ML-Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data analysis and wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "\n",
    "# visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# machine learning\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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   NaN      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   NaN      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   NaN      0      0              359309    8.0500   NaN        S  \n",
       " 417    male   NaN      1      1                2668   22.3583   NaN        C  \n",
       " \n",
       " [418 rows x 11 columns]]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv('/Users/Manav/Desktop/train.csv')\n",
    "test_df = pd.read_csv('/Users/Manav/Desktop/test.csv')\n",
    "combine = [train_df, test_df]\n",
    "combine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n",
      " 'Ticket' 'Fare' 'Cabin' 'Embarked']\n",
      "['PassengerId' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch' 'Ticket' 'Fare'\n",
      " 'Cabin' 'Embarked']\n"
     ]
    }
   ],
   "source": [
    "print(train_df.columns.values)\n",
    "print (test_df.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>886</td>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>887</td>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>888</td>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>889</td>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>890</td>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n",
      "________________________________________\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()\n",
    "print('_'*40)\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unique</td>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>681</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>Pernot, Mr. Rene</td>\n",
       "      <td>male</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Name   Sex    Ticket Cabin Embarked\n",
       "count                891   891       891   204      889\n",
       "unique               891     2       681   147        3\n",
       "top     Pernot, Mr. Rene  male  CA. 2343    G6        S\n",
       "freq                   1   577         7     4      644"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Pclass','Survived']].groupby(['Pclass'],as_index=False).mean()\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 126,
     "metadata": {},
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   ],
   "source": [
    "train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
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      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 127,
     "metadata": {},
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   ],
   "source": [
    "train_df[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"SibSp\", \"Survived\"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parch</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>2</td>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
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       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Parch\", \"Survived\"]].groupby(['Parch'], as_index=False).mean().sort_values(by='Parch',ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>891.000000</td>\n",
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       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Fare\n",
       "count  891.000000\n",
       "mean    32.204208\n",
       "std     49.693429\n",
       "min      0.000000\n",
       "25%      7.910400\n",
       "50%     14.454200\n",
       "75%     31.000000\n",
       "max    512.329200"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Fare']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a27be1b10>"
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     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df, col='Pclass')\n",
    "g.map(plt.hist,'Fare', bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a27be0110>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df, col='Survived')\n",
    "g.map(plt.hist, 'Age', bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', height=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
    "grid.add_legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/usr/local/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py:715: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "/usr/local/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py:720: UserWarning: Using the pointplot function without specifying `hue_order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a28ddf190>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 323.94x475.2 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, row='Embarked', size=2.2, aspect=1.6)\n",
    "grid.map(sns.pointplot, 'Pclass', 'Survived','Sex', palette='deep')\n",
    "grid.add_legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 514.88x316.8 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, row='Sex', col='Survived', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist,'Age',bins=20)\n",
    "grid.add_legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py:715: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a2933c710>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, row='Embarked', col='Survived', size=2.2, aspect=1.6)\n",
    "grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before (891, 12) (418, 11) (891, 12) (418, 11)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('After', (891, 10), (418, 9), (891, 10), (418, 9))"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Before\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)\n",
    "\n",
    "train_df = train_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "test_df = test_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "\"After\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Capt</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Col</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Countess</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Don</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dr</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Jonkheer</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Lady</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Major</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Miss</td>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mlle</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mme</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mrs</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Ms</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Rev</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sir</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     2\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dr             1     6\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    40\n",
