NaN        S  \n",
       " 887      0            112053  30.0000   B42        S  \n",
       " 888      2        W./C. 6607  23.4500   NaN        S  \n",
       " 889      0            111369  30.0000  C148        C  \n",
       " 890      0            370376   7.7500   NaN        Q  \n",
       " \n",
       " [891 rows x 12 columns],\n",
       "      PassengerId  Pclass                                          Name  \\\n",
       " 0            892       3                              Kelly, Mr. James   \n",
       " 1            893       3              Wilkes, Mrs. James (Ellen Needs)   \n",
       " 2            894       2                     Myles, Mr. Thomas Francis   \n",
       " 3            895       3                              Wirz, Mr. Albert   \n",
       " 4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
       " ..           ...     ...                                           ...   \n",
       " 413         1305       3                            Spector, Mr. Woolf   \n",
       " 414         1306       1                  Oliva y Ocana, Dona. Fermina   \n",
       " 415         1307       3                  Saether, Mr. Simon Sivertsen   \n",
       " 416         1308       3                           Ware, Mr. Frederick   \n",
       " 417         1309       3                      Peter, Master. Michael J   \n",
       " \n",
       "         Sex   Age  SibSp  Parch              Ticket      Fare Cabin Embarked  \n",
       " 0      male  34.5      0      0              330911    7.8292   NaN        Q  \n",
       " 1    female  47.0      1      0              363272    7.0000   NaN        S  \n",
       " 2      male  62.0      0      0              240276    9.6875   NaN        Q  \n",
       " 3      male  27.0      0      0              315154    8.6625   NaN        S  \n",
       " 4    female  22.0      1      1             3101298   12.2875   NaN        S  \n",
       " ..      ...   ...    ...    ...                 ...       ...   ...      ...  \n",
       " 413    male  27.0      0      0           A.5. 3236    8.0500   NaN        S  \n",
       " 414  female  39.0      0      0            PC 17758  108.9000  C105        C  \n",
       " 415    male  38.5      0      0  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
       " 416    male  27.0      0      0              359309    8.0500   NaN        S  \n",
       " 417    male  27.0      1      1                2668   22.3583   NaN        C  \n",
       " \n",
       " [418 rows x 11 columns]]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_to_be_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived    0\n",
      "Pclass      0\n",
      "Name        0\n",
      "Sex         0\n",
      "Age         0\n",
      "SibSp       0\n",
      "Parch       0\n",
      "Fare        0\n",
      "Embarked    0\n",
      "dtype: int64\n",
      "--------------------------------------------------\n",
      "PassengerId      0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age              0\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             0\n",
      "Cabin          327\n",
      "Embarked         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#delete the cabin feature/column and others previously stated to exclude in train dataset\n",
    "drop_column = ['PassengerId','Cabin', 'Ticket']\n",
    "data_train.drop(drop_column, axis=1, inplace = True)\n",
    "\n",
    "print(data_train.isnull().sum())\n",
    "print(\"-\"*50)\n",
    "print(data_test.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "###CREATE: Feature Engineering for train and test/validation dataset\n",
    "for dataset in data_to_be_cleaned:    \n",
    "    #Discrete variables\n",
    "    dataset['FamilySize'] = dataset ['SibSp'] + dataset['Parch'] + 1\n",
    "\n",
    "    dataset['IsAlone'] = 1 #initialize to yes/1 is alone\n",
    "    dataset['IsAlone'].loc[dataset['FamilySize'] > 1] = 0 # now update to no/0 if family size is greater than 1\n",
    "\n",
    "    #quick and dirty code split title from name\n",
    "    dataset['Title'] = dataset['Name'].str.split(\", \", expand=True)[1].str.split(\".\", expand=True)[0]\n",
    "\n",
    "\n",
    "    #Continuous variable bins\n",
    "    dataset['FareBin'] = pd.qcut(dataset['Fare'], 4)\n",
    "\n",
    "    #Age Bins/Buckets using cut or value bins\n",
    "    dataset['AgeBin'] = pd.cut(dataset['Age'].astype(int), 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 14 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   Survived    891 non-null    int64   \n",
      " 1   Pclass      891 non-null    int64   \n",
      " 2   Name        891 non-null    object  \n",
      " 3   Sex         891 non-null    object  \n",
      " 4   Age         891 non-null    float64 \n",
      " 5   SibSp       891 non-null    int64   \n",
      " 6   Parch       891 non-null    int64   \n",
      " 7   Fare        891 non-null    float64 \n",
      " 8   Embarked    891 non-null    object  \n",
      " 9   FamilySize  891 non-null    int64   \n",
      " 10  IsAlone     891 non-null    int64   \n",
      " 11  Title       891 non-null    object  \n",
      " 12  FareBin     891 non-null    category\n",
      " 13  AgeBin      891 non-null    category\n",
      "dtypes: category(2), float64(2), int64(6), object(4)\n",
      "memory usage: 85.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data_to_be_cleaned[0].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 16 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   PassengerId  418 non-null    int64   \n",
      " 1   Pclass       418 non-null    int64   \n",
      " 2   Name         418 non-null    object  \n",
      " 3   Sex          418 non-null    object  \n",
      " 4   Age          418 non-null    float64 \n",
      " 5   SibSp        418 non-null    int64   \n",
      " 6   Parch        418 non-null    int64   \n",
      " 7   Ticket       418 non-null    object  \n",
      " 8   Fare         418 non-null    float64 \n",
      " 9   Cabin        91 non-null     object  \n",
      " 10  Embarked     418 non-null    object  \n",
      " 11  FamilySize   418 non-null    int64   \n",
      " 12  IsAlone      418 non-null    int64   \n",
      " 13  Title        418 non-null    object  \n",
      " 14  FareBin      418 non-null    category\n",
      " 15  AgeBin       418 non-null    category\n",
