kaggle_titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender=pd.read_csv('gender_submission.csv')\n",
    "train_set=pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set['FamilySize']=train_set['SibSp']+train_set['Parch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>FamilySize</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
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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",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
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       "      <td>S</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
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       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  FamilySize  \n",
       "0      0         A/5 21171   7.2500   NaN        S           1  \n",
       "1      0          PC 17599  71.2833   C85        C           1  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S           0  \n",
       "3      0            113803  53.1000  C123        S           1  \n",
       "4      0            373450   8.0500   NaN        S           0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set['FamilySize']=test_set['SibSp']+test_set['Parch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "418"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "418"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "FamilySize     891 non-null int64\n",
      "dtypes: float64(2), int64(6), object(5)\n",
      "memory usage: 90.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train_set.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
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       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
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       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
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       "      <td>0.904602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
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       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  FamilySize  \n",
       "count  891.000000  891.000000  891.000000  \n",
       "mean     0.381594   32.204208    0.904602  \n",
       "std      0.806057   49.693429    1.613459  \n",
       "min      0.000000    0.000000    0.000000  \n",
       "25%      0.000000    7.910400    0.000000  \n",
       "50%      0.000000   14.454200    0.000000  \n",
       "75%      0.000000   31.000000    1.000000  \n",
       "max      6.000000  512.329200   10.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>891</td>\n",
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       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>male</td>\n",
       "      <td>347082</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
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       "      <th>freq</th>\n",
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      ],
      "text/plain": [
       "                            Name   Sex  Ticket Cabin Embarked\n",
       "count                        891   891     891   204      889\n",
       "unique                       891     2     681   147        3\n",
       "top     Carlsson, Mr. Frans Olof  male  347082    G6        S\n",
       "freq                           1   577       7     4      644"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.describe(include=[np.object])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>0.016639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.065997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>-0.301914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.890712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>0.783111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.217138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FamilySize</th>\n",
       "      <td>-0.040143</td>\n",
       "      <td>0.016639</td>\n",
       "      <td>0.065997</td>\n",
       "      <td>-0.301914</td>\n",
       "      <td>0.890712</td>\n",
       "      <td>0.783111</td>\n",
       "      <td>0.217138</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.036847 -0.057527 -0.001652   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629   \n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.369226  0.083081  0.018443   \n",
       "Age             0.036847 -0.077221 -0.369226  1.000000 -0.308247 -0.189119   \n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.308247  1.000000  0.414838   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.189119  0.414838  1.000000   \n",
       "Fare            0.012658  0.257307 -0.549500  0.096067  0.159651  0.216225   \n",
       "FamilySize     -0.040143  0.016639  0.065997 -0.301914  0.890712  0.783111   \n",
       "\n",
       "                 Fare  FamilySize  \n",
       "PassengerId  0.012658   -0.040143  \n",
       "Survived     0.257307    0.016639  \n",
       "Pclass      -0.549500    0.065997  \n",
       "Age          0.096067   -0.301914  \n",
       "SibSp        0.159651    0.890712  \n",