       "Miss         182     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   517\n",
       "Mrs          125     0\n",
       "Ms             1     0\n",
       "Rev            0     6\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "\n",
    "\n",
    "pd.crosstab(train_df['Title'], train_df['Sex'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Rare</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "0  Master  0.575000\n",
       "1    Miss  0.702703\n",
       "2      Mr  0.156673\n",
       "3     Mrs  0.793651\n",
       "4    Rare  0.347826"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\\\n",
    " 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "    \n",
    "train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch     Fare Embarked  Title  \n",
       "0      0   7.2500        S      1  \n",
       "1      0  71.2833        C      3  \n",
       "2      0   7.9250        S      2  \n",
       "3      0  53.1000        S      3  \n",
       "4      0   8.0500        S      1  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    517\n",
       "2    185\n",
       "3    126\n",
       "4     40\n",
       "5     23\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   PassengerId  Survived  Pclass  \\\n",
      "0            1         0       3   \n",
      "1            2         1       1   \n",
      "2            3         1       3   \n",
      "3            4         1       1   \n",
      "4            5         0       3   \n",
      "\n",
      "                                                Name     Sex   Age  SibSp  \\\n",
      "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
      "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
      "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
      "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
      "4                           Allen, Mr. William Henry    male  35.0      0   \n",
      "\n",
      "   Parch     Fare Embarked  Title  \n",
      "0      0   7.2500        S      1  \n",
      "1      0  71.2833        C      3  \n",
      "2      0   7.9250        S      2  \n",
      "3      0  53.1000        S      3  \n",
      "4      0   8.0500        S      1  \n"
     ]
    }
   ],
   "source": [
    "print(combine[0].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 9), (418, 9))"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Name', 'PassengerId'], axis=1)\n",
    "test_df = test_df.drop(['Name'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    male  22.0      1      0   7.2500        S      1\n",
       "1         1       1  female  38.0      1      0  71.2833        C      3\n",
       "2         1       3  female  26.0      0      0   7.9250        S      2\n",
       "3         1       1  female  35.0      1      0  53.1000        S      3\n",
       "4         0       3    male  35.0      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
      "0         0       3    0  22.0      1      0   7.2500        S      1\n",
      "1         1       1    1  38.0      1      0  71.2833        C      3\n",
      "2         1       3    1  26.0      0      0   7.9250        S      2\n",
      "3         1       1    1  35.0      1      0  53.1000        S      3\n",
      "4         0       3    0  35.0      0      0   8.0500        S      1\n"
     ]
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0}).astype(int)\n",
    "\n",
    "print(train_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   PassengerId  Pclass  Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
      "0          892       3    0  34.5      0      0   7.8292        Q      1\n",
      "1          893       3    1  47.0      1      0   7.0000        S      3\n",
      "2          894       2    0  62.0      0      0   9.6875        Q      1\n",
      "3          895       3    0  27.0      0      0   8.6625        S      1\n",
      "4          896       3    1  22.0      1      1  12.2875        S      3\n"
     ]
    }
   ],
   "source": [
    "print (test_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a296a9f50>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, row='Pclass', col='Sex', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guess_ages = np.zeros((2,3))\n",
    "guess_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0   22      1      0   7.2500        S      1\n",
       "1         1       1    1   38      1      0  71.2833        C      3\n",
       "2         1       3    1   26      0      0   7.9250        S      2\n",
       "3         1       1    1   35      1      0  53.1000        S      3\n",
       "4         0       3    0   35      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            guess_df = dataset[(dataset['Sex'] == i) & \\\n",
    "                                  (dataset['Pclass'] == j+1)]['Age'].dropna()\n",
    "\n",
    "            # age_mean = guess_df.mean()\n",
    "            # age_std = guess_df.std()\n",
    "            # age_guess = rnd.uniform(age_mean - age_std, age_mean + age_std)\n",
    "\n",
    "            age_guess = guess_df.median()\n",
    "\n",
    "            # Convert random age float to nearest .5 age\n",
    "            guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5\n",
    "            \n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            dataset.loc[ (dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1),\\\n",
    "                    'Age'] = guess_ages[i,j]\n",
    "\n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AgeBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>(-0.08, 16.0]</td>\n",
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       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "      <td>0.337374</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
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       "      <td>0.412037</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>(48.0, 64.0]</td>\n",