      "dtypes: category(2), float64(2), int64(6), object(6)\n",
      "memory usage: 47.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data_to_be_cleaned[1].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mr              517\n",
      "Miss            182\n",
      "Mrs             125\n",
      "Master           40\n",
      "Dr                7\n",
      "Rev               6\n",
      "Col               2\n",
      "Major             2\n",
      "Mlle              2\n",
      "Capt              1\n",
      "Sir               1\n",
      "Mme               1\n",
      "Jonkheer          1\n",
      "Ms                1\n",
      "Lady              1\n",
      "Don               1\n",
      "the Countess      1\n",
      "Name: Title, dtype: int64\n",
      "*-**-**-**-**-**-**-**-**-**-*\n",
      "Mr        517\n",
      "Miss      182\n",
      "Mrs       125\n",
      "Master     40\n",
      "Misc       27\n",
      "Name: Title, dtype: int64\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#cleanup rare title names\n",
    "print(data_train['Title'].value_counts())\n",
    "print('*-*'*10)\n",
    "stat_min = 10 \n",
    "title_names = (data_train['Title'].value_counts() < stat_min) #this will create a true false series with title name as index\n",
    "\n",
    "#apply and lambda functions are quick and dirty code to find and replace with fewer lines of code\n",
    "data_train['Title'] = data_train['Title'].apply(lambda x: 'Misc' if title_names.loc[x] == True else x)\n",
    "print(data_train['Title'].value_counts())\n",
    "print(\"-\"*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 14 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   Survived    891 non-null    int64   \n",
      " 1   Pclass      891 non-null    int64   \n",
      " 2   Name        891 non-null    object  \n",
      " 3   Sex         891 non-null    object  \n",
      " 4   Age         891 non-null    float64 \n",
      " 5   SibSp       891 non-null    int64   \n",
      " 6   Parch       891 non-null    int64   \n",
      " 7   Fare        891 non-null    float64 \n",
      " 8   Embarked    891 non-null    object  \n",
      " 9   FamilySize  891 non-null    int64   \n",
      " 10  IsAlone     891 non-null    int64   \n",
      " 11  Title       891 non-null    object  \n",
      " 12  FareBin     891 non-null    category\n",
      " 13  AgeBin      891 non-null    category\n",
      "dtypes: category(2), float64(2), int64(6), object(4)\n",
      "memory usage: 85.8+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 16 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   PassengerId  418 non-null    int64   \n",
      " 1   Pclass       418 non-null    int64   \n",
      " 2   Name         418 non-null    object  \n",
      " 3   Sex          418 non-null    object  \n",
      " 4   Age          418 non-null    float64 \n",
      " 5   SibSp        418 non-null    int64   \n",
      " 6   Parch        418 non-null    int64   \n",
      " 7   Ticket       418 non-null    object  \n",
      " 8   Fare         418 non-null    float64 \n",
      " 9   Cabin        91 non-null     object  \n",
      " 10  Embarked     418 non-null    object  \n",
      " 11  FamilySize   418 non-null    int64   \n",
      " 12  IsAlone      418 non-null    int64   \n",
      " 13  Title        418 non-null    object  \n",
      " 14  FareBin      418 non-null    category\n",
      " 15  AgeBin       418 non-null    category\n",
      "dtypes: category(2), float64(2), int64(6), object(6)\n",
      "memory usage: 47.1+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>FamilySize</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Title</th>\n",
       "      <th>FareBin</th>\n",
       "      <th>AgeBin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>(31.0, 512.329]</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Miss</td>\n",
       "      <td>(7.91, 14.454]</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>(31.0, 512.329]</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>(7.91, 14.454]</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>(7.91, 14.454]</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\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>51.8625</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>(31.0, 512.329]</td>\n",
       "      <td>(48.0, 64.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\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>21.0750</td>\n",
       "      <td>S</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Master</td>\n",
       "      <td>(14.454, 31.0]</td>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\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>11.1333</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>(7.91, 14.454]</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\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>30.0708</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>(14.454, 31.0]</td>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2         1       3                             Heikkinen, Miss. Laina   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4         0       3                           Allen, Mr. William Henry   \n",
       "5         0       3                                   Moran, Mr. James   \n",
       "6         0       1                            McCarthy, Mr. Timothy J   \n",
       "7         0       3                     Palsson, Master. Gosta Leonard   \n",
       "8         1       3  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)   \n",
       "9         1       2                Nasser, Mrs. Nicholas (Adele Achem)   \n",
       "\n",
       "      Sex   Age  SibSp  Parch     Fare Embarked  FamilySize  IsAlone   Title  \\\n",
       "0    male  22.0      1      0   7.2500        S           2        0      Mr   \n",
       "1  female  38.0      1      0  71.2833        C           2        0     Mrs   \n",
       "2  female  26.0      0      0   7.9250        S           1        1    Miss   \n",
       "3  female  35.0      1      0  53.1000        S           2        0     Mrs   \n",
       "4    male  35.0      0      0   8.0500        S           1        1      Mr   \n",
       "5    male  28.0      0      0   8.4583        Q           1        1      Mr   \n",
       "6    male  54.0      0      0  51.8625        S           1        1      Mr   \n",