       "Parch        0.216225    0.783111  \n",
       "Fare         1.000000    0.217138  \n",
       "FamilySize   0.217138    1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set[['Pclass','Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set[['Sex','Survived']].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>0.336957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.336957"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set[['Embarked','Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jelly\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_set, 'Survived',size=4,aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', bins=20)\n",
    "sns.set_style('darkgrid')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jelly\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 921.6x864 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_set, row='Pclass',col='Survived',size=4,aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', bins=20)\n",
    "sns.set_style('darkgrid')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jelly\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 921.6x864 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid=sns.FacetGrid(train_set,row='Embarked',col='Survived',size=4,aspect=1.6)\n",
    "grid.map(plt.hist,'Age',bins=20)\n",
    "sns.set_style('darkgrid')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jelly\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 921.6x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid=sns.FacetGrid(train_set,row='Sex',col='Survived',size=4,aspect=1.6)\n",
    "grid.map(plt.hist,'Age',bins=20)\n",
    "sns.set_style('darkgrid')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jelly\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\Users\\Jelly\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:715: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 921.6x864 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid=sns.FacetGrid(train_set,row='Embarked',col='Survived',size=4,aspect=1.6)\n",
    "grid.map(sns.barplot,'Sex','Pclass')\n",
    "sns.set_style('darkgrid')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Survived_Cabin=train_set.Survived[(pd.notnull(train_set.Cabin))].value_counts()\n",
    "Survived_noCabin=train_set.Survived[(pd.isnull(train_set.Cabin))].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6666666666666666\n"
     ]
    }
   ],
   "source": [
    "Survived_Cabin_Rate=Survived_Cabin[1]/(Survived_Cabin[0]+Survived_Cabin[1])\n",
    "print(Survived_Cabin_Rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.29985443959243085\n"
     ]
    }
   ],
   "source": [
    "Survived_noCabin_Rate=Survived_noCabin[1]/(Survived_noCabin[0]+Survived_noCabin[1])\n",
    "print(Survived_noCabin_Rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set['Title'] = train_set.Name.str.extract('([A-Za-z]+)\\.', expand=False)\n",
    "train_set['Title'] = train_set['Title'].replace(['Lady', 'Countess', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "train_set['Title'] = train_set['Title'].replace('Mlle', 'Miss')\n",
    "train_set['Title'] = train_set['Title'].replace('Ms', 'Miss')\n",
    "train_set['Title'] = train_set['Title'].replace('Mme', 'Mrs')\n",
    "title_map = {'Mr': 1, 'Miss': 2, 'Mrs': 3, 'Master': 4, 'Rare': 5}\n",
    "train_set['Title'] = train_set['Title'].map(title_map)\n",
    "train_set['Title'] = train_set['Title'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set['Title'] = test_set.Name.str.extract('([A-Za-z]+)\\.', expand=False)\n",
    "test_set['Title'] = test_set['Title'].replace(['Lady', 'Countess', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "test_set['Title'] = test_set['Title'].replace('Mlle', 'Miss')\n",
    "test_set['Title'] = test_set['Title'].replace('Ms', 'Miss')\n",
    "test_set['Title'] = test_set['Title'].replace('Mme', 'Mrs')\n",
    "title_map = {'Mr': 1, 'Miss': 2, 'Mrs': 3, 'Master': 4, 'Rare': 5}\n",
    "test_set['Title'] = test_set['Title'].map(title_map)\n",
    "test_set['Title'] = test_set['Title'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Title  Survived\n",
       "3    3.0  0.793651\n",
       "2    2.0  0.702703\n",
       "4    4.0  0.575000\n",
       "5    5.0  0.363636\n",
       "1    1.0  0.156673\n",
       "0    0.0  0.000000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set[['Title','Survived']].groupby(['Title'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set['Sex'] = train_set['Sex'].map({'female': 0, 'male': 1}).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set['Sex'] = test_set['Sex'].map({'female': 0, 'male': 1}).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "guess_ages = np.zeros((2, 3))\n",
    "for i in range(0, 2):\n",
    "    for j in range(0, 3):\n",
    "         guess_df = train_set[(train_set['Sex'] == i) & (train_set['Pclass'] == j+1)]['Age'].dropna()\n",
    "         age_guess = guess_df.median()\n",
    "         guess_ages[i,j] = int(age_guess / 0.5 + 0.5) * 0.5\n",
    "for i in range(0, 2):\n",
    "    for j in range(0, 3):\n",