       "      <td>0.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>(64.0, 80.0]</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         AgeBand  Survived\n",
       "0  (-0.08, 16.0]  0.550000\n",
       "1   (16.0, 32.0]  0.337374\n",
       "2   (32.0, 48.0]  0.412037\n",
       "3   (48.0, 64.0]  0.434783\n",
       "4   (64.0, 80.0]  0.090909"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['AgeBand'] = pd.cut(train_df['Age'], 5)\n",
    "train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title  \\\n",
       "0         0       3    0    1      1      0   7.2500        S      1   \n",
       "1         1       1    1    2      1      0  71.2833        C      3   \n",
       "2         1       3    1    1      0      0   7.9250        S      2   \n",
       "3         1       1    1    2      1      0  53.1000        S      3   \n",
       "4         0       3    0    2      0      0   8.0500        S      1   \n",
       "\n",
       "        AgeBand  \n",
       "0  (16.0, 32.0]  \n",
       "1  (32.0, 48.0]  \n",
       "2  (16.0, 32.0]  \n",
       "3  (32.0, 48.0]  \n",
       "4  (32.0, 48.0]  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:    \n",
    "    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n",
    "    dataset.loc[ dataset['Age'] > 64, 'Age']\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
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       "   PassengerId  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
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     "execution_count": 152,
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   ],
   "source": [
    "train_df = train_df.drop(['AgeBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.head()\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
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       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
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     "execution_count": 153,
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  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
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       "   PassengerId  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0          892       3    0    2      0      0   7.8292        Q      1\n",
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   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in combine:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
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       "      <td>5</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FamilySize  Survived\n",
       "3           4  0.724138\n",
       "2           3  0.578431\n",
       "1           2  0.552795\n",
       "6           7  0.333333\n",
       "0           1  0.303538\n",
       "4           5  0.200000\n",
       "5           6  0.136364\n",
       "7           8  0.000000\n",
       "8          11  0.000000"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()\n",
    "train_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 10 columns):\n",
      "Survived      891 non-null int64\n",
      "Pclass        891 non-null int64\n",
      "Sex           891 non-null int64\n",
      "Age           891 non-null int64\n",
      "SibSp         891 non-null int64\n",
      "Parch         891 non-null int64\n",
      "Fare          891 non-null float64\n",
      "Embarked      889 non-null object\n",
      "Title         891 non-null int64\n",
      "FamilySize    891 non-null int64\n",
      "dtypes: float64(1), int64(8), object(1)\n",
      "memory usage: 69.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     537\n",
       "2     161\n",
       "3     102\n",
       "4      29\n",
       "6      22\n",
       "5      15\n",
       "7      12\n",
       "11      7\n",
       "8       6\n",
       "Name: FamilySize, dtype: int64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['FamilySize'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in combine:\n",
    "    dataset['IsAlone'] = 0\n",
    "    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IsAlone  Survived\n",
       "0        0  0.505650\n",
       "1        1  0.303538"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()\n",
    "train_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "886    0\n",
       "887    0\n",
       "888    2\n",
       "889    0\n",
       "890    0\n",
       "Name: Parch, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropped_one = train_df['Parch']\n",
    "dropped_two = train_df['SibSp']\n",
    "dropped_three = train_df['FamilySize']\n",
    "dropped_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>Title</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title  \\\n",
       "0          892       3    0    2      0      0   7.8292        Q      1   \n",
       "1          893       3    1    2      1      0   7.0000        S      3   \n",
       "2          894       2    0    3      0      0   9.6875        Q      1   \n",
       "3          895       3    0    1      0      0   8.6625        S      1   \n",
       "4          896       3    1    1      1      1  12.2875        S      3   \n",
       "\n",
       "   FamilySize  IsAlone  \n",
       "0           1        1  \n",
       "1           2        0  \n",
       "2           1        1  \n",
       "3           1        1  \n",
       "4           3        0  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare Embarked  Title  IsAlone\n",
       "0         0       3    0    1   7.2500        S      1        0\n",
       "1         1       1    1    2  71.2833        C      3        0\n",
       "2         1       3    1    1   7.9250        S      2        1\n",
       "3         1       1    1    2  53.1000        S      3        0\n",
       "4         0       3    0    2   8.0500        S      1        1"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
    "test_df = test_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
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       "      <th>Age*Class</th>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age*Class  Age  Pclass\n",
       "0          3    1       3\n",
       "1          2    2       1\n",
       "2          3    1       3\n",
       "3          2    2       1\n",
       "4          6    2       3\n",
