Titanic_passengers_Kaggle.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: pylab import has clobbered these variables: ['test']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import xgboost as xgb\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, VotingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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</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</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</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</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</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      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('train.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>22</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",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>38</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",
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       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
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       "      <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>0</td>\n",
       "      <td>35</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>1</td>\n",
       "      <td>35</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  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1   22      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0   38      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0   26      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0   35      1      0   \n",
       "4                           Allen, Mr. William Henry    1   35      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  \n",
       "0         A/5 21171   7.2500   NaN        S  \n",
       "1          PC 17599  71.2833   C85        C  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3            113803  53.1000  C123        S  \n",
       "4            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#work with categorial sex feature\n",
    "data['Sex'] = np.where(data['Sex']=='male',1,0)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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",
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       "      <th>0</th>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>38</td>\n",
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       "      <td>0</td>\n",
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       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>35</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>1</td>\n",
       "      <td>35</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  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1   22      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0   38      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0   26      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0   35      1      0   \n",
       "4                           Allen, Mr. William Henry    1   35      0      0   \n",
       "\n",
       "    Ticket     Fare Cabin Embarked  \n",
       "0    21171   7.2500   NaN        S  \n",
       "1    17599  71.2833   C85        C  \n",
       "2  3101282   7.9250   NaN        S  \n",
       "3   113803  53.1000  C123        S  \n",
       "4   373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(0,len(data)):\n",
    "    ticket = data.iloc[i,8].split()\n",
    "    try:\n",
    "        data.iloc[i,8] = int(ticket[-1])\n",
    "    except:\n",
    "        data.iloc[i,8] = 0\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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",
       "      <th>Pclass</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",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
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       "      <td>0</td>\n",
       "      <td>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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>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>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35</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>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35</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": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch   Ticket     Fare Cabin Embarked\n",
       "0         0       3    1   22      1      0    21171   7.2500   NaN        S\n",
       "1         1       1    0   38      1      0    17599  71.2833   C85        C\n",
       "2         1       3    0   26      0      0  3101282   7.9250   NaN        S\n",
       "3         1       1    0   35      1      0   113803  53.1000  C123        S\n",
       "4         0       3    1   35      0      0   373450   8.0500   NaN        S"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(['Name','PassengerId'],axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Ticket        0\n",
       "Fare          0\n",
       "Cabin       687\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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",
       "      <th>Pclass</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>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch   Ticket     Fare Embarked\n",
       "0         0       3    1   22      1      0    21171   7.2500        S\n",
       "1         1       1    0   38      1      0    17599  71.2833        C\n",
       "2         1       3    0   26      0      0  3101282   7.9250        S\n",
       "3         1       1    0   35      1      0   113803  53.1000        S\n",
