Titanic EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "%pylab inline\n",
    "\n",
    "data = pd.read_csv('D:\\Work\\dataset_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trying to do EXPLORATORY DATA ANALYSIS\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# column based counting with only column name &&& categorical counting\n",
    "data.Sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe068898>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V1X0L2o5OpZJVZFJ3iurseCDJG4BzklwCXAd8cso1SQAPJ/kJugFGkl9kdJOhJsjr0Hss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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdd06128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot for male ,female sex\n",
    "data.Sex.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Survived.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xde805f8>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+GPinJIu8e82cW4GdwGdnNitpZI7RlVZ/tGJ/gH/74KezcRj0Hqqq55J8gtFVLZf/UvSV\nqvrx7GYmAfB14Oaqen3lJ5IMP/jpbByuoUtSI3yXiyQ1wqBLUiMMuiQ1wqBLUiP+H6/LJPZEVPEE\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe107ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot for survived/ dead counts\n",
    "data.Survived.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3838383838383838"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percentage of poeple who survived the crash out of total passenger list\n",
    "#placing \".00\" after digit to force computer to show float value \n",
    "342/891.00"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Isham, Miss. Ann Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17595</td>\n",
       "      <td>28.7125</td>\n",
       "      <td>C49</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>260</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Parrish, Mrs. (Lutie Davis)</td>\n",
       "      <td>female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Baxter, Mrs. James (Helene DeLaudeniere Chaput)</td>\n",
       "      <td>female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17558</td>\n",
       "      <td>247.5208</td>\n",
       "      <td>B58 B60</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>435</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Silvey, Mr. William Baird</td>\n",
       "      <td>male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13507</td>\n",
       "      <td>55.9000</td>\n",
       "      <td>E44</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>459</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Toomey, Miss. Ellen</td>\n",
       "      <td>female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>F.C.C. 13531</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>483</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rouse, Mr. Richard Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 3594</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>527</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Ridsdale, Miss. Lucy</td>\n",
       "      <td>female</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>W./C. 14258</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>545</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Douglas, Mr. Walter Donald</td>\n",
       "      <td>male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17761</td>\n",
       "      <td>106.4250</td>\n",
       "      <td>C86</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>661</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Frauenthal, Dr. Henry William</td>\n",
       "      <td>male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17611</td>\n",
       "      <td>133.6500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>724</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Hodges, Mr. Henry Price</td>\n",
       "      <td>male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250643</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "177          178         0       1   \n",
       "259          260         1       2   \n",
       "299          300         1       1   \n",
       "434          435         0       1   \n",
       "458          459         1       2   \n",
       "482          483         0       3   \n",
       "526          527         1       2   \n",
       "544          545         0       1   \n",
       "660          661         1       1   \n",
       "723          724         0       2   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "177                       Isham, Miss. Ann Elizabeth  female  50.0      0   \n",
       "259                      Parrish, Mrs. (Lutie Davis)  female  50.0      0   \n",
       "299  Baxter, Mrs. James (Helene DeLaudeniere Chaput)  female  50.0      0   \n",
       "434                        Silvey, Mr. William Baird    male  50.0      1   \n",
       "458                              Toomey, Miss. Ellen  female  50.0      0   \n",
       "482                         Rouse, Mr. Richard Henry    male  50.0      0   \n",
       "526                             Ridsdale, Miss. Lucy  female  50.0      0   \n",
       "544                       Douglas, Mr. Walter Donald    male  50.0      1   \n",
       "660                    Frauenthal, Dr. Henry William    male  50.0      2   \n",
       "723                          Hodges, Mr. Henry Price    male  50.0      0   \n",
       "\n",
       "     Parch        Ticket      Fare    Cabin Embarked  \n",
       "177      0      PC 17595   28.7125      C49        C  \n",
       "259      1        230433   26.0000      NaN        S  \n",
       "299      1      PC 17558  247.5208  B58 B60        C  \n",
       "434      0         13507   55.9000      E44        S  \n",
       "458      0  F.C.C. 13531   10.5000      NaN        S  \n",
       "482      0      A/5 3594    8.0500      NaN        S  \n",
       "526      0   W./C. 14258   10.5000      NaN        S  \n",
       "544      0      PC 17761  106.4250      C86        C  \n",
       "660      0      PC 17611  133.6500      NaN        S  \n",
