titanic_analysis_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sn\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"data/train.csv\")\n",
    "test = pd.read_csv(\"data/test.csv\")\n",
    "dataset = [train,test]\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
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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>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</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",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
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       "      <td>S</td>\n",
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       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(10)"
   ]
  },
  {
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</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",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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",
       "5             6         0       3   \n",
       "6             7         0       1   \n",
       "7             8         0       3   \n",
       "8             9         1       3   \n",
       "9            10         1       2   \n",
       "10           11         1       3   \n",
       "11           12         1       1   \n",
       "12           13         0       3   \n",
       "13           14         0       3   \n",
       "14           15         0       3   \n",
       "15           16         1       2   \n",
       "16           17         0       3   \n",
       "17           18         1       2   \n",
       "18           19         0       3   \n",
       "19           20         1       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",
       "5                                    Moran, Mr. James    male  28.0      0   \n",
       "6                             McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                      Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8   Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                 Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "10                    Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "12                     Saundercock, Mr. William Henry    male  20.0      0   \n",
       "13                        Andersson, Mr. Anders Johan    male  39.0      1   \n",
       "14               Vestrom, Miss. Hulda Amanda Adolfina  female  14.0      0   \n",
       "15                   Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "16                               Rice, Master. Eugene    male   2.0      4   \n",
       "17                       Williams, Mr. Charles Eugene    male  28.0      0   \n",
       "18  Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0      1   \n",
       "19                            Masselmani, Mrs. Fatima  female  28.0      0   \n",
       "\n",
       "    Parch            Ticket     Fare Cabin Embarked  \n",
       "0       0         A/5 21171   7.2500   NaN        S  \n",
       "1       0          PC 17599  71.2833   C85        C  \n",
       "2       0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3       0            113803  53.1000  C123        S  \n",
       "4       0            373450   8.0500   NaN        S  \n",
       "5       0            330877   8.4583   NaN        Q  \n",
       "6       0             17463  51.8625   E46        S  \n",
       "7       1            349909  21.0750   NaN        S  \n",
       "8       2            347742  11.1333   NaN        S  \n",
       "9       0            237736  30.0708   NaN        C  \n",
       "10      1           PP 9549  16.7000    G6        S  \n",
       "11      0            113783  26.5500  C103        S  \n",
       "12      0         A/5. 2151   8.0500   NaN        S  \n",
       "13      5            347082  31.2750   NaN        S  \n",
       "14      0            350406   7.8542   NaN        S  \n",
       "15      0            248706  16.0000   NaN        S  \n",
       "16      1            382652  29.1250   NaN        Q  \n",
       "17      0            244373  13.0000   NaN        S  \n",
       "18      0            345763  18.0000   NaN        S  \n",
       "19      0              2649   7.2250   NaN        C  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# formatting data\n",
    "train['Age'] = train['Age'].fillna(train['Age'].median())\n",
    "train.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#change titles\n",
    "#change the values of survived to yes, no\n",
    "#preprocess cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vep+bI+IySaOB+cBgoAGYEhGvSuoLXA8cCWwE6iLiyUrV19XmzZtXdAlmZh1SyUNMrwDH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\ncQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fac11ff4be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# survival based on pclass\n",
    "plt.figure()\n",
    "plt.title(\"Survival based on Pclass\")\n",
    "g = sn.countplot(x=\"Pclass\", hue=\"Survived\", data=train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fac0ff2ff98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ageVsSur = train.values#[['Age'],['Survived']].values\n",
    "plt.figure()\n",
    "plt.title(\"Survival based on Gender\");\n",
    "sn.countplot(x=\"Sex\", hue=\"Survived\", data=train)\n",
    "plt.show()\n",
    "# batter use pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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koZKelTRL0j8k3SLpEEnzJD0vae/076+SnpT0iKTPNlLPZpJ+IenxtFzDW5SY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fac0fec8780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#gender vs survived\n",
    "plt.figure()\n",
    "plt.title(\"Survival based on Gender and Pclass\");\n",
    "sn.barplot(x=\"Sex\", y=\"Survived\", hue=\"Pclass\", data=train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1XOORkF1eM9DsBG4DHifTdLhHc7iOjImzEkdOnZ+yID45fFBPFas6pzsn1IAD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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fac0fedd080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# male female group\n",
    "plt.figure()\n",
    "plt.title(\"Total male and female percentage\")\n",