       "4         0       3    1   35      0      0   373450   8.0500        S"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(['Cabin'], axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Q58+3FUL8qGROWZZ7A+gM4Hh+fr60cePGpw4cOKCLi4tTMgYLIdnZ2UhNTS08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\nzd5Ih7LSUzCPeVWkp5eTlTWPG26IncsD27dDVpbZSA8cCNddB9XV8MQ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WLbuY9u17MWuW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a06e9ef128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_pos = data[data['Survived']==1]\n",
    "data_neg = data[data['Survived']==0]\n",
    "plt.figure(figsize=(12, 12))\n",
    "plt.subplot(221)\n",
    "plt.pie([sum(data_pos['Sex'])/len(data_pos),(len(data_pos)-sum(data_pos['Sex']))/len(data_pos)],\n",
    "                                             labels=['Men', 'Women'], explode=[0, 0.3], autopct = '%1.1f%%', shadow=True,\n",
    "       colors=['white','cyan'])\n",
    "plt.title('Survived')\n",
    "plt.subplot(222)\n",
    "plt.pie([sum(data_neg['Sex'])/len(data_neg),(len(data_neg)-sum(data_neg['Sex']))/len(data_neg)],\n",
    "                                             labels=['Men', 'Women'], explode=[0, 0.3], autopct = '%1.1f%%', shadow=True,\n",
    "       colors=['white','cyan'])\n",
    "plt.title('Died')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CV14J4vPdasP2Ya8BnUXkCxG5U0QOb7z8m6Omu3/fDzjaGDMZeBw4AxJHYwNt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\nAJQi4AIAUIqACwBAKS/enP8VIq3kdAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a06ea0cf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_men = data[data['Sex']==1]\n",
    "data_women = data[data['Sex']==0]\n",
    "plt.figure(figsize=(12, 12))\n",
    "plt.subplot(221)\n",
    "plt.pie([sum(data_men['Survived'])/len(data_men),(len(data_men)-sum(data_men['Survived']))/len(data_men)],\n",
    "                                             labels=['Survived', 'Died'], explode=[0, 0.3], autopct = '%1.1f%%', shadow=True,\n",
    "       colors=['cyan','white'])\n",
    "plt.title('Men')\n",
    "plt.subplot(222)\n",
    "plt.pie([sum(data_women['Survived'])/len(data_women),(len(data_women)-sum(data_women['Survived']))/len(data_women)],\n",
    "                                             labels=['Survived', 'Died'], explode=[0, 0.3], autopct = '%1.1f%%', shadow=True,\n",
    "       colors=['cyan','white'])\n",
    "plt.title('Women')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AKxeUUJIkSRqxeZ9JrqrPJ/kYcAPwSPPf9wFHAFckuQDYAZyzGEElSZKkUVnIcguqaiPQ\nfs/xPoZLMSRJkqRlyW/ckyRJklosyZIkSVKLJVmSJElqsSRLkiRJLZZkSZIkqcWSLEmSJLVYkiVJ\nkqQWS7IkSZLUYkmWJEmSWizJkiRJUoslWZIkSWqxJEuSJEktlmRJkiSpxZIsSZIktViSJUmSpBZL\nsiRJktRiSZYkSZJaLMmSJElSiyVZkiRJarEkS5IkSS2WZElaQZIckuT6JFua7dVJrkpye5KpJEd2\nnVGSxoElWZJWlrcC22dsXwxsrarjgWuASzpJJUljxpIsSStEkmOBM4A/n7H7LGBTc3sTcPaoc0nS\nOLIkS9LK8R7g7UDN2DdRVdMAVbUTOKaLYJI0blZ1HUCStPSSvBaYrqobk/T2cWjt4z42bNiw53av\n16PX29dTSVI3+v0+/X5/Qc9hSZakleFk4MwkZwBPBo5IcjmwM8lEVU0nmQTu2deTzCzJkjSu2r/E\nb9y4cc7P4XILSVoBqurSqlpTVc8DzgWuqapfAD4FnN8cdh5wZUcRJWmsWJIlaWV7J/CqJLcDpzTb\nkrTiudxCklaYqvoM8Jnm9n3Aqd0mkqTx45lkSZIkqcWSLEmSJLVYkiVJkqQWS7IkSZLUYkmWJEmS\nWizJkiRJUouXgNOCDQY7WLt23cjGW7NmgqmpzSMbT5IkrTyWZC3Yrl1hcnLbyMYbDEZXyCVJ0srk\ncgtJkiSpxZIsSZIktViSJUmSpBbXJEuSDtj27dvZvXv3yMc9+uijecYznjHycSWtXAsqyUmOBP4c\n+GfAbuAC4EvAR4DjgDuBc6rqwYXFlCSNg0te/3qefeihIx3ze1Xcc+SRfPwznxnpuJJWtoWeSX4v\n8Omqen2SVcBTgEuBrVX17iQXAZcAFy9wHEnSGDjxiU9k/VFHjXTMh3bt4tyHHhrpmJI07zXJSZ4G\n/GRVXQZQVbuaM8ZnAZuawzYBZy84pSRJkjRCC/ng3nOBbyS5LMn1Sd6X5HBgoqqmAapqJ3DMYgSV\nJEmSRmUhJXkVcCLwJ1V1IvBthssqqnVce1uSJEkaawtZk/xV4K6quq7Z/jjDkjydZKKqppNMAvfM\n9gQbNmzYc7vX69Hr9RYQR5KWTr/fp9/vdx1DkjQi8y7JTQm+K8kLq+pLwCnALc3P+cC7gPOAK2d7\njpklWZLGWfsX+Y0bN3YXRpK05BZ6dYsLgQ8meSJwB/Bm4AnAFUkuAHYA5yxwDEmSJGmkFlSSq+qL\nwMv2ctepC3leSZIkqUt+LbUkSZLUYkmWJEmSWizJkiRJUoslWZIkSWqxJEuSJEktlmRJkiSpxZIs\nSZIktViSJUmSpBZLsiRJktRiSZYkSZJaLMmSJElSiyVZkiRJarEkS5IkSS2WZElaIZIcluTvktyQ\n5OYk65v9q5NcleT2JFNJjuw6qyR1zZIsSStEVX0XWFdVLwVeArwmyUnAxcDWqjoeuAa4pMOYkjQW\nLMmStIJU1T81Nw8DVgEFnAVsavZvAs7uIJokjRVLsiStIEkOSXIDsBO4uqquBSaqahqgqnYCx3SZ\nUZLGwaquA0iSRqeqdgMvTfI04BNJTmB4Nvkxh832+G07d1IPPABA76ij6K1evWRZJWm++v0+/X5/\nQc9hSZakFaiq/jFJHzgdmE4yUVXTSSaBe2Z73LrJSdZbjCWNuV6vR6/X27O9cePGOT+Hyy0kaYVI\n8gOPXrkiyZOBVwG3AluA85vDzgOu7CSgJI0RzyRL0srxDGBTkkMYniT5SFV9OsnngCuSXADsAM7p\nMqQkjQNLsiStEFV1M3DiXvbfB5w6+kSSNL5cbiFJkiS1WJIlSZKkFkuyJEmS1GJJliRJklosyZIk\nSVKLJVmSJElqsSRLkiRJLZZkSZIkqcWSLEmSJLVYkiVJkqQWS7IkSZLUYkmWJEmSWlZ1HUCaq8Fg\nB2vXrhvZeGvWTDA1tXlk40mSpO5ZkrXs7NoVJie3jWy8wWB0hVySJI0Hl1tIkiRJLZZkSZIkqcWS\nLEmSJLVYkiVJkqQWS7IkSZLUYkmWJEmSWhZckpMckuT6JFua7dVJrkpye5KpJEcuPKYkSZI0Ootx\nneS3AtuBpzXbFwNbq+rdSS4CLmn2SZIkzWrHYMC6tWtHPu7EmjVsnpoa+bgabwsqyUmOBc4Afg94\nW7P7LOCVze1NQB9LsiRJ2o/s2sW2ycmRj7tuMBj5mBp/C11u8R7g7UDN2DdRVdMAVbUTOGaBY0iS\nJEkjNe8zyUleC0xX1Y1Jevs4tGa7Y8OGDXtu93o9er19PY0kdaff79Pv97uOIUkakYUstzgZODPJ\nGcCTgSOSXA7sTDJRVdNJJoF7ZnuCmSVZksZZ+xf5jRs3dhdGkrTk5r3coqourao1VfU84Fzgmqr6\nBeBTwPnNYecBVy44pSRJkjRCS3Gd5HcCr0pyO3BKsy1JkiQtG4txCTiq6jPAZ5rb9wGnLsbzSpIk\nSV1YlJIsHcwGgx2sXbtuZOOtWTPB1NTmkY0nSZIez5Is7ceuXWFyctvIxhsMRlfIJUnS3i3FmmRJ\nkiRpWbMkS5IkSS2WZEmSJKnFNcmSJGlF2zEYsG7t2pGPO7FmDZunpkY+rg6MJVmSVogkxwIfACaA\n3cCfVdUfJVkNfAQ4DrgTOKeqHuwsqDRi2bWLbZOTIx933WAw8jF14FxuIUkrxy7gbVV1AvDjwFuS\n/DBwMbC1qo4HrgEu6TCjJI0FS7IkrRBVtbOqbmxuPwTcChwLnAVsag7bBJzdTUJJGh+WZElagZI8\nB3gJ8DlgoqqmYVikgWO6SyZJ48E1yZK0wiR5KvAx4K1V9VCSah3S3t5j286d1AMPANA76ih6q1cv\nXVBJmqd+v0+/31/Qc1iSJWkFSbKKYUG+vKqubHZPJ5moqukkk8A9sz1+3eQk6y3GksZcr9ej1+vt\n2d64ceOcn8PlFpK0svwFsL2q3jtj3xbg/Ob2ecCV7QdJ0krjmWRJWiGSnAy8Cbg5yQ0Ml1VcCrwL\nuCLJBcAO4JzuUkrSeLAkS9IKUVV/CzxhlrtPHWWWufrSl7/cyZc9ANw9GEAH19CV1C1LsiRp7O1+\n+OFOvuwB4Pl33NHJuJK65ZpkSZIkqcWSLEmSJLVYkiVJkqQWS7IkSZLUYkmWJEmSWizJkiRJUosl\nWZIkSWqxJEuSJEktlmRJkiSpxZIsSZIktViSJUmSpBZLsiRJktRiSZYkSZJaLMmSJElSiyVZkiRJ\narEkS5IkSS2WZEmSJKnFkixJkiS1WJIlSZKkFkuyJEmS1GJJliRJklosyZIkSVKLJVmSJElqsSRL\nkiRJLZZkSZIkqcWSLEmSJLXMuyQnOTbJNUluSXJzkgub/auTXJXk9iRTSY5cvLiSJEnS0lvImeRd\nwNuq6gTgx4G3JPlh4GJga1UdD1wDXLLwmJIkSdLozLskV9XOqrqxuf0QcCtwLHAWsKk5bBNw9kJD\nSpIkSaO0KGuSkzwHeAnwOWCiqqZhWKSBYxZjDEmSJGlUVi30CZI8FfgY8NaqeihJtQ5pb++xYcOG\nPbd7vR69Xm+hcSRpSfT7ffr9ftcxJEkjsqCSnGQVw4J8eVVd2eyeTjJRVdNJJoF7Znv8zJIsSeOs\n/Yv8xo0buwsjSVpyC11u8RfA9qp674x9W4Dzm9vnAVe2HyRJkiSNs4VcAu5k4E3ATyW5Icn1SU4H\n3gW8KsntwCnAOxcnqiRpIZK8P8l0kptm7POynZK0F/NeblFVfws8YZa7T53Lcz3yyCP89V//NVWz\nLl9eVEl4xStewWGHHTaS8SRpTFwG/GfgAzP2PXrZzncnuYjhZTsv7iKcJI2TBX9wbzFs27aNCy+8\njMMOO2Ek433nO7fyB3/wHV73uteNZDxJGgdV9dkkx7V2nwW8srm9CehjSZak8SjJVcVhh72Ipz99\nNN878s1v/v7IzlpL0pg7ZuZlO5N42U5JYkxKsiRpbOzzDMK2nTupBx4AoHfUUfRWrx5JKEmai8W4\nbKclWZJWtgO+bCfAuslJ1luMJY25xbhs56J8454kadlI8/MoL9spSXuxYs8kr1//Hn7rt/5gZOMN\nBnczOTmy4STpcZJ8COgBRycZAOsZXqbzo0kuAHYA53SXUJLGx4otyffe+y1e8IJrRzbeHXc8f2Rj\nSdLeVNUbZ7lrTpftlKSVYMWWZEmSpC7tGAxYt3btyMedWLOGzVNTIx93ubEkS2NmMNjB2rXrRjLW\nmjUTTE1tHslYkqTHyq5dbOtgLea6wWDkYy5HlmRpzOzaFSYnt41krMFgNGVckqTlxqtbSJIkSS2W\nZEmSJKnFkixJkiS1uCZZWsFG+SFB8IOCkqTlw5IsrWCj/JAg+EFBSdLy4XILSZIkqcWSLEmSJLVY\nkiVJkqQWS7IkSZLUYkmWJEmSWizJkiRJUoslWZIkSWqxJEuSJEktlmRJkiSpxZIsSZIktViSJUmS\npBZLsiRJktRiSZYkSZJaLMmSJElSiyVZkiRJarEkS5IkSS2rug4gaeUYDHawdu26kY23Zs0EU1Ob\nRzaeJOngYUmWNDK7doXJyW0jG28wGF0hlyQdXFxuIUmSJLVYkiVJkqQWS7IkSZLUYkmWJEmSWizJ\nkiRJUoslWZIkSWqxJEuSJEktlmRJkiSpxS8TkSRJ0kice9ppTA8GXcc4IEtWkpOcDvwhw7PV76+q\ndy3VWIvp/vv7rF7d6zrG44xzrnE1zq+ZuQ7cuOY62CzXORugf//99Fav7jrG45hrbvr33991hL0a\n59drLHP1+/R6vVnvnx4M2DY5ObpAjdx225wfsyTLLZIcAvwxcBpwAvCGJD+8FGMttgce6HcdYa/M\nNXfjms1cczOuuQ4my3nOBug/8EDXEfbKXHNjrrkZ21z9ftcRFs1SrUk+CfhyVe2oqkeAzcBZSzSW\nJGlhnLMlqWWplls8C7hrxvZXGU7Cew+xahUPP3w9Dzxw6RLFeayHH/7KSMaRpGXigOfsmx9+mEtH\nfAZr1+7d5BA/Zy5ptFJVi/+kyc8Cp1XVLzfbPw+cVFUXzjhm8QeWpBGqqnSdYTEcyJzd7HfelrRs\nzXXOXqozyXcDa2ZsH9vs2+Ng+T8XSToI7HfOBudtSSvLUr1/dS3wgiTHJTkUOBfYskRjSZIWxjlb\nklqW5ExyVX0vya8BV/H9ywnduhRjSZIWxjlbkh5vSdYkS5IkSctZJx8XTnJ6ktuSfCnJRV1kaHK8\nP8l0kptm7Fud5KoktyeZSnJkB7mOTXJNkluS3JzkwnHIluSwJH+X5IYm1/pxyDUj3yFJrk+yZVxy\nJbkzyReb1+zzY5TryCQfTXJr8+/sx7rOleSFzet0ffPfB5Nc2HWuJttvJPn7JDcl+WCSQ8ch16g4\nZ+83l3P2/PI5Zx94LufsuWVblDl75CU543XR+suaHDNdDGytquOBa4BLRp4KdgFvq6oTgB8H3tK8\nRp1mq6rvAuuq6qXAS4DXJDmp61wzvBXYPmN7HHLtBnpV9dKqevSSWuOQ673Ap6tqLfBi4Lauc1XV\nl5rX6UTgnwPfBj7Rda4kzwR+HTixql7EcJnaG7rONSrO2QfEOXt+nLMPnHP2AVrUObuqRvoDvBz4\nXzO2LwYuGnWOGeMfB9w0Y/s2YKK5PQnc1lW2GZk+CZw6TtmAw4HrgJeNQy6Gn8a/GugBW8bl7xL4\nB+Do1r5OcwFPA76yl/2dv14zsrwa+JtxyAU8E9gBrG4m2y3j9r/HJf7zO2fPPaNz9v7zOGcfeCbn\n7LllWbQ5u4vlFnu7aP2zOsgxm2OqahqgqnYCx3QZJslzGJ4B+BzDv9xOszVvj90A7ASurqprxyEX\n8B7g7cDMRfbjkKuAq5Ncm+QXxyTXc4FvJLmseZvsfUkOH4NcM/0c8KHmdqe5quprwO8DA4aXRXuw\nqrZ2nWuEnLPnwDn7gDlnHzjn7DlYzDnbrzDav84+2ZjkqcDHgLdW1UN7yTLybFW1u4Zv3R0LnJTk\nhK5zJXktMF1VNwL7uo5rF3+XJ9fwragzGL4F+5N7yTHqXKuAE4E/abJ9m+HZwa5zAZDkicCZwEdn\nyTHqf19HMfyK5uMYnqF4SpI3dZ1Ls3LOnjmgc/ZcOWfP0cE8Z3dRkg/oovUdmk4yAZBkErinixBJ\nVjGcbC/XDuTKAAABzklEQVSvqivHKRtAVf0j0AdOH4NcJwNnJrkD+DDwU0kuB3Z2/XpV1deb/97L\n8C3Yk+j+9foqcFdVXddsf5zhBNx1rke9BvhCVX2j2e4616nAHVV1X1V9j+Gau58Yg1yj4px9AJyz\n58Q5e26cs+dm0ebsLkryuF20Pjz2N9ktwPnN7fOAK9sPGJG/ALZX1Xtn7Os0W5IfePTToEmeDLwK\nuLXrXFV1aVWtqarnMfz3dE1V/QLwqS5zJTm8ObNEkqcwXLN1M92/XtPAXUle2Ow6Bbil61wzvIHh\n/3E+qutcA+DlSZ6UJAxfr+1jkGtUnLMPjHP2AXLOnhvn7DlbvDl7lIupZyyqPh24HfgycHEXGZoc\nHwK+Bny3eVHfzHCh99Ym31XAUR3kOhn4HnAjcANwffOaPb3LbMCPNlluBG4CfrvZ32muVsZX8v0P\ngXT9ej13xt/hzY/+W+86V5PhxQzLz43A/wCOHJNchwP3AkfM2DcOudYzLBc3AZuAJ45DrhH++Z2z\n953LOXv+GZ2zDyybc/bcci3KnO2XiUiSJEktfnBPkiRJarEkS5IkSS2WZEmSJKnFkixJkiS1WJIl\nSZKkFkuyJEmS1GJJliRJklr+P063uY8AoWe3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a06efefd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 12))\n",
    "plt.subplot(221)\n",
    "plt.hist(data_neg['Age'].dropna(), bins=10, color='b', alpha=0.75)\n",
    "plt.title('Died')\n",
    "plt.subplot(222)\n",
    "plt.hist(data_pos['Age'].dropna(), bins=10, color='r', alpha=0.75)\n",
    "plt.title('Survived')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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",
       "      <th>Pclass</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>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch   Ticket     Fare Embarked\n",
       "0         0       3    1   22      1      0    21171   7.2500        S\n",
       "1         1       1    0   38      1      0    17599  71.2833        C\n",
       "2         1       3    0   26      0      0  3101282   7.9250        S\n",
       "3         1       1    0   35      1      0   113803  53.1000        S\n",
       "4         0       3    1   35      0      0   373450   8.0500        S"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Выживших в выборке 38.38 %, погибших в выборке: 61.62 %\n"
     ]
    }
   ],
   "source": [
    "print('Выживших в выборке {:.2f} %, погибших в выборке: {:.2f} %'.format(sum(data.Survived)/len(data)*100,(1-sum(data.Survived)/len(data))*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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",
       "      <th>Pclass</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>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>20-30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30-40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>20-30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30-40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>30-40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived Pclass  Sex    Age  SibSp  Parch   Ticket     Fare Embarked\n",
       "0         0      3    1  20-30      1      0    21171   7.2500        S\n",
       "1         1      1    0  30-40      1      0    17599  71.2833        C\n",