       "7    male   2.0      3      1  21.0750        S           5        0  Master   \n",
       "8  female  27.0      0      2  11.1333        S           3        0     Mrs   \n",
       "9  female  14.0      1      0  30.0708        C           2        0     Mrs   \n",
       "\n",
       "           FareBin         AgeBin  \n",
       "0   (-0.001, 7.91]   (16.0, 32.0]  \n",
       "1  (31.0, 512.329]   (32.0, 48.0]  \n",
       "2   (7.91, 14.454]   (16.0, 32.0]  \n",
       "3  (31.0, 512.329]   (32.0, 48.0]  \n",
       "4   (7.91, 14.454]   (32.0, 48.0]  \n",
       "5   (7.91, 14.454]   (16.0, 32.0]  \n",
       "6  (31.0, 512.329]   (48.0, 64.0]  \n",
       "7   (14.454, 31.0]  (-0.08, 16.0]  \n",
       "8   (7.91, 14.454]   (16.0, 32.0]  \n",
       "9   (14.454, 31.0]  (-0.08, 16.0]  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preview data again\n",
    "data_train.info()\n",
    "data_test.info()\n",
    "data_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 16)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 14)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CONVERT: convert objects to category using Label Encoder for train and test/validation dataset\n",
    "\n",
    "#code categorical data\n",
    "label = LabelEncoder()\n",
    "for dataset in data_to_be_cleaned:    \n",
    "    dataset['Sex_Code'] = label.fit_transform(dataset['Sex'])\n",
    "    dataset['Embarked_Code'] = label.fit_transform(dataset['Embarked'])\n",
    "    dataset['Title_Code'] = label.fit_transform(dataset['Title'])\n",
    "    dataset['AgeBin_Code'] = label.fit_transform(dataset['AgeBin'])\n",
    "    dataset['FareBin_Code'] = label.fit_transform(dataset['FareBin'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>...</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Title</th>\n",
       "      <th>FareBin</th>\n",
       "      <th>AgeBin</th>\n",
       "      <th>Sex_Code</th>\n",
       "      <th>Embarked_Code</th>\n",
       "      <th>Title_Code</th>\n",
       "      <th>AgeBin_Code</th>\n",
       "      <th>FareBin_Code</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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>(-0.001, 7.896]</td>\n",
       "      <td>(30.4, 45.6]</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</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>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>(-0.001, 7.896]</td>\n",
       "      <td>(45.6, 60.8]</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>(7.896, 14.454]</td>\n",
       "      <td>(60.8, 76.0]</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>(7.896, 14.454]</td>\n",
       "      <td>(15.2, 30.4]</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>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>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>(7.896, 14.454]</td>\n",
       "      <td>(15.2, 30.4]</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>(7.896, 14.454]</td>\n",
       "      <td>(15.2, 30.4]</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Dona</td>\n",
       "      <td>(31.472, 512.329]</td>\n",
       "      <td>(30.4, 45.6]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>(-0.001, 7.896]</td>\n",
       "      <td>(30.4, 45.6]</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>(7.896, 14.454]</td>\n",
       "      <td>(15.2, 30.4]</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Master</td>\n",
       "      <td>(14.454, 31.472]</td>\n",
       "      <td>(15.2, 30.4]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                                          Name  \\\n",
       "0            892       3                              Kelly, Mr. James   \n",
       "1            893       3              Wilkes, Mrs. James (Ellen Needs)   \n",
       "2            894       2                     Myles, Mr. Thomas Francis   \n",
       "3            895       3                              Wirz, Mr. Albert   \n",
       "4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
       "..           ...     ...                                           ...   \n",
       "413         1305       3                            Spector, Mr. Woolf   \n",
       "414         1306       1                  Oliva y Ocana, Dona. Fermina   \n",
       "415         1307       3                  Saether, Mr. Simon Sivertsen   \n",
       "416         1308       3                           Ware, Mr. Frederick   \n",
       "417         1309       3                      Peter, Master. Michael J   \n",
       "\n",
       "        Sex   Age  SibSp  Parch              Ticket      Fare Cabin  ...  \\\n",
       "0      male  34.5      0      0              330911    7.8292   NaN  ...   \n",
       "1    female  47.0      1      0              363272    7.0000   NaN  ...   \n",
       "2      male  62.0      0      0              240276    9.6875   NaN  ...   \n",
       "3      male  27.0      0      0              315154    8.6625   NaN  ...   \n",
       "4    female  22.0      1      1             3101298   12.2875   NaN  ...   \n",
       "..      ...   ...    ...    ...                 ...       ...   ...  ...   \n",
       "413    male  27.0      0      0           A.5. 3236    8.0500   NaN  ...   \n",
       "414  female  39.0      0      0            PC 17758  108.9000  C105  ...   \n",
       "415    male  38.5      0      0  SOTON/O.Q. 3101262    7.2500   NaN  ...   \n",
       "416    male  27.0      0      0              359309    8.0500   NaN  ...   \n",
       "417    male  27.0      1      1                2668   22.3583   NaN  ...   \n",
       "\n",
       "    FamilySize  IsAlone   Title            FareBin        AgeBin Sex_Code  \\\n",
       "0            1        1      Mr    (-0.001, 7.896]  (30.4, 45.6]        1   \n",
       "1            2        0     Mrs    (-0.001, 7.896]  (45.6, 60.8]        0   \n",
       "2            1        1      Mr    (7.896, 14.454]  (60.8, 76.0]        1   \n",
       "3            1        1      Mr    (7.896, 14.454]  (15.2, 30.4]        1   \n",
       "4            3        0     Mrs    (7.896, 14.454]  (15.2, 30.4]        0   \n",
       "..         ...      ...     ...                ...           ...      ...   \n",
       "413          1        1      Mr    (7.896, 14.454]  (15.2, 30.4]        1   \n",
       "414          1        1    Dona  (31.472, 512.329]  (30.4, 45.6]        0   \n",