       "723      0        250643   13.0000      NaN        S  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as observed only 38 % people survived the crash\n",
    "\n",
    "#now slicing in pandas to display people of age 50\n",
    "data[data['Age']==50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n",
       "Index: []"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now checking any nul values in the column 'SEX'   by   .isnull()\n",
    "# this method can be used for any column\n",
    "data[data['Sex'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe210860>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RVXuSbAH2AEeAtd1j1SVJ86zXwKiq3cDVJ6h/HLj2JNtsAjb12S5J0unrdUpKkjQ9DAxJ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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe047518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Fare'].plot(kind='hist',bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    468\n",
       "1    109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combining two functions here\n",
    "# to see males who survived and dead and to get categorical count\n",
    "data[data['Sex'] =='male'].Survived.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    114\n",
       "0     44\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combining two conditions here + function from above\n",
    "# to see females who survived were less than 30 years age and to get categorical count\n",
    "data[(data.Age<=30)&(data.Sex=='female')].Survived.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    90\n",
       "1    19\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to see if old 40+ people were able to survive the crash\n",
    "# male\n",
    "data[(data.Age>=40)&(data.Sex=='male')].Survived.value_counts()\n",
    "# it is clear that older age men werent able to survive the crash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    42\n",
       "0    12\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#females\n",
    "data[(data.Age>=40)&(data.Sex=='female')].Survived.value_counts()\n",
    "# it is clear that older age females were able to survive the crash,since they exited the ship first\n",
    "#even older females survived the crash more than males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    38\n",
       "0    24\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Age<10].Survived.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </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": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MEDDLING WITH NaN DATA\n",
    "\n",
    "\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
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       "      <th>Embarked</th>\n",
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       "  </thead>\n",
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       "      <td>Moran, Mr. James</td>\n",
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       "      <td>Masselmani, Mrs. Fatima</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
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       "      <td>30</td>\n",
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       "      <td>Todoroff, Mr. Lalio</td>\n",
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       "      <th>31</th>\n",
       "      <td>32</td>\n",
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       "      <td>1</td>\n",
       "      <td>Spencer, Mrs. William Augustus (Marie Eugenie)</td>\n",
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       "      <td>1</td>\n",
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       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Glynn, Miss. Mary Agatha</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>335677</td>\n",
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       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mamee, Mr. Hanna</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2677</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kraeff, Mr. Theodor</td>\n",
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       "      <td>Rogers, Mr. William John</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>S.C./A.4. 23567</td>\n",
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       "      <td>47</td>\n",
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       "      <td>Lennon, Mr. Denis</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>48</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Samaan, Mr. Youssef</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2662</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Woolner, Mr. Hugh</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>19947</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>C52</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Stewart, Mr. Albert A</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>PC 17605</td>\n",
       "      <td>27.7208</td>\n",
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       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Moubarek, Master. Gerios</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2661</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "      <td>77</td>\n",
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       "      <td>3</td>\n",
       "      <td>Staneff, Mr. Ivan</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moutal, Mr. Rahamin Haim</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>374746</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>83</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McDermott, Miss. Brigdet Delia</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330932</td>\n",
       "      <td>7.7875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Slocovski, Mr. Selman Francis</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>8.0500</td>\n",
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       "      <td>96</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>374910</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