       "5          3    1       3\n",
       "6          3    3       1\n",
       "7          0    0       3\n",
       "8          3    1       3\n",
       "9          0    0       2"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Age*Class'] = dataset.Age * dataset.Pclass\n",
    "\n",
    "train_df.loc[:, ['Age*Class', 'Age', 'Pclass']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      382\n",
       "2      196\n",
       "0      100\n",
       "6       87\n",
       "1       59\n",
       "4       47\n",
       "9        9\n",
       "71       2\n",
       "65       2\n",
       "222      1\n",
       "210      1\n",
       "195      1\n",
       "140      1\n",
       "132      1\n",
       "80       1\n",
       "70       1\n",
       "Name: Age*Class, dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Age*Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S'"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_port = train_df['Embarked'].dropna().mode()[0]\n",
    "freq_port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>0.339009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.339009"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)\n",
    "    \n",
    "train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
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       "      <td>2</td>\n",
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       "      <td>3</td>\n",
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       "      <td>3</td>\n",
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       "      <td>53.1000</td>\n",
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       "      <td>2</td>\n",
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       "      <td>3</td>\n",
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       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1   7.2500         0      1        0          3\n",
       "1         1       1    1    2  71.2833         1      3        0          2\n",
       "2         1       3    1    1   7.9250         0      2        1          3\n",
       "3         1       1    1    2  53.1000         0      3        0          2\n",
       "4         0       3    0    2   8.0500         0      1        1          6"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <td>0</td>\n",
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       "      <td>7.8292</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>896</td>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0          892       3    0    2   7.8292         2      1        1          6\n",
       "1          893       3    1    2   7.0000         0      3        0          6\n",
       "2          894       2    0    3   9.6875         2      1        1          6\n",
       "3          895       3    0    1   8.6625         0      1        1          3\n",
       "4          896       3    1    1  12.2875         0      3        0          3"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FareBand</th>\n",
       "      <th>Survived</th>\n",
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      ],
      "text/plain": [
       "          FareBand  Survived\n",
       "0   (-0.001, 7.91]  0.197309\n",
       "1   (7.91, 14.454]  0.303571\n",
       "2   (14.454, 31.0]  0.454955\n",
       "3  (31.0, 512.329]  0.581081"
      ]
     },
     "execution_count": 170,
     "metadata": {},
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    }
   ],
   "source": [
    "train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)\n",
    "train_df[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1     0         0      1        0          3\n",
       "1         1       1    1    2     3         1      3        0          2\n",
       "2         1       3    1    1     1         0      2        1          3\n",
       "3         1       1    1    2     3         0      3        0          2\n",
       "4         0       3    0    2     1         0      1        1          6\n",
       "5         0       3    0    1     1         2      1        1          3\n",
       "6         0       1    0    3     3         0      1        1          3\n",
       "7         0       3    0    0     2         0      4        0          0\n",
       "8         1       3    1    1     1         0      3        0          3\n",
       "9         1       2    1    0     2         1      3        0          0"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0\n",
    "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n",
    "    dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)\n",
    "\n",
    "train_df = train_df.drop(['FareBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "    \n",
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>899</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0          892       3    0    2     0         2      1        1          6\n",
       "1          893       3    1    2     0         0      3        0          6\n",
       "2          894       2    0    3     1         2      1        1          6\n",
       "3          895       3    0    1     1         0      1        1          3\n",
       "4          896       3    1    1     1         0      3        0          3\n",
       "5          897       3    0    0     1         0      1        1          0\n",
       "6          898       3    1    1     0         2      2        1          3\n",
       "7          899       2    0    1     2         0      1        0          2\n",
       "8          900       3    1    1     0         1      3        1          3\n",
       "9          901       3    0    1     2         0      1        0          3"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy_df=train_df.copy()\n",
    "copyTest_df=test_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_Embarked = copy_df[\"Embarked\"].values.reshape(-1,1)\n",
    "\n",
    "test_Embarked = copyTest_df[\"Embarked\"].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "onehot_encoder = OneHotEncoder(sparse=False,categories='auto')\n",
    "train_OneHotEncoded = onehot_encoder.fit_transform(train_Embarked)\n",
    "test_OneHotEncoded = onehot_encoder.fit_transform(test_Embarked)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy_df[\"EmbarkedS\"] = train_OneHotEncoded[:,0]\n",
    "copy_df[\"EmbarkedC\"] = train_OneHotEncoded[:,1]\n",