       "415          1        1      Mr    (-0.001, 7.896]  (30.4, 45.6]        1   \n",
       "416          1        1      Mr    (7.896, 14.454]  (15.2, 30.4]        1   \n",
       "417          3        0  Master   (14.454, 31.472]  (15.2, 30.4]        1   \n",
       "\n",
       "     Embarked_Code  Title_Code  AgeBin_Code  FareBin_Code  \n",
       "0                1           5            2             0  \n",
       "1                2           6            3             0  \n",
       "2                1           5            4             1  \n",
       "3                2           5            1             1  \n",
       "4                2           6            1             1  \n",
       "..             ...         ...          ...           ...  \n",
       "413              2           5            1             1  \n",
       "414              0           1            2             3  \n",
       "415              2           5            2             0  \n",
       "416              2           5            1             1  \n",
       "417              0           3            1             2  \n",
       "\n",
       "[418 rows x 21 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original X Y:  ['Survived', 'Sex', 'Pclass', 'Embarked', 'Title', 'SibSp', 'Parch', 'Age', 'Fare', 'FamilySize', 'IsAlone'] \n",
      "\n",
      "Training X Y:  ['Sex_Code', 'Pclass', 'Embarked_Code', 'Title_Code', 'IsAlone', 'FamilySize', 'AgeBin_Code', 'FareBin_Code'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#define y variable aka target/outcome\n",
    "Target = ['Survived']\n",
    "\n",
    "#define x variables for original features aka feature selection\n",
    "original_train_data = ['Sex','Pclass', 'Embarked', 'Title','SibSp', 'Parch', 'Age', 'Fare', 'FamilySize', 'IsAlone'] #pretty name/values for charts\n",
    "train_data = ['Sex_Code','Pclass', 'Embarked_Code', 'Title_Code','IsAlone', 'FamilySize', 'AgeBin_Code', 'FareBin_Code'] #coded for algorithm calculation\n",
    "original_dataset =  Target + original_train_data\n",
    "print('Original X Y: ', original_dataset, '\\n')\n",
    "print('Training X Y: ', train_data, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Double check Cleaned Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train columns with null values: \n",
      " Survived         0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age              0\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Fare             0\n",
      "Embarked         0\n",
      "FamilySize       0\n",
      "IsAlone          0\n",
      "Title            0\n",
      "FareBin          0\n",
      "AgeBin           0\n",
      "Sex_Code         0\n",
      "Embarked_Code    0\n",
      "Title_Code       0\n",
      "AgeBin_Code      0\n",
      "FareBin_Code     0\n",
      "dtype: int64\n",
      "----------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 19 columns):\n",
      " #   Column         Non-Null Count  Dtype   \n",
      "---  ------         --------------  -----   \n",
      " 0   Survived       891 non-null    int64   \n",
      " 1   Pclass         891 non-null    int64   \n",
      " 2   Name           891 non-null    object  \n",
      " 3   Sex            891 non-null    object  \n",
      " 4   Age            891 non-null    float64 \n",
      " 5   SibSp          891 non-null    int64   \n",
      " 6   Parch          891 non-null    int64   \n",
      " 7   Fare           891 non-null    float64 \n",
      " 8   Embarked       891 non-null    object  \n",
      " 9   FamilySize     891 non-null    int64   \n",
      " 10  IsAlone        891 non-null    int64   \n",
      " 11  Title          891 non-null    object  \n",
      " 12  FareBin        891 non-null    category\n",
      " 13  AgeBin         891 non-null    category\n",
      " 14  Sex_Code       891 non-null    int32   \n",
      " 15  Embarked_Code  891 non-null    int32   \n",
      " 16  Title_Code     891 non-null    int32   \n",
      " 17  AgeBin_Code    891 non-null    int32   \n",
      " 18  FareBin_Code   891 non-null    int32   \n",
      "dtypes: category(2), float64(2), int32(5), int64(6), object(4)\n",
      "memory usage: 103.3+ KB\n",
      "None\n",
      "----------\n",
      "Test/Validation columns with null values: \n",
      " PassengerId        0\n",
      "Pclass             0\n",
      "Name               0\n",
      "Sex                0\n",
      "Age                0\n",
      "SibSp              0\n",
      "Parch              0\n",
      "Ticket             0\n",
      "Fare               0\n",
      "Cabin            327\n",
      "Embarked           0\n",
      "FamilySize         0\n",
      "IsAlone            0\n",
      "Title              0\n",
      "FareBin            0\n",
      "AgeBin             0\n",
      "Sex_Code           0\n",
      "Embarked_Code      0\n",
      "Title_Code         0\n",
      "AgeBin_Code        0\n",
      "FareBin_Code       0\n",
      "dtype: int64\n",
      "----------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype   \n",
      "---  ------         --------------  -----   \n",
      " 0   PassengerId    418 non-null    int64   \n",
      " 1   Pclass         418 non-null    int64   \n",
      " 2   Name           418 non-null    object  \n",
      " 3   Sex            418 non-null    object  \n",
      " 4   Age            418 non-null    float64 \n",
      " 5   SibSp          418 non-null    int64   \n",
      " 6   Parch          418 non-null    int64   \n",
      " 7   Ticket         418 non-null    object  \n",
      " 8   Fare           418 non-null    float64 \n",
      " 9   Cabin          91 non-null     object  \n",
      " 10  Embarked       418 non-null    object  \n",
      " 11  FamilySize     418 non-null    int64   \n",
      " 12  IsAlone        418 non-null    int64   \n",
      " 13  Title          418 non-null    object  \n",
      " 14  FareBin        418 non-null    category\n",
      " 15  AgeBin         418 non-null    category\n",
      " 16  Sex_Code       418 non-null    int32   \n",
      " 17  Embarked_Code  418 non-null    int32   \n",
      " 18  Title_Code     418 non-null    int32   \n",
      " 19  AgeBin_Code    418 non-null    int32   \n",
      " 20  FareBin_Code   418 non-null    int32   \n",
      "dtypes: category(2), float64(2), int32(5), int64(6), object(6)\n",
      "memory usage: 55.3+ KB\n",
      "None\n",