    "        train_set.loc[(train_set.Age.isnull()) & (train_set.Sex == i) & (train_set.Pclass == j + 1), 'Age'] = guess_ages[i, j]\n",
    "train_set['Age'] = train_set['Age'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0, 2):\n",
    "    for j in range(0, 3):\n",
    "        test_set.loc[(test_set.Age.isnull()) & (test_set.Sex == i) & (test_set.Pclass == j + 1), 'Age'] = guess_ages[i, j]\n",
    "test_set['Age'] = test_set['Age'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = train_set['Ticket'].value_counts()\n",
    "df = pd.DataFrame(df)\n",
    "df = df[df['Ticket'] > 1]\n",
    "df_ticket = df.index.values        #共享船票的票号\n",
    "tickets = train_set.Ticket.values    #所有的船票\n",
    "result = []\n",
    "for ticket in tickets:\n",
    "    if ticket in df_ticket:\n",
    "        ticket = 1\n",
    "    else:\n",
    "        ticket = 0                 #遍历所有船票，在共享船票里面的为1，否则为0\n",
    "    result.append(ticket)\n",
    "results = pd.DataFrame(result)\n",
    "results.columns = ['Ticket_Count']\n",
    "train_set = pd.concat([train_set, results], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = test_set['Ticket'].value_counts()\n",
    "df = pd.DataFrame(df)\n",
    "df = df[df['Ticket'] > 1]\n",
    "df_ticket = df.index.values        #共享船票的票号\n",
    "tickets = test_set.Ticket.values    #所有的船票\n",
    "result = []\n",
    "for ticket in tickets:\n",
    "    if ticket in df_ticket:\n",
    "        ticket = 1\n",
    "    else:\n",
    "        ticket = 0                 #遍历所有船票，在共享船票里面的为1，否则为0\n",
    "    result.append(ticket)\n",
    "results = pd.DataFrame(result)\n",
    "results.columns = ['Ticket_Count']\n",
    "test_set = pd.concat([test_set, results], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set.loc[ train_set['Age'] <= 10, 'Age'] = 0\n",
    "train_set.loc[(train_set['Age'] > 10) & (train_set['Age'] <= 20), 'Age'] = 1\n",
    "train_set.loc[(train_set['Age'] > 20) & (train_set['Age'] <= 30), 'Age'] = 2\n",
    "train_set.loc[(train_set['Age'] > 30) & (train_set['Age'] <= 40), 'Age'] = 3\n",
    "train_set.loc[(train_set['Age'] > 40) & (train_set['Age'] <= 50), 'Age'] = 4\n",
    "train_set.loc[(train_set['Age'] > 50) & (train_set['Age'] <= 60), 'Age'] = 5\n",
    "train_set.loc[ train_set['Age'] > 60, 'Age'] = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set.loc[ test_set['Age'] <= 10, 'Age'] = 0\n",
    "test_set.loc[(test_set['Age'] > 10) & (test_set['Age'] <= 20), 'Age'] = 1\n",
    "test_set.loc[(test_set['Age'] > 20) & (test_set['Age'] <= 30), 'Age'] = 2\n",
    "test_set.loc[(test_set['Age'] > 30) & (test_set['Age'] <= 40), 'Age'] = 3\n",
    "test_set.loc[(test_set['Age'] > 40) & (test_set['Age'] <= 50), 'Age'] = 4\n",
    "test_set.loc[(test_set['Age'] > 50) & (test_set['Age'] <= 60), 'Age'] = 5\n",
    "test_set.loc[ test_set['Age'] > 60, 'Age'] = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq_fare=train_set.Fare.dropna().mode()[0]\n",
    "test_set['Fare']=test_set['Fare'].fillna(freq_fare)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Title</th>\n",
       "      <th>Ticket_Count</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",
       "      <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",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>0.647587</td>\n",
       "      <td>2.362514</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>0.904602</td>\n",
       "      <td>1.722783</td>\n",
       "      <td>0.386083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>0.477990</td>\n",
       "      <td>1.300781</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>1.613459</td>\n",
       "      <td>1.025806</td>\n",
       "      <td>0.487123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\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",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Sex         Age  \\\n",
       "count   891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642    0.647587    2.362514   \n",
       "std     257.353842    0.486592    0.836071    0.477990    1.300781   \n",
       "min       1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000    0.000000    2.000000   \n",
       "50%     446.000000    0.000000    3.000000    1.000000    2.000000   \n",
       "75%     668.500000    1.000000    3.000000    1.000000    3.000000   \n",
       "max     891.000000    1.000000    3.000000    1.000000    6.000000   \n",
       "\n",
       "            SibSp       Parch        Fare  FamilySize       Title  \\\n",
       "count  891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean     0.523008    0.381594   32.204208    0.904602    1.722783   \n",
       "std      1.102743    0.806057   49.693429    1.613459    1.025806   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    7.910400    0.000000    1.000000   \n",
       "50%      0.000000    0.000000   14.454200    0.000000    1.000000   \n",
       "75%      1.000000    0.000000   31.000000    1.000000    2.000000   \n",
       "max      8.000000    6.000000  512.329200   10.000000    5.000000   \n",
       "\n",
       "       Ticket_Count  \n",
       "count    891.000000  \n",