    "copy_df[\"EmbarkedQ\"] = train_OneHotEncoded[:,2]\n",
    "copyTest_df[\"EmbarkedS\"] = test_OneHotEncoded[:,0]\n",
    "copyTest_df[\"EmbarkedC\"] = test_OneHotEncoded[:,1]\n",
    "copyTest_df[\"EmbarkedQ\"] = test_OneHotEncoded[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class  \\\n",
       "0         0       3    0    1     0         0      1        0          3   \n",
       "1         1       1    1    2     3         1      3        0          2   \n",
       "2         1       3    1    1     1         0      2        1          3   \n",
       "3         1       1    1    2     3         0      3        0          2   \n",
       "4         0       3    0    2     1         0      1        1          6   \n",
       "\n",
       "   EmbarkedS  EmbarkedC  EmbarkedQ  \n",
       "0        1.0        0.0        0.0  \n",
       "1        0.0        1.0        0.0  \n",
       "2        1.0        0.0        0.0  \n",
       "3        1.0        0.0        0.0  \n",
       "4        1.0        0.0        0.0  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class  \\\n",
       "0          892       3    0    2     0         2      1        1          6   \n",
       "1          893       3    1    2     0         0      3        0          6   \n",
       "2          894       2    0    3     1         2      1        1          6   \n",
       "3          895       3    0    1     1         0      1        1          3   \n",
       "4          896       3    1    1     1         0      3        0          3   \n",
       "\n",
       "   EmbarkedS  EmbarkedC  EmbarkedQ  \n",
       "0        0.0        0.0        1.0  \n",
       "1        1.0        0.0        0.0  \n",
       "2        0.0        0.0        1.0  \n",
       "3        1.0        0.0        0.0  \n",
       "4        1.0        0.0        0.0  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copyTest_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>6</td>\n",
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      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1     0         0      1        0          3\n",
       "1         1       1    1    2     3         1      3        0          2\n",
       "2         1       3    1    1     1         0      2        1          3\n",
       "3         1       1    1    2     3         0      3        0          2\n",
       "4         0       3    0    2     1         0      1        1          6"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0          892       3    0    2     0         2      1        1          6\n",
       "1          893       3    1    2     0         0      3        0          6\n",
       "2          894       2    0    3     1         2      1        1          6\n",
       "3          895       3    0    1     1         0      1        1          3\n",
       "4          896       3    1    1     1         0      3        0          3"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
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       "   Pclass  Sex  Age  Fare  Title  IsAlone  Age*Class  EmbarkedS  EmbarkedC  \\\n",
       "0       3    0    1     0      1        0          3        1.0        0.0   \n",
       "1       1    1    2     3      3        0          2        0.0        1.0   \n",
       "2       3    1    1     1      2        1          3        1.0        0.0   \n",
       "3       1    1    2     3      3        0          2        1.0        0.0   \n",
       "4       3    0    2     1      1        1          6        1.0        0.0   \n",
       "\n",
       "   EmbarkedQ  \n",
       "0        0.0  \n",
       "1        0.0  \n",
       "2        0.0  \n",
       "3        0.0  \n",
       "4        0.0  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "X_trainTest = copy_df.drop(copy_df.columns[[0,5]],axis=1)\n",
    "Y_trainTest = copy_df[\"Survived\"]\n",
    "X_testTest = copyTest_df.drop(copyTest_df.columns[[0,5]],axis=1)\n",
    "X_trainTest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Pclass  Sex  Age  Fare  Title  IsAlone  Age*Class  EmbarkedS  EmbarkedC  \\\n",
       "0       3    0    2     0      1        1          6        0.0        0.0   \n",
       "1       3    1    2     0      3        0          6        1.0        0.0   \n",
       "2       2    0    3     1      1        1          6        0.0        0.0   \n",
       "3       3    0    1     1      1        1          3        1.0        0.0   \n",
       "4       3    1    1     1      3        0          3        1.0        0.0   \n",
       "\n",
       "   EmbarkedQ  \n",
       "0        1.0  \n",
       "1        0.0  \n",
       "2        1.0  \n",
       "3        0.0  \n",
       "4        0.0  "
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "X_testTest.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
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     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "logReg = LogisticRegression(solver='lbfgs')\n",
    "logReg.fit(X_trainTest,Y_trainTest)\n",
    "acc = logReg.score(X_trainTest,Y_trainTest)\n",
    "acc"
   ]
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  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0       3    0    1     0         0      1        0          3\n",
       "1       1    1    2     3         1      3        0          2\n",
       "2       3    1    1     1         0      2        1          3\n",
       "3       1    1    2     3         0      3        0          2\n",
       "4       3    0    2     1         0      1        1          6"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train_df.drop(\"Survived\", axis=1)\n",
    "Y_train = train_df[\"Survived\"]\n",
    "X_test  = test_df.drop(\"PassengerId\", axis=1).copy()\n",
    "X_train.shape, Y_train.shape, X_test.shape\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
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       "   Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0       3    0    2     0         2      1        1          6\n",
       "1       3    1    2     0         0      3        0          6\n",
       "2       2    0    3     1         2      1        1          6\n",
       "3       3    0    1     1         0      1        1          3\n",
       "4       3    1    1     1         0      3        0          3"
      ]
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     "execution_count": 186,