      "----------\n"
     ]
    }
   ],
   "source": [
    "print('Train columns with null values: \\n', data_train.isnull().sum())\n",
    "print(\"-\"*10)\n",
    "print (data_train.info())\n",
    "print(\"-\"*10)\n",
    "\n",
    "print('Test/Validation columns with null values: \\n', data_test.isnull().sum())\n",
    "print(\"-\"*10)\n",
    "print (data_test.info())\n",
    "print(\"-\"*10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_train[train_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = data_train[Target]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split Training and Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data given for training - Shape: (891, 19)\n",
      "Train split of the given training data - Shape: (712, 8)\n",
      "Test split of the tgiven training data - Shape: (179, 8)\n"
     ]
    },
    {
     "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex_Code</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Embarked_Code</th>\n",
       "      <th>Title_Code</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>AgeBin_Code</th>\n",
       "      <th>FareBin_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sex_Code  Pclass  Embarked_Code  Title_Code  IsAlone  FamilySize  \\\n",
       "140         0       3              0           4        0           3   \n",
       "439         1       2              2           3        1           1   \n",
       "817         1       2              0           3        0           3   \n",
       "378         1       3              0           3        1           1   \n",
       "491         1       3              2           3        1           1   \n",
       "\n",
       "     AgeBin_Code  FareBin_Code  \n",
       "140            1             2  \n",
       "439            1             1  \n",
       "817            1             3  \n",
       "378            1             0  \n",
       "491            1             0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#split train and test data with function defaults\n",
    "#random_state -> seed or control random number generator: https://www.quora.com/What-is-seed-in-random-number-generation\n",
    "x_train, x_test, y_train, y_test = model_selection.train_test_split(data_train[train_data], data_train[Target],test_size= 0.2, random_state = 0)\n",
    "\n",
    "print(\"Data given for training - Shape: {}\".format(data_train.shape))\n",
    "print(\"Train split of the given training data - Shape: {}\".format(x_train.shape))\n",
    "print(\"Test split of the tgiven training data - Shape: {}\".format(x_test.shape))\n",
    "\n",
    "original_refined_dataset = data_train[original_train_data]\n",
    "\n",
    "x_train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>IsAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>Misc</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>Q</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sex  Pclass Embarked Title  SibSp  Parch   Age     Fare  FamilySize  \\\n",
       "0      male       3        S    Mr      1      0  22.0   7.2500           2   \n",
       "1    female       1        C   Mrs      1      0  38.0  71.2833           2   \n",
       "2    female       3        S  Miss      0      0  26.0   7.9250           1   \n",
       "3    female       1        S   Mrs      1      0  35.0  53.1000           2   \n",
       "4      male       3        S    Mr      0      0  35.0   8.0500           1   \n",
       "..      ...     ...      ...   ...    ...    ...   ...      ...         ...   \n",
       "886    male       2        S  Misc      0      0  27.0  13.0000           1   \n",
       "887  female       1        S  Miss      0      0  19.0  30.0000           1   \n",
       "888  female       3        S  Miss      1      2  28.0  23.4500           4   \n",
       "889    male       1        C    Mr      0      0  26.0  30.0000           1   \n",
       "890    male       3        Q    Mr      0      0  32.0   7.7500           1   \n",
       "\n",
       "     IsAlone  \n",
       "0          0  \n",
       "1          0  \n",
       "2          1  \n",
       "3          0  \n",
       "4          1  \n",
       "..       ...  \n",
       "886        1  \n",
       "887        1  \n",
       "888        0  \n",
       "889        1  \n",
       "890        1  \n",
       "\n",
       "[891 rows x 10 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_refined_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform Exploratory Analysis with Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survival Correlation by: Sex\n",
      "      Sex  Survived\n",
      "0  female  0.742038\n",
      "1    male  0.188908\n",
      "---------- \n",
      "\n",
      "Survival Correlation by: Pclass\n",
      "   Pclass  Survived\n",
      "0       1  0.629630\n",
      "1       2  0.472826\n",
      "2       3  0.242363\n",
      "---------- \n",
      "\n",
      "Survival Correlation by: Embarked\n",
      "  Embarked  Survived\n",
      "0        C  0.553571\n",
      "1        Q  0.389610\n",
      "2        S  0.339009\n",
      "---------- \n",
      "\n",
      "Survival Correlation by: Title\n",
      "    Title  Survived\n",
      "0  Master  0.575000\n",
      "1    Misc  0.444444\n",
      "2    Miss  0.697802\n",
      "3      Mr  0.156673\n",
      "4     Mrs  0.792000\n",
      "---------- \n",
      "\n",
      "Survival Correlation by: SibSp\n",
      "   SibSp  Survived\n",
      "0      0  0.345395\n",
      "1      1  0.535885\n",
      "2      2  0.464286\n",
      "3      3  0.250000\n",
      "4      4  0.166667\n",
      "5      5  0.000000\n",
      "6      8  0.000000\n",
      "---------- \n",
      "\n",
      "Survival Correlation by: Parch\n",
      "   Parch  Survived\n",
      "0      0  0.343658\n",
      "1      1  0.550847\n",
      "2      2  0.500000\n",
      "3      3  0.600000\n",
      "4      4  0.000000\n",
      "5      5  0.200000\n",
      "6      6  0.000000\n",
      "---------- \n",
      "\n",
      "Survival Correlation by: FamilySize\n",
      "   FamilySize  Survived\n",
      "0           1  0.303538\n",
      "1           2  0.552795\n",
      "2           3  0.578431\n",
      "3           4  0.724138\n",
      "4           5  0.200000\n",
      "5           6  0.136364\n",
      "6           7  0.333333\n",
      "7           8  0.000000\n",