       "mean       0.386083  \n",
       "std        0.487123  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        1.000000  \n",
       "max        1.000000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set['Pass_0']=(train_set['PassengerId']/100).astype(int)\n",
    "train_set['Pass_1']=(train_set['PassengerId']%100).astype(int)\n",
    "train_set['Pass_2']=(train_set['PassengerId']%10).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set['Pass_0']=(test_set['PassengerId']/100).astype(int)\n",
    "test_set['Pass_1']=(test_set['PassengerId']%100).astype(int)\n",
    "test_set['Pass_2']=(test_set['PassengerId']%10).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Title</th>\n",
       "      <th>Ticket_Count</th>\n",
       "      <th>Pass_0</th>\n",
       "      <th>Pass_1</th>\n",
       "      <th>Pass_2</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",
       "      <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",
       "      <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>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>0.647587</td>\n",
       "      <td>2.362514</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>0.904602</td>\n",
       "      <td>1.722783</td>\n",
       "      <td>0.386083</td>\n",
       "      <td>3.968575</td>\n",
       "      <td>49.142536</td>\n",
       "      <td>4.496072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>0.477990</td>\n",
       "      <td>1.300781</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>1.613459</td>\n",
       "      <td>1.025806</td>\n",
       "      <td>0.487123</td>\n",
       "      <td>2.564915</td>\n",
       "      <td>28.647233</td>\n",
       "      <td>2.874674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\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",
       "      <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>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Sex         Age  \\\n",
       "count   891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642    0.647587    2.362514   \n",
       "std     257.353842    0.486592    0.836071    0.477990    1.300781   \n",
       "min       1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000    0.000000    2.000000   \n",
       "50%     446.000000    0.000000    3.000000    1.000000    2.000000   \n",
       "75%     668.500000    1.000000    3.000000    1.000000    3.000000   \n",
       "max     891.000000    1.000000    3.000000    1.000000    6.000000   \n",
       "\n",
       "            SibSp       Parch        Fare  FamilySize       Title  \\\n",
       "count  891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean     0.523008    0.381594   32.204208    0.904602    1.722783   \n",
       "std      1.102743    0.806057   49.693429    1.613459    1.025806   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    7.910400    0.000000    1.000000   \n",
       "50%      0.000000    0.000000   14.454200    0.000000    1.000000   \n",
       "75%      1.000000    0.000000   31.000000    1.000000    2.000000   \n",
       "max      8.000000    6.000000  512.329200   10.000000    5.000000   \n",
       "\n",
       "       Ticket_Count      Pass_0      Pass_1      Pass_2  \n",
       "count    891.000000  891.000000  891.000000  891.000000  \n",
       "mean       0.386083    3.968575   49.142536    4.496072  \n",
       "std        0.487123    2.564915   28.647233    2.874674  \n",
       "min        0.000000    0.000000    0.000000    0.000000  \n",
       "25%        0.000000    2.000000   24.000000    2.000000  \n",
       "50%        0.000000    4.000000   49.000000    4.000000  \n",
       "75%        1.000000    6.000000   74.000000    7.000000  \n",
       "max        1.000000    8.000000   99.000000    9.000000  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set.loc[ train_set['Fare'] <= train_set['Fare'].quantile(.3), 'Fare'] = 0\n",
    "train_set.loc[(train_set['Fare'] > train_set['Fare'].quantile(.3)) & (train_set['Fare'] <= train_set['Fare'].quantile(.7)), 'Fare'] = 1\n",
    "train_set.loc[ train_set['Fare'] > train_set['Fare'].quantile(.7), 'Fare'] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set.loc[ test_set['Fare'] <= train_set['Fare'].quantile(.3), 'Fare'] = 0\n",
    "test_set.loc[(test_set['Fare'] > train_set['Fare'].quantile(.3)) & (test_set['Fare'] <= train_set['Fare'].quantile(.7)), 'Fare'] = 1\n",
    "test_set.loc[ test_set['Fare'] > train_set['Fare'].quantile(.7), 'Fare'] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq_port = train_set.Embarked.dropna().mode()[0]\n",
    "train_set['Embarked'] = train_set['Embarked'].fillna(freq_port)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set['Embarked'] = test_set['Embarked'].fillna(freq_port)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set['Embarked'] = train_set['Embarked'].map({'S': 0, 'C': 1, 'Q': 2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set['Embarked'] = test_set['Embarked'].map({'S': 0, 'C': 1, 'Q': 2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set.loc[(train_set.Cabin.notnull()), 'Cabin'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set.loc[(train_set.Cabin.isnull()), 'Cabin'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set.loc[(test_set.Cabin.notnull()), 'Cabin'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set.loc[(test_set.Cabin.isnull()), 'Cabin'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>Title</th>\n",