       "2         1      3    0  20-30      0      0  3101282   7.9250        S\n",
       "3         1      1    0  30-40      1      0   113803  53.1000        S\n",
       "4         0      3    1  30-40      0      0   373450   8.0500        S"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's insert age categpries and try 3 variates nan's replacing\n",
    "for i in range(0,len(data)):\n",
    "    age = data.iloc[i,3]\n",
    "    if math.isnan(data.iloc[i,3]):\n",
    "        data.iloc[i,3] = 'none group'\n",
    "    elif int(age)<10:\n",
    "        data.iloc[i,3] = '<10'\n",
    "    elif 10<=int(age)<20:\n",
    "        data.iloc[i,3] = '10-20'\n",
    "    elif 20<=int(age)<30:\n",
    "        data.iloc[i,3] = '20-30'\n",
    "    elif 30<=int(age)<40:\n",
    "        data.iloc[i,3] = '30-40'\n",
    "    elif 40<=int(age)<50:\n",
    "        data.iloc[i,3] = '40-50'\n",
    "    elif 50<=int(age)<60:\n",
    "        data.iloc[i,3] = '50-60'\n",
    "    elif 60<=int(age)<70:\n",
    "        data.iloc[i,3] = '60-70'\n",
    "    elif 70<=int(age)<80:\n",
    "        data.iloc[i,3] = '70-80'\n",
    "    elif 80<=int(age)<90:\n",
    "        data.iloc[i,3] = '80-90'\n",
    "    elif int(age)>90:\n",
    "        data.iloc[i,3] = '>90'\n",
    "    data.iloc[i,1] = str(data.iloc[i,1])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    491\n",
       "1    216\n",
       "2    184\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Pclass'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived    0\n",
      "Pclass      0\n",
      "Sex         0\n",
      "Age         0\n",
      "SibSp       0\n",
      "Parch       0\n",
      "Ticket      0\n",
      "Fare        0\n",
      "Embarked    2\n",
      "dtype: int64\n",
      "891\n"
     ]
    }
   ],
   "source": [
    "print(data.isnull().sum())\n",
    "print(len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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",
       "      <th>Pclass</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>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30-40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>60-70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived Pclass  Sex    Age  SibSp  Parch  Ticket  Fare Embarked\n",
       "61          1      1    0  30-40      0      0  113572    80      NaN\n",
       "829         1      1    0  60-70      0      0  113572    80      NaN"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Embarked nan replacing\n",
    "data[data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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",
       "      <th>Sex</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>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.383838</td>\n",
       "      <td>0.647587</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>0.486592</td>\n",
       "      <td>0.477990</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>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>1.000000</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>1.000000</td>\n",
       "      <td>1.000000</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>1.000000</td>\n",
       "      <td>1.000000</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": [
       "         Survived         Sex       SibSp       Parch        Fare\n",
       "count  891.000000  891.000000  891.000000  891.000000  891.000000\n",
       "mean     0.383838    0.647587    0.523008    0.381594   32.204208\n",
       "std      0.486592    0.477990    1.102743    0.806057   49.693429\n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    0.000000    0.000000    0.000000    7.910400\n",
       "50%      0.000000    1.000000    0.000000    0.000000   14.454200\n",
       "75%      1.000000    1.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    1.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.iloc[61,8]=data[(data['Pclass']=='1') & (data['Fare']>60) & (data['Fare']<100) & (data['Age']=='30-40')]['Embarked'].value_counts().index[0]\n",
    "data.iloc[829,8]=data[(data['Pclass']=='1') & (data['Fare']>60) & (data['Fare']<100) & (data['Age']=='60-70')]['Embarked'].value_counts().index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived    0\n",
      "Pclass      0\n",
      "Sex         0\n",
      "Age         0\n",
      "SibSp       0\n",
      "Parch       0\n",
      "Ticket      0\n",
      "Fare        0\n",
      "Embarked    0\n",
      "dtype: int64\n",
      "891\n"
     ]
    }
   ],
   "source": [
    "print(data.isnull().sum())\n",
    "print(len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Рабочий софт\\Анаконда\\lib\\site-packages\\sklearn\\preprocessing\\data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "E:\\Рабочий софт\\Анаконда\\lib\\site-packages\\sklearn\\preprocessing\\data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age=10-20</th>\n",
       "      <th>Age=20-30</th>\n",
       "      <th>Age=30-40</th>\n",
       "      <th>Age=40-50</th>\n",
       "      <th>Age=50-60</th>\n",
       "      <th>Age=60-70</th>\n",
       "      <th>Age=70-80</th>\n",
       "      <th>Age=80-90</th>\n",
       "      <th>Age=&lt;10</th>\n",
       "      <th>Age=none group</th>\n",
       "      <th>Embarked=C</th>\n",
       "      <th>Embarked=Q</th>\n",
       "      <th>Embarked=S</th>\n",
       "      <th>Pclass=1</th>\n",
       "      <th>Pclass=2</th>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age=10-20  Age=20-30  Age=30-40  Age=40-50  Age=50-60  Age=60-70  \\\n",
       "0          0          1          0          0          0          0   \n",
       "1          0          0          1          0          0          0   \n",