      "8          11  0.000000\n",
      "---------- \n",
      "\n",
      "Survival Correlation by: IsAlone\n",
      "   IsAlone  Survived\n",
      "0        0  0.505650\n",
      "1        1  0.303538\n",
      "---------- \n",
      "\n",
      "Survived    0    1\n",
      "Title             \n",
      "Master     17   23\n",
      "Misc       15   12\n",
      "Miss       55  127\n",
      "Mr        436   81\n",
      "Mrs        26   99\n"
     ]
    }
   ],
   "source": [
    "#Discrete Variable Correlation by Survival using\n",
    "#group by aka pivot table\n",
    "for x in original_refined_dataset:\n",
    "    if data_train[x].dtype != 'float64' :\n",
    "        print('Survival Correlation by:', x)\n",
    "        print(data_train[[x, Target[0]]].groupby(x, as_index=False).mean())\n",
    "        print('-'*10, '\\n')\n",
    "        \n",
    "\n",
    "#using crosstabs\n",
    "print(pd.crosstab(data_train['Title'],data_train[Target[0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1eecb59adc8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=[16,12])\n",
    "\n",
    "plt.subplot(231)\n",
    "plt.boxplot(x=data_train['Fare'], showmeans = True, meanline = True)\n",
    "plt.title('Fare Boxplot')\n",
    "plt.ylabel('Fare ($)')\n",
    "\n",
    "plt.subplot(232)\n",
    "plt.boxplot(data_train['Age'], showmeans = True, meanline = True)\n",
    "plt.title('Age Boxplot')\n",
    "plt.ylabel('Age (Years)')\n",
    "\n",
    "plt.subplot(233)\n",
    "plt.boxplot(data_train['FamilySize'], showmeans = True, meanline = True)\n",
    "plt.title('Family Size Boxplot')\n",
    "plt.ylabel('Family Size (#)')\n",
    "\n",
    "plt.subplot(234)\n",
    "plt.hist(x = [data_train[data_train['Survived']==1]['Fare'], data_train[data_train['Survived']==0]['Fare']], \n",
    "         stacked=True, color = ['g','r'],label = ['Survived','Dead'])\n",
    "plt.title('Fare Histogram by Survival')\n",
    "plt.xlabel('Fare ($)')\n",
    "plt.ylabel('# of Passengers')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(235)\n",
    "plt.hist(x = [data_train[data_train['Survived']==1]['Age'], data_train[data_train['Survived']==0]['Age']], \n",
    "         stacked=True, color = ['g','r'],label = ['Survived','Dead'])\n",
    "plt.title('Age Histogram by Survival')\n",
    "plt.xlabel('Age (Years)')\n",
    "plt.ylabel('# of Passengers')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(236)\n",
    "plt.hist(x = [data_train[data_train['Survived']==1]['FamilySize'], data_train[data_train['Survived']==0]['FamilySize']], \n",
    "         stacked=True, color = ['g','r'],label = ['Survived','Dead'])\n",
    "plt.title('Family Size Histogram by Survival')\n",
    "plt.xlabel('Family Size (#)')\n",
    "plt.ylabel('# of Passengers')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1eecb6e1788>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graph individual features by survival\n",
    "fig, saxis = plt.subplots(2, 3,figsize=(16,12))\n",
    "\n",
    "sns.barplot(x = 'Embarked', y = 'Survived', data=data_train, ax = saxis[0,0])\n",
    "sns.barplot(x = 'Pclass', y = 'Survived', order=[1,2,3], data=data_train, ax = saxis[0,1])\n",
    "sns.barplot(x = 'IsAlone', y = 'Survived', order=[1,0], data=data_train, ax = saxis[0,2])\n",
    "\n",
    "sns.pointplot(x = 'FareBin', y = 'Survived',  data=data_train, ax = saxis[1,0])\n",
    "sns.pointplot(x = 'AgeBin', y = 'Survived',  data=data_train, ax = saxis[1,1])\n",
    "sns.pointplot(x = 'FamilySize', y = 'Survived', data=data_train, ax = saxis[1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pclass vs Family Size Survival Comparison')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graph distribution of qualitative data: Pclass\n",
    "#we know class mattered in survival, now let's compare class and a 2nd feature\n",
    "fig, (axis1,axis2,axis3) = plt.subplots(1,3,figsize=(14,12))\n",
    "\n",
    "sns.boxplot(x = 'Pclass', y = 'Fare', hue = 'Survived', data = data_train, ax = axis1)\n",
    "axis1.set_title('Pclass vs Fare Survival Comparison')\n",
    "\n",
    "sns.violinplot(x = 'Pclass', y = 'Age', hue = 'Survived', data = data_train, split = True, ax = axis2)\n",
    "axis2.set_title('Pclass vs Age Survival Comparison')\n",
    "\n",
    "sns.boxplot(x = 'Pclass', y ='FamilySize', hue = 'Survived', data = data_train, ax = axis3)\n",
    "axis3.set_title('Pclass vs Family Size Survival Comparison')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Sex vs IsAlone Survival Comparison')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graph distribution of qualitative data: Sex\n",
    "#we know sex mattered in survival, now let's compare sex and a 2nd feature\n",
    "fig, qaxis = plt.subplots(1,3,figsize=(14,12))\n",
    "\n",
    "sns.barplot(x = 'Sex', y = 'Survived', hue = 'Embarked', data=data_train, ax = qaxis[0])\n",
    "axis1.set_title('Sex vs Embarked Survival Comparison')\n",
    "\n",
    "sns.barplot(x = 'Sex', y = 'Survived', hue = 'Pclass', data=data_train, ax  = qaxis[1])\n",
    "axis1.set_title('Sex vs Pclass Survival Comparison')\n",
    "\n",
    "sns.barplot(x = 'Sex', y = 'Survived', hue = 'IsAlone', data=data_train, ax  = qaxis[2])\n",
    "axis1.set_title('Sex vs IsAlone Survival Comparison')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation heatmap of dataset\n",
    "def correlation_heatmap(df):\n",
    "    _ , ax = plt.subplots(figsize =(14, 12))\n",
    "    colormap = sns.diverging_palette(220, 10, as_cmap = True)\n",
    "    \n",
    "    _ = sns.heatmap(\n",
    "        df.corr(), \n",
    "        cmap = colormap,\n",
    "        square=True, \n",
    "        cbar_kws={'shrink':.9 }, \n",
    "        ax=ax,\n",
    "        annot=True, \n",
    "        linewidths=0.1,vmax=1.0, linecolor='white',\n",
    "        annot_kws={'fontsize':12 }\n",
    "    )\n",
    "    \n",
    "    plt.title('Correlation of Features', y=1.05, size=15)\n",
    "\n",
    "correlation_heatmap(data_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV, StratifiedKFold, StratifiedShuffleSplit, cross_val_score\n",
    "# Importing the model. \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score,classification_report, confusion_matrix\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### KNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(metric='minkowski', p=2) \n",
    "## doing 10 fold staratified-shuffle-split cross validation \n",