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       "      <th>Pass_1</th>\n",
       "      <th>Pass_2</th>\n",
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       "  </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>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <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>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</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>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
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       "      <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>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "    <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",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
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       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "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    2      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0    3      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0    2      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0    3      1      0   \n",
       "4                           Allen, Mr. William Henry    1    3      0      0   \n",
       "\n",
       "             Ticket  Fare  Cabin  Embarked  FamilySize  Title  Ticket_Count  \\\n",
       "0         A/5 21171   0.0      0         0           1    1.0             0   \n",
       "1          PC 17599   2.0      1         1           1    3.0             0   \n",
       "2  STON/O2. 3101282   0.0      0         0           0    2.0             0   \n",
       "3            113803   2.0      1         0           1    3.0             1   \n",
       "4            373450   0.0      0         0           0    1.0             0   \n",
       "\n",
       "   Pass_0  Pass_1  Pass_2  \n",
       "0       0       1       1  \n",
       "1       0       2       2  \n",
       "2       0       3       3  \n",
       "3       0       4       4  \n",
       "4       0       5       5  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=train_set.drop(['Survived','PassengerId','Name','Ticket','Parch','SibSp'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test=test_set.drop(['PassengerId','Name','Ticket','Parch','SibSp'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Age  Fare  Cabin  Embarked  FamilySize  Title  Ticket_Count  \\\n",
       "0       3    1    2   0.0      0         0           1    1.0             0   \n",
       "1       1    0    3   2.0      1         1           1    3.0             0   \n",
       "2       3    0    2   0.0      0         0           0    2.0             0   \n",
       "3       1    0    3   2.0      1         0           1    3.0             1   \n",
       "4       3    1    3   0.0      0         0           0    1.0             0   \n",
       "\n",
       "   Pass_0  Pass_1  Pass_2  \n",
       "0       0       1       1  \n",
       "1       0       2       2  \n",
       "2       0       3       3  \n",
       "3       0       4       4  \n",
       "4       0       5       5  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=train_set['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test=gender['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Title</th>\n",
       "      <th>Ticket_Count</th>\n",
       "      <th>Pass_0</th>\n",
       "      <th>Pass_1</th>\n",
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       "      <td>3</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>5</td>\n",
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      ],
      "text/plain": [
       "   Pclass  Sex  Age  Fare  Cabin  Embarked  FamilySize  Title  Ticket_Count  \\\n",
       "0       3    1    2   0.0      0         0           1    1.0             0   \n",
       "1       1    0    3   2.0      1         1           1    3.0             0   \n",
       "2       3    0    2   0.0      0         0           0    2.0             0   \n",
       "3       1    0    3   2.0      1         0           1    3.0             1   \n",
       "4       3    1    3   0.0      0         0           0    1.0             0   \n",
       "\n",
       "   Pass_0  Pass_1  Pass_2  \n",
       "0       0       1       1  \n",
       "1       0       2       2  \n",
       "2       0       3       3  \n",
       "3       0       4       4  \n",
       "4       0       5       5  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression,SGDClassifier\n",