       "2          0          1          0          0          0          0   \n",
       "3          0          0          1          0          0          0   \n",
       "4          0          0          1          0          0          0   \n",
       "\n",
       "   Age=70-80  Age=80-90  Age=<10  Age=none group  Embarked=C  Embarked=Q  \\\n",
       "0          0          0        0               0           0           0   \n",
       "1          0          0        0               0           1           0   \n",
       "2          0          0        0               0           0           0   \n",
       "3          0          0        0               0           0           0   \n",
       "4          0          0        0               0           0           0   \n",
       "\n",
       "   Embarked=S  Pclass=1  Pclass=2  Pclass=3  \n",
       "0           1         0         0         1  \n",
       "1           0         1         0         0  \n",
       "2           1         0         0         1  \n",
       "3           1         1         0         0  \n",
       "4           1         0         0         1  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder=DictVectorizer(sparse=False).fit(data[['Age','Embarked','Pclass']].T.to_dict().values())\n",
    "data_cat = encoder.transform(data[['Age','Embarked','Pclass']].T.to_dict().values())\n",
    "scaler = StandardScaler().fit(data['Fare'])\n",
    "data_real = scaler.transform(data['Fare'])\n",
    "data_cat = pd.DataFrame(data_cat, columns=[encoder.feature_names_])\n",
    "data_real = pd.DataFrame(data_real,columns=['Fare'])\n",
    "data_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>Age=50-60</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.786845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3101282</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.488854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.420730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.486337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Sex  SibSp  Parch   Ticket  Age=10-20  Age=20-30  Age=30-40  \\\n",
       "0         0    1      1      0    21171          0          1          0   \n",
       "1         1    0      1      0    17599          0          0          1   \n",
       "2         1    0      0      0  3101282          0          1          0   \n",
       "3         1    0      1      0   113803          0          0          1   \n",
       "4         0    1      0      0   373450          0          0          1   \n",
       "\n",
       "   Age=40-50  Age=50-60    ...     Age=80-90  Age=<10  Age=none group  \\\n",
       "0          0          0    ...             0        0               0   \n",
       "1          0          0    ...             0        0               0   \n",
       "2          0          0    ...             0        0               0   \n",
       "3          0          0    ...             0        0               0   \n",
       "4          0          0    ...             0        0               0   \n",
       "\n",
       "   Embarked=C  Embarked=Q  Embarked=S  Pclass=1  Pclass=2  Pclass=3      Fare  \n",
       "0           0           0           1         0         0         1 -0.502445  \n",
       "1           1           0           0         1         0         0  0.786845  \n",
       "2           0           0           1         0         0         1 -0.488854  \n",
       "3           0           0           1         1         0         0  0.420730  \n",
       "4           0           0           1         0         0         1 -0.486337  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(['Fare','Age','Embarked','Pclass'], axis=1)\n",
    "data = pd.concat([data, data_cat, data_real], axis=1, join_axes=[data.index])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "label = data['Survived']\n",
    "data = data.drop(['Survived'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Age=10-20</th>\n",
       "      <th>Age=20-30</th>\n",
       "      <th>Age=30-40</th>\n",
       "      <th>Age=40-50</th>\n",
       "      <th>Age=50-60</th>\n",
       "      <th>Age=60-70</th>\n",
       "      <th>...</th>\n",
       "      <th>Age=80-90</th>\n",
       "      <th>Age=&lt;10</th>\n",
       "      <th>Age=none group</th>\n",
       "      <th>Embarked=C</th>\n",
       "      <th>Embarked=Q</th>\n",
       "      <th>Embarked=S</th>\n",
       "      <th>Pclass=1</th>\n",
       "      <th>Pclass=2</th>\n",
       "      <th>Pclass=3</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "      <td>1</td>\n",
       "      <td>-0.486337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sex  SibSp  Parch   Ticket  Age=10-20  Age=20-30  Age=30-40  Age=40-50  \\\n",
       "0    1      1      0    21171          0          1          0          0   \n",
       "1    0      1      0    17599          0          0          1          0   \n",
       "2    0      0      0  3101282          0          1          0          0   \n",
       "3    0      1      0   113803          0          0          1          0   \n",
       "4    1      0      0   373450          0          0          1          0   \n",
       "\n",
       "   Age=50-60  Age=60-70    ...     Age=80-90  Age=<10  Age=none group  \\\n",
       "0          0          0    ...             0        0               0   \n",
       "1          0          0    ...             0        0               0   \n",
       "2          0          0    ...             0        0               0   \n",
       "3          0          0    ...             0        0               0   \n",
       "4          0          0    ...             0        0               0   \n",