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       "      <td>Petroff, Mr. Pastcho (\"Pentcho\")</td>\n",
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       "      <td>0</td>\n",
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       "      <td>349215</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>107</th>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
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       "      <td>Moss, Mr. Albert Johan</td>\n",
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       "      <td>0</td>\n",
       "      <td>312991</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>110</td>\n",
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       "      <td>Moran, Miss. Bertha</td>\n",
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       "      <td>24.1500</td>\n",
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       "      <th>121</th>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
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       "      <td>Moore, Mr. Leonard Charles</td>\n",
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       "      <td>129</td>\n",
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       "      <th>140</th>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
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       "      <td>15.2458</td>\n",
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       "      <th>154</th>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Olsen, Mr. Ole Martin</td>\n",
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       "      <td>0</td>\n",
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       "      <td>7.3125</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>719</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>McEvoy, Mr. Michael</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36568</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>728</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mannion, Miss. Margareth</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36866</td>\n",
       "      <td>7.7375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>733</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Knight, Mr. Robert J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239855</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>739</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ivanoff, Mr. Kanio</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349201</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>740</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Nankoff, Mr. Minko</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349218</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>741</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hawksford, Mr. Walter James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16988</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>D45</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>761</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Garfirth, Mr. John</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>358585</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>767</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Brewe, Dr. Arthur Jackson</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112379</td>\n",
       "      <td>39.6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>769</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. Daniel J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>371110</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>774</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Elias, Mr. Dibo</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2674</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>777</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Tobin, Mr. Roger</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>383121</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>F38</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>779</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kilgannon, Mr. Thomas J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36865</td>\n",
       "      <td>7.7375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>784</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Mr. Andrew G</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>791</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Keane, Mr. Andrew \"Andy\"</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12460</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>793</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Stella Anna</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>793</th>\n",
       "      <td>794</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Hoyt, Mr. William Fisher</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17600</td>\n",
       "      <td>30.6958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>816</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fry, Mr. Richard</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112058</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>B102</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>826</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Flynn, Mr. John</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>368323</td>\n",
       "      <td>6.9500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>827</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Lam, Mr. Len</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1601</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>829</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McCormack, Mr. Thomas Joseph</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>367228</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>833</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saad, Mr. Amin</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2671</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>838</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sirota, Mr. Maurice</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>392092</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>840</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Marechal, Mr. Pierre</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11774</td>\n",