    "cv = StratifiedShuffleSplit(n_splits=10, test_size=.25, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy scores are: [0.7320224719101124, 0.7634831460674156, 0.7786516853932584, 0.7898876404494383, 0.7932584269662922, 0.7853932584269663, 0.798876404494382, 0.7915730337078652, 0.8016853932584269, 0.7960674157303371, 0.798314606741573, 0.7938202247191011, 0.792696629213483, 0.7898876404494383, 0.7921348314606742, 0.7876404494382022, 0.7910112359550562, 0.7893258426966293, 0.7926966292134832, 0.7904494382022472, 0.7887640449438201, 0.7893258426966292, 0.7853932584269663, 0.7859550561797752, 0.7865168539325843, 0.7803370786516853, 0.7882022471910112, 0.7831460674157302, 0.7842696629213484, 0.7780898876404494]\n",
      "\n",
      "Mean accuracy score: 0.7866292134831462\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1eecc6ab848>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Search for an optimal value of k for KNN.\n",
    "k_range = range(1,31)\n",
    "k_scores = []\n",
    "for k in k_range:\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    scores = cross_val_score(knn, x_train , y_train , cv = cv, scoring = 'accuracy')\n",
    "    k_scores.append(scores.mean())\n",
    "print(\"Accuracy scores are: {}\\n\".format(k_scores))\n",
    "print (\"Mean accuracy score: {}\".format(np.mean(k_scores)))\n",
    "plt.plot(k_range, k_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = KNeighborsClassifier(n_neighbors=9)\n",
    "classifier.fit(x_train, y_train)\n",
    "y_pred = classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[94 16]\n",
      " [18 51]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.85      0.85       110\n",
      "           1       0.76      0.74      0.75        69\n",
      "\n",
      "    accuracy                           0.81       179\n",
      "   macro avg       0.80      0.80      0.80       179\n",
      "weighted avg       0.81      0.81      0.81       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8100558659217877\n"
     ]
    }
   ],
   "source": [
    "knn_accy = accuracy_score(y_pred, y_test)\n",
    "print(knn_accy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gaussian Naive Bayes Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7988826815642458\n"
     ]
    }
   ],
   "source": [
    "# Gaussian Naive Bayes\n",
    "\n",
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X,Y)\n",
    "\n",
    "y_pred = gaussian.predict(x_test)\n",
    "gaussian_accy = accuracy_score(y_pred, y_test)\n",
    "\n",
    "print(gaussian_accy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[85 25]\n",
      " [11 58]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.77      0.83       110\n",
      "           1       0.70      0.84      0.76        69\n",
      "\n",
      "    accuracy                           0.80       179\n",
      "   macro avg       0.79      0.81      0.79       179\n",
      "weighted avg       0.81      0.80      0.80       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SVM Classifier Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8268156424581006\n"
     ]
    }
   ],
   "source": [
    "svclassifier = SVC(kernel='rbf')\n",
    "\n",
    "svclassifier.fit(x_train, y_train)\n",
    "y_pred = svclassifier.predict(x_test)\n",
    "\n",
    "svclassifier_accy = accuracy_score(y_pred, y_test)\n",
    "print(svclassifier_accy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[96 14]\n",
      " [17 52]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.87      0.86       110\n",
      "           1       0.79      0.75      0.77        69\n",
      "\n",
      "    accuracy                           0.83       179\n",
      "   macro avg       0.82      0.81      0.82       179\n",
      "weighted avg       0.83      0.83      0.83       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8324022346368715\n"
     ]
    }
   ],
   "source": [
    "decision_tree_classifier =DecisionTreeClassifier()\n",
    "\n",
    "decision_tree_classifier.fit(x_train, y_train)\n",
    "y_pred = decision_tree_classifier.predict(x_test)\n",
    "\n",
    "decision_tree_classifier_accy = accuracy_score(y_pred, y_test)\n",
    "print(decision_tree_classifier_accy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[101   9]\n",
      " [ 21  48]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.92      0.87       110\n",
      "           1       0.84      0.70      0.76        69\n",
      "\n",
      "    accuracy                           0.83       179\n",
      "   macro avg       0.83      0.81      0.82       179\n",
      "weighted avg       0.83      0.83      0.83       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The maximum accuracy that was obtained is 83.8% of Decision Tree model. This model will be used to predict the values of the validation test data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_data = data_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
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       "      <th>Title</th>\n",
       "      <th>FareBin</th>\n",
       "      <th>AgeBin</th>\n",
       "      <th>Sex_Code</th>\n",
       "      <th>Embarked_Code</th>\n",
       "      <th>Title_Code</th>\n",
       "      <th>AgeBin_Code</th>\n",
       "      <th>FareBin_Code</th>\n",
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       "      <td>892</td>\n",
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       "      <td>Kelly, Mr. James</td>\n",
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       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
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       "      <td>1</td>\n",
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       "      <td>(30.4, 45.6]</td>\n",
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       "      <td>47.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
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       "      <td>(45.6, 60.8]</td>\n",
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       "      <td>Myles, Mr. Thomas Francis</td>\n",
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       "      <td>62.0</td>\n",
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       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>(7.896, 14.454]</td>\n",
       "      <td>(60.8, 76.0]</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "    <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",