       "\n",
       "   Embarked=C  Embarked=Q  Embarked=S  Pclass=1  Pclass=2  Pclass=3      Fare  \n",
       "0           0           0           1         0         0         1 -0.502445  \n",
       "1           1           0           0         1         0         0  0.786845  \n",
       "2           0           0           1         0         0         1 -0.488854  \n",
       "3           0           0           1         1         0         0  0.420730  \n",
       "4           0           0           1         0         0         1 -0.486337  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.803591470258\n"
     ]
    }
   ],
   "source": [
    "class_res = []\n",
    "#LogisticRegression grid_search\n",
    "params = {\n",
    "    'C':[0.25,0.5,1,2,5,10,15],\n",
    "    'class_weight':['balanced', None],\n",
    "    'max_iter':[70,100,200],\n",
    "    'penalty':['l2','l1']\n",
    "}\n",
    "grid_cv_log = GridSearchCV(LogisticRegression(), params, scoring='accuracy', cv=3)\n",
    "grid_cv_log.fit(data.as_matrix(),label.as_matrix())\n",
    "class_res.append(['logReg',grid_cv_log.best_score_,grid_cv_log.best_params_])\n",
    "print(grid_cv_log.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.659932659933\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "    'n_neighbors':[3,5,7]\n",
    "}\n",
    "grid_cv_kN = GridSearchCV(KNeighborsClassifier(), params, scoring = 'accuracy', cv=3)\n",
    "grid_cv_kN.fit(data.as_matrix(),label.as_matrix())\n",
    "class_res.append(['KN',grid_cv_kN.best_score_,grid_cv_kN.best_params_])\n",
    "print(grid_cv_kN.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.811447811448\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "    'n_estimators':[10,20,50,70],\n",
    "    'max_depth':[30,50,None],\n",
    "    'n_jobs':[-1]\n",
    "}\n",
    "grid_cv_rndForest = GridSearchCV(RandomForestClassifier(), params, n_jobs=-1, scoring = 'accuracy', cv=3)\n",
    "grid_cv_rndForest.fit(data.as_matrix(),label.as_matrix())\n",
    "class_res.append(['RFC',grid_cv_rndForest.best_score_,grid_cv_rndForest.best_params_])\n",
    "print(grid_cv_rndForest.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.800224466891\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "    'learning_rate':[1,2,3],\n",
    "    'n_estimators':[10,30,50,70],\n",
    "    'base_estimator':[DecisionTreeClassifier(), LogisticRegression()]\n",
    "}\n",
    "grid_cv_ada = GridSearchCV(AdaBoostClassifier(), params, n_jobs=-1, scoring = 'accuracy', cv=3)\n",
    "grid_cv_ada.fit(data.as_matrix(),label.as_matrix())\n",
    "class_res.append(['Ada',grid_cv_ada.best_score_,grid_cv_ada.best_params_])\n",
    "print(grid_cv_ada.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.817059483726\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "    'max_depth': [10,20,50,None],\n",
    "    'class_weight': [None, 'balanced']\n",
    "}\n",
    "grid_cv_dTree = GridSearchCV(DecisionTreeClassifier(), params, n_jobs=-1, scoring = 'accuracy', cv=3)\n",
    "grid_cv_dTree.fit(data.as_matrix(),label.as_matrix())\n",
    "class_res.append(['DTree',grid_cv_dTree.best_score_,grid_cv_dTree.best_params_])\n",
    "print(grid_cv_dTree.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.615039281706\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "    'class_weight': [None, 'balanced'],\n",
    "    'penalty': ['l2','l1']\n",
    "}\n",
    "grid_cv_SGD = GridSearchCV(SGDClassifier(), params, n_jobs=-1, scoring = 'accuracy', cv=3)\n",
    "grid_cv_SGD.fit(data.as_matrix(),label.as_matrix())\n",
    "class_res.append(['SGD',grid_cv_SGD.best_score_,grid_cv_SGD.best_params_])\n",
    "print(grid_cv_SGD.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['DTree', 0.81705948372615034, {'class_weight': None, 'max_depth': 10}],\n",
       " ['RFC',\n",
       "  0.81144781144781142,\n",
       "  {'max_depth': None, 'n_estimators': 20, 'n_jobs': -1}],\n",
       " ['logReg',\n",
       "  0.80359147025813693,\n",
       "  {'C': 5, 'class_weight': 'balanced', 'max_iter': 70, 'penalty': 'l1'}],\n",
       " ['Ada',\n",
       "  0.80022446689113358,\n",
       "  {'base_estimator': DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "               max_features=None, max_leaf_nodes=None,\n",
       "               min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "               min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "               presort=False, random_state=None, splitter='best'),\n",
       "   'learning_rate': 2,\n",
       "   'n_estimators': 70}]]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(0,len(class_res)):\n",
    "    tmp = i\n",
    "    for j in range(i+1,len(class_res)):\n",
    "        if class_res[j][1]>class_res[tmp][1]:\n",
    "            tmp=j\n",
    "    chg = class_res[tmp]\n",
    "    class_res[tmp] = class_res[i]\n",
    "    class_res[i] = chg\n",
    "class_res[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Первый вариант:  0.833956961997\n",
      "Второй вариант:  0.827202834824\n"
     ]
    }
   ],
   "source": [
    "clf_1 = VotingClassifier([('DFC', DecisionTreeClassifier(class_weight=None, max_depth=10)),\n",
    "                          ('RFC', RandomForestClassifier(max_depth=None, n_estimators=20)),\n",
    "                         ('ogReg', LogisticRegression(penalty='l1', C=5, max_iter=70))], voting='hard')\n",
    "clf_2 = VotingClassifier([('DFC', DecisionTreeClassifier(class_weight=None, max_depth=10)),\n",
    "                         ('LogReg', LogisticRegression(penalty='l1', C=5, max_iter=70)),\n",
    "                         ('Ada', AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), learning_rate=2,n_estimators=70))])\n",