       "      <td>29.7000</td>\n",
       "      <td>C47</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>847</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Mr. Douglas Bullen</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>850</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Goldenberg, Mrs. Samuel L (Edwiga Grabowska)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17453</td>\n",
       "      <td>89.1042</td>\n",
       "      <td>C92</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>860</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Razi, Mr. Raihed</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2629</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>879</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\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>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "5              6         0       3   \n",
       "17            18         1       2   \n",
       "19            20         1       3   \n",
       "26            27         0       3   \n",
       "28            29         1       3   \n",
       "29            30         0       3   \n",
       "31            32         1       1   \n",
       "32            33         1       3   \n",
       "36            37         1       3   \n",
       "42            43         0       3   \n",
       "45            46         0       3   \n",
       "46            47         0       3   \n",
       "47            48         1       3   \n",
       "48            49         0       3   \n",
       "55            56         1       1   \n",
       "64            65         0       1   \n",
       "65            66         1       3   \n",
       "76            77         0       3   \n",
       "77            78         0       3   \n",
       "82            83         1       3   \n",
       "87            88         0       3   \n",
       "95            96         0       3   \n",
       "101          102         0       3   \n",
       "107          108         1       3   \n",
       "109          110         1       3   \n",
       "121          122         0       3   \n",
       "126          127         0       3   \n",
       "128          129         1       3   \n",
       "140          141         0       3   \n",
       "154          155         0       3   \n",
       "..           ...       ...     ...   \n",
       "718          719         0       3   \n",
       "727          728         1       3   \n",
       "732          733         0       2   \n",
       "738          739         0       3   \n",
       "739          740         0       3   \n",
       "740          741         1       1   \n",
       "760          761         0       3   \n",
       "766          767         0       1   \n",
       "768          769         0       3   \n",
       "773          774         0       3   \n",
       "776          777         0       3   \n",
       "778          779         0       3   \n",
       "783          784         0       3   \n",
       "790          791         0       3   \n",
       "792          793         0       3   \n",
       "793          794         0       1   \n",
       "815          816         0       1   \n",
       "825          826         0       3   \n",
       "826          827         0       3   \n",
       "828          829         1       3   \n",
       "832          833         0       3   \n",
       "837          838         0       3   \n",
       "839          840         1       1   \n",
       "846          847         0       3   \n",
       "849          850         1       1   \n",
       "859          860         0       3   \n",
       "863          864         0       3   \n",
       "868          869         0       3   \n",
       "878          879         0       3   \n",
       "888          889         0       3   \n",
       "\n",
       "                                               Name     Sex  Age  SibSp  \\\n",
       "5                                  Moran, Mr. James    male  NaN      0   \n",
       "17                     Williams, Mr. Charles Eugene    male  NaN      0   \n",
       "19                          Masselmani, Mrs. Fatima  female  NaN      0   \n",
       "26                          Emir, Mr. Farred Chehab    male  NaN      0   \n",
       "28                    O'Dwyer, Miss. Ellen \"Nellie\"  female  NaN      0   \n",
       "29                              Todoroff, Mr. Lalio    male  NaN      0   \n",
       "31   Spencer, Mrs. William Augustus (Marie Eugenie)  female  NaN      1   \n",
       "32                         Glynn, Miss. Mary Agatha  female  NaN      0   \n",
       "36                                 Mamee, Mr. Hanna    male  NaN      0   \n",
       "42                              Kraeff, Mr. Theodor    male  NaN      0   \n",
       "45                         Rogers, Mr. William John    male  NaN      0   \n",
       "46                                Lennon, Mr. Denis    male  NaN      1   \n",
       "47                        O'Driscoll, Miss. Bridget  female  NaN      0   \n",
       "48                              Samaan, Mr. Youssef    male  NaN      2   \n",
       "55                                Woolner, Mr. Hugh    male  NaN      0   \n",
       "64                            Stewart, Mr. Albert A    male  NaN      0   \n",
       "65                         Moubarek, Master. Gerios    male  NaN      1   \n",
       "76                                Staneff, Mr. Ivan    male  NaN      0   \n",
       "77                         Moutal, Mr. Rahamin Haim    male  NaN      0   \n",