    "from sklearn.svm import SVC,LinearSVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jelly\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Jelly\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\Jelly\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\Jelly\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:166: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        Model   Score\n",
      "3               Random Forest  100.00\n",
      "7               Decision Tree  100.00\n",
      "0     Support Vector Machines   92.37\n",
      "1                         KNN   83.05\n",
      "2         Logistic Regression   81.71\n",
      "6                  Linear SVC   79.57\n",
      "4                 Naive Bayes   79.24\n",
      "5  Stochastic Gradient Decent   69.14\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X, y)\n",
    "Y_pred = logreg.predict(X_test)\n",
    "acc_log = round(logreg.score(X, y) * 100, 2)\n",
    "\n",
    "# Support Vector Machines\n",
    "svc = SVC()\n",
    "svc.fit(X, y)\n",
    "Y_pred = svc.predict(X_test)\n",
    "acc_svc = round(svc.score(X, y) * 100, 2)\n",
    "\n",
    "#  k-Nearest Neighbors\n",
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X, y)\n",
    "Y_pred = knn.predict(X_test)\n",
    "acc_knn = round(knn.score(X, y) * 100, 2)\n",
    "\n",
    "# Gaussian Naive Bayes\n",
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X, y)\n",
    "Y_pred = gaussian.predict(X_test)\n",
    "acc_gaussian = round(gaussian.score(X, y) * 100, 2)\n",
    "\n",
    "# Linear SVC\n",
    "linear_svc = LinearSVC()\n",
    "linear_svc.fit(X, y)\n",
    "Y_pred = linear_svc.predict(X_test)\n",
    "acc_linear_svc = round(linear_svc.score(X, y) * 100, 2)\n",
    "\n",
    "# Stochastic Gradient Descent\n",
    "sgd = SGDClassifier()\n",
    "sgd.fit(X, y)\n",
    "Y_pred = sgd.predict(X_test)\n",
    "acc_sgd = round(sgd.score(X, y) * 100, 2)\n",
    "\n",
    "# Decision Tree\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X, y)\n",
    "Y_pred = decision_tree.predict(X_test)\n",
    "acc_decision_tree = round(decision_tree.score(X, y) * 100, 2)\n",
    "\n",
    "# Random Forest\n",
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X, y)\n",
    "Y_pred = random_forest.predict(X_test)\n",
    "random_forest.score(X, y)\n",
    "acc_random_forest = round(random_forest.score(X, y) * 100, 2)\n",
    "\n",
    "models = pd.DataFrame({\n",
    "    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n",
    "              'Random Forest', 'Naive Bayes', \n",
    "              'Stochastic Gradient Decent', 'Linear SVC', \n",
    "              'Decision Tree'],\n",
    "    'Score': [acc_svc, acc_knn, acc_log, \n",
    "              acc_random_forest, acc_gaussian, \n",
    "              acc_sgd, acc_linear_svc, acc_decision_tree]})\n",
    "print(models.sort_values(by='Score', ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Feature  importance\n",
      "0         Pclass    0.070662\n",
      "1            Sex    0.133359\n",
      "2            Age    0.065779\n",
      "3           Fare    0.047330\n",
      "4          Cabin    0.041147\n",
      "5       Embarked    0.033362\n",
      "6     FamilySize    0.069744\n",
      "7          Title    0.172766\n",
      "8   Ticket_Count    0.023309\n",
      "9         Pass_0    0.096897\n",
      "10        Pass_1    0.150048\n",
      "11        Pass_2    0.095598\n"
     ]
    }
   ],
   "source": [
    "features = pd.DataFrame()\n",
    "features['Feature'] = X.columns\n",
    "features['importance'] = random_forest.feature_importances_\n",
    "print(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold,GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.8338945005611672\n",
      "Best parameters: {'criterion': 'entropy', 'max_depth': 5, 'n_estimators': 64}\n"
     ]
    }
   ],
   "source": [
    "forest = RandomForestClassifier(max_features='sqrt')\n",
    "parameter_grid = {\n",
    "                 'max_depth' : [4,5,6],\n",
    "                 'n_estimators': [64,128,256],\n",
    "                 'criterion': ['gini','entropy']\n",
    "                 }\n",
    "cross_validation = StratifiedKFold(n_splits=5,random_state=10)\n",
    "grid_search = GridSearchCV(forest,\n",
    "                           param_grid=parameter_grid,\n",
    "                           cv=cross_validation)\n",
    "grid_search.fit(X, y)\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-65-84f6c0c2fc4b>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-65-84f6c0c2fc4b>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    Best score: 0.8361391694725028\u001b[0m\n\u001b[1;37m             ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "Best score: 0.8316498316498316\n",
    "Best parameters: {'criterion': 'entropy', 'max_depth': 5, 'n_estimators': 64}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest = RandomForestClassifier(criterion='entropy', max_depth=5, n_estimators=64, )\n",
    "random_forest.fit(X, y)\n",
    "Y_pred = random_forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test_set[\"PassengerId\"],\n",
    "        \"Survived\": Y_pred\n",
    "    })\n",
    "submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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