    "cross_1 = cross_val_score(clf_1, data.as_matrix(), label.as_matrix(), cv=5, scoring='accuracy')\n",
    "cross_2 = cross_val_score(clf_2, data.as_matrix(), label.as_matrix(), cv=5, scoring='accuracy')\n",
    "print('Первый вариант: ', cross_1.mean())\n",
    "print('Второй вариант: ', cross_2.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.833957245707\n"
     ]
    }
   ],
   "source": [
    "clf_3 = xgb.XGBClassifier()\n",
    "cross_3 = cross_val_score(clf_3, data.as_matrix(), label.as_matrix(), cv=5, scoring='accuracy')\n",
    "print(cross_3.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('test.csv')\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>1</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name  Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    1   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)    0   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    1   \n",
       "3          895       3                              Wirz, Mr. Albert    1   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)    0   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Sex'] = np.where(test['Sex']=='male',1,0)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>Pclass</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>Embarked</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
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       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>7.0000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex   Age  SibSp  Parch   Ticket     Fare Embarked\n",
       "0       3    1  34.5      0      0   330911   7.8292        Q\n",
       "1       3    0  47.0      1      0   363272   7.0000        S\n",
       "2       2    1  62.0      0      0   240276   9.6875        Q\n",
       "3       3    1  27.0      0      0   315154   8.6625        S\n",
       "4       3    0  22.0      1      1  3101298  12.2875        S"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(0,len(test)):\n",
    "    ticket = test.iloc[i,7].split()\n",
    "    try:\n",
    "        test.iloc[i,7] = int(ticket[-1])\n",
    "    except:\n",
    "        test.iloc[i,7] = 0\n",
    "test = test.drop(['Name','PassengerId','Cabin'],axis=1)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1</td>\n",
       "      <td>30-40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>40-50</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>60-70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>20-30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>20-30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Pclass  Sex    Age  SibSp  Parch   Ticket     Fare Embarked\n",
       "0      3    1  30-40      0      0   330911   7.8292        Q\n",
       "1      3    0  40-50      1      0   363272   7.0000        S\n",
       "2      2    1  60-70      0      0   240276   9.6875        Q\n",
       "3      3    1  20-30      0      0   315154   8.6625        S\n",
       "4      3    0  20-30      1      1  3101298  12.2875        S"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(0,len(test)):\n",
    "    age = test.iloc[i,2]\n",
    "    if math.isnan(test.iloc[i,2]):\n",
    "        test.iloc[i,2] = 'none group'\n",
    "    elif int(age)<10:\n",
    "        test.iloc[i,2] = '<10'\n",
    "    elif 10<=int(age)<20:\n",
    "        test.iloc[i,2] = '10-20'\n",
    "    elif 20<=int(age)<30:\n",
    "        test.iloc[i,2] = '20-30'\n",
    "    elif 30<=int(age)<40:\n",
    "        test.iloc[i,2] = '30-40'\n",
    "    elif 40<=int(age)<50:\n",
    "        test.iloc[i,2] = '40-50'\n",
    "    elif 50<=int(age)<60:\n",
    "        test.iloc[i,2] = '50-60'\n",
    "    elif 60<=int(age)<70:\n",
    "        test.iloc[i,2] = '60-70'\n",
    "    elif 70<=int(age)<80:\n",
    "        test.iloc[i,2] = '70-80'\n",
    "    elif 80<=int(age)<90:\n",
    "        test.iloc[i,2] = '80-90'\n",
    "    elif int(age)>90:\n",
    "        test.iloc[i,2] = '>90'\n",
    "    test.iloc[i,0] = str(test.iloc[i,0])\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>Pclass</th>\n",
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       "      <th>SibSp</th>\n",
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       "  </thead>\n",
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       "      <th>152</th>\n",
       "      <td>3</td>\n",
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      "text/plain": [
       "    Pclass  Sex    Age  SibSp  Parch Ticket  Fare Embarked\n",
       "152      3    1  60-70      0      0   3701   NaN        S"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[test.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>60-70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>20-30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>20-30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Pclass  Sex    Age  SibSp  Parch   Ticket     Fare Embarked\n",
       "0      3    1  30-40      0      0   330911   7.8292        Q\n",
       "1      3    0  40-50      1      0   363272   7.0000        S\n",
       "2      2    1  60-70      0      0   240276   9.6875        Q\n",
       "3      3    1  20-30      0      0   315154   8.6625        S\n",
       "4      3    0  20-30      1      1  3101298  12.2875        S"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.iloc[152,6] = test[(test['Pclass']=='3') & (test['Embarked']=='S')]['Fare'].mean()\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Ticket      0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Рабочий софт\\Анаконда\\lib\\site-packages\\sklearn\\preprocessing\\data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    },
    {