       "82                   McDermott, Miss. Brigdet Delia  female  NaN      0   \n",
       "87                    Slocovski, Mr. Selman Francis    male  NaN      0   \n",
       "95                      Shorney, Mr. Charles Joseph    male  NaN      0   \n",
       "101                Petroff, Mr. Pastcho (\"Pentcho\")    male  NaN      0   \n",
       "107                          Moss, Mr. Albert Johan    male  NaN      0   \n",
       "109                             Moran, Miss. Bertha  female  NaN      1   \n",
       "121                      Moore, Mr. Leonard Charles    male  NaN      0   \n",
       "126                             McMahon, Mr. Martin    male  NaN      0   \n",
       "128                               Peter, Miss. Anna  female  NaN      1   \n",
       "140                   Boulos, Mrs. Joseph (Sultana)  female  NaN      0   \n",
       "154                           Olsen, Mr. Ole Martin    male  NaN      0   \n",
       "..                                              ...     ...  ...    ...   \n",
       "718                             McEvoy, Mr. Michael    male  NaN      0   \n",
       "727                        Mannion, Miss. Margareth  female  NaN      0   \n",
       "732                            Knight, Mr. Robert J    male  NaN      0   \n",
       "738                              Ivanoff, Mr. Kanio    male  NaN      0   \n",
       "739                              Nankoff, Mr. Minko    male  NaN      0   \n",
       "740                     Hawksford, Mr. Walter James    male  NaN      0   \n",
       "760                              Garfirth, Mr. John    male  NaN      0   \n",
       "766                       Brewe, Dr. Arthur Jackson    male  NaN      0   \n",
       "768                             Moran, Mr. Daniel J    male  NaN      1   \n",
       "773                                 Elias, Mr. Dibo    male  NaN      0   \n",
       "776                                Tobin, Mr. Roger    male  NaN      0   \n",
       "778                         Kilgannon, Mr. Thomas J    male  NaN      0   \n",
       "783                          Johnston, Mr. Andrew G    male  NaN      1   \n",
       "790                        Keane, Mr. Andrew \"Andy\"    male  NaN      0   \n",
       "792                         Sage, Miss. Stella Anna  female  NaN      8   \n",
       "793                        Hoyt, Mr. William Fisher    male  NaN      0   \n",
       "815                                Fry, Mr. Richard    male  NaN      0   \n",
       "825                                 Flynn, Mr. John    male  NaN      0   \n",
       "826                                    Lam, Mr. Len    male  NaN      0   \n",
       "828                    McCormack, Mr. Thomas Joseph    male  NaN      0   \n",
       "832                                  Saad, Mr. Amin    male  NaN      0   \n",
       "837                             Sirota, Mr. Maurice    male  NaN      0   \n",
       "839                            Marechal, Mr. Pierre    male  NaN      0   \n",
       "846                        Sage, Mr. Douglas Bullen    male  NaN      8   \n",
       "849    Goldenberg, Mrs. Samuel L (Edwiga Grabowska)  female  NaN      1   \n",
       "859                                Razi, Mr. Raihed    male  NaN      0   \n",
       "863               Sage, Miss. Dorothy Edith \"Dolly\"  female  NaN      8   \n",
       "868                     van Melkebeke, Mr. Philemon    male  NaN      0   \n",
       "878                              Laleff, Mr. Kristo    male  NaN      0   \n",
       "888        Johnston, Miss. Catherine Helen \"Carrie\"  female  NaN      1   \n",
       "\n",
       "     Parch           Ticket      Fare  Cabin Embarked  \n",
       "5        0           330877    8.4583    NaN        Q  \n",
       "17       0           244373   13.0000    NaN        S  \n",
       "19       0             2649    7.2250    NaN        C  \n",
       "26       0             2631    7.2250    NaN        C  \n",
       "28       0           330959    7.8792    NaN        Q  \n",
       "29       0           349216    7.8958    NaN        S  \n",
       "31       0         PC 17569  146.5208    B78        C  \n",
       "32       0           335677    7.7500    NaN        Q  \n",
       "36       0             2677    7.2292    NaN        C  \n",
       "42       0           349253    7.8958    NaN        C  \n",
       "45       0  S.C./A.4. 23567    8.0500    NaN        S  \n",
       "46       0           370371   15.5000    NaN        Q  \n",
       "47       0            14311    7.7500    NaN        Q  \n",
       "48       0             2662   21.6792    NaN        C  \n",
       "55       0            19947   35.5000    C52        S  \n",
       "64       0         PC 17605   27.7208    NaN        C  \n",
       "65       1             2661   15.2458    NaN        C  \n",
       "76       0           349208    7.8958    NaN        S  \n",
       "77       0           374746    8.0500    NaN        S  \n",
       "82       0           330932    7.7875    NaN        Q  \n",
       "87       0  SOTON/OQ 392086    8.0500    NaN        S  \n",
       "95       0           374910    8.0500    NaN        S  \n",
       "101      0           349215    7.8958    NaN        S  \n",
       "107      0           312991    7.7750    NaN        S  \n",
       "109      0           371110   24.1500    NaN        Q  \n",
       "121      0        A4. 54510    8.0500    NaN        S  \n",
       "126      0           370372    7.7500    NaN        Q  \n",
       "128      1             2668   22.3583  F E69        C  \n",
       "140      2             2678   15.2458    NaN        C  \n",
       "154      0        Fa 265302    7.3125    NaN        S  \n",
       "..     ...              ...       ...    ...      ...  \n",
       "718      0            36568   15.5000    NaN        Q  \n",
       "727      0            36866    7.7375    NaN        Q  \n",
       "732      0           239855    0.0000    NaN        S  \n",
       "738      0           349201    7.8958    NaN        S  \n",