     "metadata": {},
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    "X_test.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.36"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg = LogisticRegression(solver='lbfgs')\n",
    "logreg.fit(X_train, Y_train)\n",
    "Y_pred = logreg.predict(X_test)\n",
    "acc_log = round(logreg.score(X_train, Y_train) * 100, 2)\n",
    "acc_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Age*Class</td>\n",
       "      <td>-0.311069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Pclass</td>\n",
       "      <td>-0.750700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Feature  Correlation\n",
       "1        Sex     2.201619\n",
       "5      Title     0.397888\n",
       "2        Age     0.287011\n",
       "4   Embarked     0.261473\n",
       "6    IsAlone     0.126553\n",
       "3       Fare    -0.086655\n",
       "7  Age*Class    -0.311069\n",
       "0     Pclass    -0.750700"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(train_df.columns.delete(0))\n",
    "coeff_df.columns = ['Feature']\n",
    "coeff_df[\"Correlation\"] = pd.Series(logreg.coef_[0])\n",
    "\n",
    "coeff_df.sort_values(by='Correlation', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8383838383838383"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svcTest = SVC(gamma='auto')\n",
    "svcTest.fit(X_trainTest, Y_trainTest)\n",
    "acc_svcTest = svcTest.score(X_trainTest, Y_trainTest)\n",
    "acc_svcTest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.84"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Support Vector Machines\n",
    "\n",
    "svc = SVC(gamma='auto')\n",
    "svc.fit(X_train, Y_train)\n",
    "Y_pred = svc.predict(X_test)\n",
    "acc_svc = round(svc.score(X_train, Y_train) * 100, 2)\n",
    "acc_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.74"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train, Y_train)\n",
    "Y_pred = knn.predict(X_test)\n",
    "acc_knn = round(knn.score(X_train, Y_train) * 100, 2)\n",
    "acc_knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72.28"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gaussian Naive Bayes\n",
    "\n",
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X_train, Y_train)\n",
    "Y_pred = gaussian.predict(X_test)\n",
    "acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)\n",
    "acc_gaussian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.34"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perceptron\n",
    "\n",
    "perceptron = Perceptron()\n",
    "perceptron.fit(X_train, Y_train)\n",
    "Y_pred = perceptron.predict(X_test)\n",
    "acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)\n",
    "acc_perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "79.12"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear SVC\n",
    "\n",
    "linear_svc = LinearSVC()\n",
    "linear_svc.fit(X_train, Y_train)\n",
    "Y_pred = linear_svc.predict(X_test)\n",
    "acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)\n",
    "acc_linear_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76.77"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stochastic Gradient Descent\n",
    "\n",
    "sgd = SGDClassifier()\n",
    "sgd.fit(X_train, Y_train)\n",
    "Y_pred = sgd.predict(X_test)\n",
    "acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)\n",
    "acc_sgd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.76"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree\n",
    "\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train, Y_train)\n",
    "Y_pred = decision_tree.predict(X_test)\n",
    "acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)\n",
    "acc_decision_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.76"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forestTest = RandomForestClassifier(n_estimators=100)\n",
    "random_forestTest.fit(X_trainTest, Y_trainTest)\n",
    "Y_predTest = random_forestTest.predict(X_testTest)\n",
    "acc_random_forestTest = round(random_forestTest.score(X_trainTest, Y_trainTest) * 100, 2)\n",
    "acc_random_forestTest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.76"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest\n",
    "\n",
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_pred = random_forest.predict(X_test)\n",
    "random_forest.score(X_train, Y_train)\n",
    "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n",
    "acc_random_forest\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>86.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>86.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>KNN</td>\n",
       "      <td>84.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>83.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>80.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>79.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Perceptron</td>\n",
       "      <td>78.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Stochastic Gradient Decent</td>\n",
       "      <td>76.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>72.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model  Score\n",
       "3               Random Forest  86.76\n",
       "8               Decision Tree  86.76\n",
       "1                         KNN  84.74\n",
       "0     Support Vector Machines  83.84\n",
       "2         Logistic Regression  80.36\n",
       "7                  Linear SVC  79.12\n",
       "5                  Perceptron  78.34\n",
       "6  Stochastic Gradient Decent  76.77\n",
       "4                 Naive Bayes  72.28"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n",
    "              'Random Forest', 'Naive Bayes', 'Perceptron', \n",
    "              'Stochastic Gradient Decent', 'Linear SVC', \n",
    "              'Decision Tree'],\n",
    "    'Score': [acc_svc, acc_knn, acc_log, \n",
    "              acc_random_forest, acc_gaussian, acc_perceptron, \n",
    "              acc_sgd, acc_linear_svc, acc_decision_tree]})\n",
    "models.sort_values(by='Score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 2)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": copyTest_df[\"PassengerId\"],\n",
    "        \"Survived\": Y_predTest\n",
    "    })\n",
    "submission.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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