     "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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Age=10-20</th>\n",
       "      <th>Age=20-30</th>\n",
       "      <th>Age=30-40</th>\n",
       "      <th>Age=40-50</th>\n",
       "      <th>Age=50-60</th>\n",
       "      <th>Age=60-70</th>\n",
       "      <th>...</th>\n",
       "      <th>Age=80-90</th>\n",
       "      <th>Age=&lt;10</th>\n",
       "      <th>Age=none group</th>\n",
       "      <th>Embarked=C</th>\n",
       "      <th>Embarked=Q</th>\n",
       "      <th>Embarked=S</th>\n",
       "      <th>Pclass=1</th>\n",
       "      <th>Pclass=2</th>\n",
       "      <th>Pclass=3</th>\n",
       "      <th>Fare</th>\n",
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       "      <td>-0.401017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sex  SibSp  Parch   Ticket  Age=10-20  Age=20-30  Age=30-40  Age=40-50  \\\n",
       "0    1      0      0   330911          0          0          1          0   \n",
       "1    0      1      0   363272          0          0          0          1   \n",
       "2    1      0      0   240276          0          0          0          0   \n",
       "3    1      0      0   315154          0          1          0          0   \n",
       "4    0      1      1  3101298          0          1          0          0   \n",
       "\n",
       "   Age=50-60  Age=60-70    ...     Age=80-90  Age=<10  Age=none group  \\\n",
       "0          0          0    ...             0        0               0   \n",
       "1          0          0    ...             0        0               0   \n",
       "2          0          1    ...             0        0               0   \n",
       "3          0          0    ...             0        0               0   \n",
       "4          0          0    ...             0        0               0   \n",
       "\n",
       "   Embarked=C  Embarked=Q  Embarked=S  Pclass=1  Pclass=2  Pclass=3      Fare  \n",
       "0           0           1           0         0         0         1 -0.490783  \n",
       "1           0           0           1         0         0         1 -0.507479  \n",
       "2           0           1           0         0         1         0 -0.453367  \n",
       "3           0           0           1         0         0         1 -0.474005  \n",
       "4           0           0           1         0         0         1 -0.401017  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_cat = encoder.transform(test[['Age','Embarked','Pclass']].T.to_dict().values())\n",
    "test_real = scaler.transform(test['Fare'])\n",
    "test_cat = pd.DataFrame(test_cat, columns=[encoder.feature_names_])\n",
    "test_real = pd.DataFrame(test_real,columns=['Fare'])\n",
    "test = test.drop(['Fare','Age','Embarked','Pclass'], axis=1)\n",
    "test = pd.concat([test, test_cat, test_real], axis=1, join_axes=[test.index])\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         1\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1"
      ]
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     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "sample = pd.read_csv('gendermodel.csv')\n",
    "sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf_1 = clf_1.fit(data.as_matrix(),label.as_matrix())\n",
    "clf_2 = clf_2.fit(data.as_matrix(),label.as_matrix())\n",
    "clf_3 = clf_3.fit(data.as_matrix(),label.as_matrix())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res_1 = clf_1.predict(test.as_matrix())\n",
    "res_2 = clf_2.predict(test.as_matrix())\n",
    "res_3 = clf_3.predict(test.as_matrix())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "   PassengerId  Survived\n",
       "0          892         0\n",
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       "2          894         0\n",
       "3          895         0\n",
       "4          896         1"
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     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "res_1 = pd.DataFrame(list(zip(sample['PassengerId'].as_matrix(),res_1)), columns=['PassengerId','Survived'])\n",
    "res_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "   PassengerId  Survived\n",
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     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "res_2 = pd.DataFrame(list(zip(sample['PassengerId'].as_matrix(),res_2)), columns=['PassengerId','Survived'])\n",
    "res_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "   PassengerId  Survived\n",
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     "execution_count": 117,
     "metadata": {},
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   ],
   "source": [
    "res_3 = pd.DataFrame(list(zip(sample['PassengerId'].as_matrix(),res_3)), columns=['PassengerId','Survived'])\n",
    "res_3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res_1.to_csv('220117_01.csv', header=True, index=None)\n",
    "res_2.to_csv('220117_02.csv', header=True, index=None)\n",
    "res_3.to_csv('220117_03.csv', header=True, index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf_4 = VotingClassifier([('DFC', DecisionTreeClassifier(class_weight=None, max_depth=10)),\n",
    "                         ('XgBoost', xgb.XGBClassifier()),\n",
    "                         ('Ada', AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), learning_rate=2,n_estimators=70))]).fit(data.as_matrix(),label.as_matrix())\n",
    "res_4 = clf_4.predict(test.as_matrix())\n",
    "res_4 = pd.DataFrame(list(zip(sample['PassengerId'].as_matrix(),res_4)), columns=['PassengerId','Survived'])\n",
    "res_4.to_csv('220117_04.csv', header=True, index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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