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       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>(7.896, 14.454]</td>\n",
       "      <td>(15.2, 30.4]</td>\n",
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       "      <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>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>(7.896, 14.454]</td>\n",
       "      <td>(15.2, 30.4]</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
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       "      <td>A.5. 3236</td>\n",
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       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Dona</td>\n",
       "      <td>(31.472, 512.329]</td>\n",
       "      <td>(30.4, 45.6]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1</td>\n",
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       "      <td>(30.4, 45.6]</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>(7.896, 14.454]</td>\n",
       "      <td>(15.2, 30.4]</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Master</td>\n",
       "      <td>(14.454, 31.472]</td>\n",
       "      <td>(15.2, 30.4]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                                          Name  \\\n",
       "0            892       3                              Kelly, Mr. James   \n",
       "1            893       3              Wilkes, Mrs. James (Ellen Needs)   \n",
       "2            894       2                     Myles, Mr. Thomas Francis   \n",
       "3            895       3                              Wirz, Mr. Albert   \n",
       "4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
       "..           ...     ...                                           ...   \n",
       "413         1305       3                            Spector, Mr. Woolf   \n",
       "414         1306       1                  Oliva y Ocana, Dona. Fermina   \n",
       "415         1307       3                  Saether, Mr. Simon Sivertsen   \n",
       "416         1308       3                           Ware, Mr. Frederick   \n",
       "417         1309       3                      Peter, Master. Michael J   \n",
       "\n",
       "        Sex   Age  SibSp  Parch              Ticket      Fare Cabin  ...  \\\n",
       "0      male  34.5      0      0              330911    7.8292   NaN  ...   \n",
       "1    female  47.0      1      0              363272    7.0000   NaN  ...   \n",
       "2      male  62.0      0      0              240276    9.6875   NaN  ...   \n",
       "3      male  27.0      0      0              315154    8.6625   NaN  ...   \n",
       "4    female  22.0      1      1             3101298   12.2875   NaN  ...   \n",
       "..      ...   ...    ...    ...                 ...       ...   ...  ...   \n",
       "413    male  27.0      0      0           A.5. 3236    8.0500   NaN  ...   \n",
       "414  female  39.0      0      0            PC 17758  108.9000  C105  ...   \n",
       "415    male  38.5      0      0  SOTON/O.Q. 3101262    7.2500   NaN  ...   \n",
       "416    male  27.0      0      0              359309    8.0500   NaN  ...   \n",
       "417    male  27.0      1      1                2668   22.3583   NaN  ...   \n",
       "\n",
       "    FamilySize  IsAlone   Title            FareBin        AgeBin Sex_Code  \\\n",
       "0            1        1      Mr    (-0.001, 7.896]  (30.4, 45.6]        1   \n",
       "1            2        0     Mrs    (-0.001, 7.896]  (45.6, 60.8]        0   \n",
       "2            1        1      Mr    (7.896, 14.454]  (60.8, 76.0]        1   \n",
       "3            1        1      Mr    (7.896, 14.454]  (15.2, 30.4]        1   \n",
       "4            3        0     Mrs    (7.896, 14.454]  (15.2, 30.4]        0   \n",
       "..         ...      ...     ...                ...           ...      ...   \n",
       "413          1        1      Mr    (7.896, 14.454]  (15.2, 30.4]        1   \n",
       "414          1        1    Dona  (31.472, 512.329]  (30.4, 45.6]        0   \n",
       "415          1        1      Mr    (-0.001, 7.896]  (30.4, 45.6]        1   \n",
       "416          1        1      Mr    (7.896, 14.454]  (15.2, 30.4]        1   \n",
       "417          3        0  Master   (14.454, 31.472]  (15.2, 30.4]        1   \n",
       "\n",
       "     Embarked_Code  Title_Code  AgeBin_Code  FareBin_Code  \n",
       "0                1           5            2             0  \n",
       "1                2           6            3             0  \n",
       "2                1           5            4             1  \n",
       "3                2           5            1             1  \n",
       "4                2           6            1             1  \n",
       "..             ...         ...          ...           ...  \n",
       "413              2           5            1             1  \n",
       "414              0           1            2             3  \n",
       "415              2           5            2             0  \n",
       "416              2           5            1             1  \n",
       "417              0           3            1             2  \n",
       "\n",
       "[418 rows x 21 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val = validation_data[train_data]\n",
    "val_prediction = decision_tree_classifier.predict(X_val)\n",
    "passengerid = validation_data[\"PassengerId\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": passengerid,\n",
    "        \"Survived\": val_prediction\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "0            892         0\n",
       "1            893         0\n",
       "2            894         0\n",
       "3            895         0\n",
       "4            896         0\n",
       "..           ...       ...\n",
       "413         1305         0\n",
       "414         1306         1\n",
       "415         1307         0\n",
       "416         1308         0\n",
       "417         1309         0\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.PassengerId = submission.PassengerId.astype(int)\n",
    "submission.Survived = submission.Survived.astype(int)\n",
    "\n",
    "submission.to_csv(\"titanic1_submission.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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