       "739      0           349218    7.8958    NaN        S  \n",
       "740      0            16988   30.0000    D45        S  \n",
       "760      0           358585   14.5000    NaN        S  \n",
       "766      0           112379   39.6000    NaN        C  \n",
       "768      0           371110   24.1500    NaN        Q  \n",
       "773      0             2674    7.2250    NaN        C  \n",
       "776      0           383121    7.7500    F38        Q  \n",
       "778      0            36865    7.7375    NaN        Q  \n",
       "783      2       W./C. 6607   23.4500    NaN        S  \n",
       "790      0            12460    7.7500    NaN        Q  \n",
       "792      2         CA. 2343   69.5500    NaN        S  \n",
       "793      0         PC 17600   30.6958    NaN        C  \n",
       "815      0           112058    0.0000   B102        S  \n",
       "825      0           368323    6.9500    NaN        Q  \n",
       "826      0             1601   56.4958    NaN        S  \n",
       "828      0           367228    7.7500    NaN        Q  \n",
       "832      0             2671    7.2292    NaN        C  \n",
       "837      0           392092    8.0500    NaN        S  \n",
       "839      0            11774   29.7000    C47        C  \n",
       "846      2         CA. 2343   69.5500    NaN        S  \n",
       "849      0            17453   89.1042    C92        C  \n",
       "859      0             2629    7.2292    NaN        C  \n",
       "863      2         CA. 2343   69.5500    NaN        S  \n",
       "868      0           345777    9.5000    NaN        S  \n",
       "878      0           349217    7.8958    NaN        S  \n",
       "888      2       W./C. 6607   23.4500    NaN        S  \n",
       "\n",
       "[177 rows x 12 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Age.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Age.describe()\n",
    "data.Age.mean()\n",
    "# there are two way in pandas to find the MEAN of AGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "meanvalue = int(round(data.Age.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# going to fill all the NaN with this new mean value\n",
    "data.Age = data.Age.fillna(value= meanvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n",
       "Index: []"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Age.isnull()]\n",
    "#as seen below there aren't any NaN ( not a number )  row under Age,since replaced with mean value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now LINEAR REGRESSION prep\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = pd.DataFrame()\n",
    "\n",
    "x['sex'] = data.Sex\n",
    "\n",
    "for i in range(891):\n",
    "    if x.sex[i] == 'male':\n",
    "        x.sex[i] = 0\n",
    "    if x.sex[i] == 'female':\n",
    "        x.sex[i] = 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x['fare']=data.Fare\n",
    "x['age'] = data.Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>fare</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sex     fare   age\n",
       "0   0   7.2500  22.0\n",
       "1   1  71.2833  38.0\n",
       "2   1   7.9250  26.0\n",
       "3   1  53.1000  35.0\n",
       "4   0   8.0500  35.0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#x[x.embarked.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# syntax to drop elements that have NaN values\n",
    "#x = x.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#x[x.embarked.isnull()]\n",
    "# rows dropped with NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# SINCE embarked column isnt required ,i am dropping it \n",
    "#syntax -->  x.drop(['embarked'],axis=1)\n",
    "# axis 0 means row wise\n",
    "# axis 1 means column wise\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scalar = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>fare</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sex     fare   age\n",
       "0   0   7.2500  22.0\n",
       "1   1  71.2833  38.0\n",
       "2   1   7.9250  26.0\n",
       "3   1  53.1000  35.0\n",
       "4   0   8.0500  35.0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\panne\\Anaconda2\\envs\\gl-env\\lib\\site-packages\\ipykernel\\__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n"
     ]
    }
   ],
   "source": [
    "# trying to convert the age into INTeger\n",
    "for i in range(891):\n",
    "    x.age[i] = int(round(x.age[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>fare</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sex     fare   age\n",
       "0   0   7.2500  22.0\n",
       "1   1  71.2833  38.0\n",
       "2   1   7.9250  26.0\n",
       "3   1  53.1000  35.0\n",
       "4   0   8.0500  35.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#x = scalar.fit_transform(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived\n",
       "0         0\n",
       "1         1\n",
       "2         1\n",
       "3         1\n",
       "4         0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# creating Y \n",
    "y = pd.DataFrame()\n",
    "y['survived'] = data.Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.2,random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression(penalty = 'l2',C=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\panne\\Anaconda2\\envs\\gl-env\\lib\\site-packages\\sklearn\\utils\\validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.77653631284916202"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "for i in range(891):\n",
    "    a = pd.DataFrame()\n",
    "    a[i]=0\n",
    "\n",
    "accuracy_score(y_test,model.predict(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0,\n",
       "       1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1,\n",
       "       0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0,\n",
       "       0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0,\n",
       "       0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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