    "# Data to plot\n",
    "labels = 'male', 'Female'\n",
    "sizes = [ np.count_nonzero(train['Sex']=='male'), np.count_nonzero(train['Sex']=='female')]\n",
    "colors = ['gold', 'yellowgreen']\n",
    "explode = (0.1, 0)  # explode 1st slice\n",
    " \n",
    "# Plot\n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/AZxO0Qs4NyaGfTNncmpr2ujipAKfgfgoO4fe2TkyC7gd+Bn1QxoL8NVKdl95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\nUO4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fac0fdf0668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# passengers age group\n",
    "le  = LabelEncoder()\n",
    "agegroup = []\n",
    "for age in train['Age']:\n",
    "    if age<18:\n",
    "        agegroup.append(\"Young\")\n",
    "    elif age<60:\n",
    "        agegroup.append(\"Adult\")\n",
    "    else:\n",
    "        agegroup.append(\"Old\")\n",
    "sizes=[]\n",
    "cls = ['Young','Adult','Old']\n",
    "for cl in cls:\n",
    "    size = agegroup.count(cl)\n",
    "    sizes.append(size)\n",
    "\n",
    "#plooting\n",
    "plt.figure()\n",
    "plt.title(\"Percentage of different age groups\")\n",
    "labels = ['Young','Adult','Old']\n",
    "colors = ['yellow','yellowgreen','gold']\n",
    "explode = (0.1,0.1,0.1) \n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.2f%%', shadow=True, startangle=140)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#preprocessing\n",
    "train['Age'] = train['Age'].fillna(train['Age'].median())\n",
    "train['Cabin'] = train['Cabin'].fillna(\"A0\") #consider some A0 cabin number\n",
    "train['Embarked'] = train['Embarked'].fillna(\"0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['female' 'male']\n",
      "['A0' 'A10' 'A14' 'A16' 'A19' 'A20' 'A23' 'A24' 'A26' 'A31' 'A32' 'A34'\n",
      " 'A36' 'A5' 'A6' 'A7' 'B101' 'B102' 'B18' 'B19' 'B20' 'B22' 'B28' 'B3'\n",
      " 'B30' 'B35' 'B37' 'B38' 'B39' 'B4' 'B41' 'B42' 'B49' 'B5' 'B50'\n",
      " 'B51 B53 B55' 'B57 B59 B63 B66' 'B58 B60' 'B69' 'B71' 'B73' 'B77' 'B78'\n",
      " 'B79' 'B80' 'B82 B84' 'B86' 'B94' 'B96 B98' 'C101' 'C103' 'C104' 'C106'\n",
      " 'C110' 'C111' 'C118' 'C123' 'C124' 'C125' 'C126' 'C128' 'C148' 'C2'\n",
      " 'C22 C26' 'C23 C25 C27' 'C30' 'C32' 'C45' 'C46' 'C47' 'C49' 'C50' 'C52'\n",
      " 'C54' 'C62 C64' 'C65' 'C68' 'C7' 'C70' 'C78' 'C82' 'C83' 'C85' 'C86' 'C87'\n",
      " 'C90' 'C91' 'C92' 'C93' 'C95' 'C99' 'D' 'D10 D12' 'D11' 'D15' 'D17' 'D19'\n",
      " 'D20' 'D21' 'D26' 'D28' 'D30' 'D33' 'D35' 'D36' 'D37' 'D45' 'D46' 'D47'\n",
      " 'D48' 'D49' 'D50' 'D56' 'D6' 'D7' 'D9' 'E10' 'E101' 'E12' 'E121' 'E17'\n",
      " 'E24' 'E25' 'E31' 'E33' 'E34' 'E36' 'E38' 'E40' 'E44' 'E46' 'E49' 'E50'\n",
      " 'E58' 'E63' 'E67' 'E68' 'E77' 'E8' 'F E69' 'F G63' 'F G73' 'F2' 'F33'\n",
      " 'F38' 'F4' 'G6' 'T']\n",
      "['0' 'C' 'Q' 'S']\n"
     ]
    }
   ],
   "source": [
    "# as we can not give categorical data to model, convert categorical data to numerical data\n",
    "def label_encoding(dataset,features):\n",
    "    le = LabelEncoder()\n",
    "    for feature in features:\n",
    "        le.fit(dataset[feature])\n",
    "        print(le.classes_)\n",
    "        dataset[feature] = le.transform(dataset[feature])\n",
    "    return dataset\n",
    "\n",
    "train = label_encoding(train, ['Sex','Cabin','Embarked'])\n",
    "#train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#normalization\n",
    "def normalize_features(dataset, features):\n",
    "    for feature in features:\n",
    "        values = dataset[feature].tolist();\n",
    "        mean = np.mean(values)\n",
    "        std = np.std(values)\n",
    "        values = values - mean/std\n",
    "        dataset[feature] = values\n",
    "        \n",
    "    return dataset\n",
    "    \n",
    "train = normalize_features(train, ['Age', 'Fare'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# training data is ready\n",
    "X_train = train[['PassengerId','Pclass','Sex','Age','SibSp','Parch','Embarked',]]\n",
    "#fare, cabin features are removed since in test data, its values NaN\n",
    "X_train = X_train.values\n",
    "Y_train = train['Survived']\n",
    "Y_train = Y_train.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81.6011235955\n",
      "Accuracy Score\n",
      " 0.754189944134\n",
      "Confusion Matrix\n",
      " [[92 18]\n",
      " [26 43]]\n",
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.78      0.84      0.81       110\n",
      "          1       0.70      0.62      0.66        69\n",
      "\n",
      "avg / total       0.75      0.75      0.75       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# metric\n",
    "# Evaluationg the model\n",
    "x_train, x_test, y_train, y_test = train_test_split(X_train, Y_train, test_size=0.2, random_state=7)\n",
    "model = LogisticRegressionCV()\n",
    "clf = model.fit(x_train,y_train)\n",
    "print(clf.score(x_train,y_train)*100)\n",
    "predictions = model.predict(x_test)\n",
    "print(\"Accuracy Score\\n\", accuracy_score(y_test,predictions))\n",
    "print(\"Confusion Matrix\\n\", confusion_matrix(y_test,predictions))\n",
    "print(\"Classification Report\\n\", classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['female' 'male']\n",
      "['C' 'Q' 'S']\n"
     ]
    }
   ],
   "source": [
    "# preprocessing test data\n",
    "test['Age'] = test['Age'].fillna(test['Age'].median())\n",
    "test['Embarked'] = test['Embarked'].fillna(\"0\")\n",
    "test = label_encoding(test,['Sex','Embarked'])\n",
    "test = normalize_features(test,['Age','Fare'])\n",
    "X_test = test[['PassengerId','Pclass','Sex','Age','SibSp','Parch','Embarked',]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# model building\n",
    "# model = LogisticRegressionCV()\n",
    "clf = model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1,\n",
       "       1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1,\n",
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       "       1, 0, 0, 0])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#prediction\n",
    "predictions = model.predict(X_test)\n",
    "predictions"
   ]
  }
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