Titanic_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing some Standard Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(\"train.csv\")\n",
    "test_data = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
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       "      <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",
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       "      <td>S</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",
       "      <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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
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       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>895</td>\n",
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       "      <td>27.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>315154</td>\n",
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       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
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       "      <th>413</th>\n",
       "      <td>1305</td>\n",
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       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
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       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "<p>418 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "     PassengerId  Pclass                                          Name  \\\n",
       "0            892       3                              Kelly, Mr. James   \n",
       "1            893       3              Wilkes, Mrs. James (Ellen Needs)   \n",
       "2            894       2                     Myles, Mr. Thomas Francis   \n",
       "3            895       3                              Wirz, Mr. Albert   \n",
       "4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
       "..           ...     ...                                           ...   \n",
       "413         1305       3                            Spector, Mr. Woolf   \n",
       "414         1306       1                  Oliva y Ocana, Dona. Fermina   \n",
       "415         1307       3                  Saether, Mr. Simon Sivertsen   \n",
       "416         1308       3                           Ware, Mr. Frederick   \n",
       "417         1309       3                      Peter, Master. Michael J   \n",
       "\n",
       "        Sex   Age  SibSp  Parch              Ticket      Fare Cabin Embarked  \n",
       "0      male  34.5      0      0              330911    7.8292   NaN        Q  \n",
       "1    female  47.0      1      0              363272    7.0000   NaN        S  \n",
       "2      male  62.0      0      0              240276    9.6875   NaN        Q  \n",
       "3      male  27.0      0      0              315154    8.6625   NaN        S  \n",
       "4    female  22.0      1      1             3101298   12.2875   NaN        S  \n",
       "..      ...   ...    ...    ...                 ...       ...   ...      ...  \n",
       "413    male   NaN      0      0           A.5. 3236    8.0500   NaN        S  \n",
       "414  female  39.0      0      0            PC 17758  108.9000  C105        C  \n",
       "415    male  38.5      0      0  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
       "416    male   NaN      0      0              359309    8.0500   NaN        S  \n",
       "417    male   NaN      1      1                2668   22.3583   NaN        C  \n",
       "\n",
       "[418 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Survived\", data=train_data, hue=\"Sex\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Pclass',data = train_data, hue = 'Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "147"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.Cabin.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filling Embarked Missing values with mode value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['S', 'C', 'Q', nan], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Embarked'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    S\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Embarked'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Embarked'] = train_data.Embarked.fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.Embarked.isnull().sum()    # NOW there are NO missing values in Embarked feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### analysing which value is best fits Age feature missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    714.000000\n",
       "mean      29.699118\n",
       "std       14.526497\n",
       "min        0.420000\n",
       "25%       20.125000\n",
       "50%       28.000000\n",
       "75%       38.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x = 'Age', data = train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = 'Age', data = train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling Age with mean\n",
    "train_data['Age'] = train_data.Age.fillna(train_data.Age.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Droping Cabin, Ticket and Name Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = train_data.drop(columns = ['Cabin','Ticket', 'Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>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>male</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</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>female</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</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>male</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass     Sex        Age  SibSp  Parch     Fare  \\\n",
       "0              1         0       3    male  22.000000      1      0   7.2500   \n",
       "1              2         1       1  female  38.000000      1      0  71.2833   \n",
       "2              3         1       3  female  26.000000      0      0   7.9250   \n",
       "3              4         1       1  female  35.000000      1      0  53.1000   \n",
       "4              5         0       3    male  35.000000      0      0   8.0500   \n",
       "..           ...       ...     ...     ...        ...    ...    ...      ...   \n",
       "886          887         0       2    male  27.000000      0      0  13.0000   \n",
       "887          888         1       1  female  19.000000      0      0  30.0000   \n",
       "888          889         0       3  female  29.699118      1      2  23.4500   \n",
       "889          890         1       1    male  26.000000      0      0  30.0000   \n",
       "890          891         0       3    male  32.000000      0      0   7.7500   \n",
       "\n",
       "    Embarked  \n",
       "0          S  \n",
       "1          C  \n",
       "2          S  \n",
       "3          S  \n",
       "4          S  \n",
       "..       ...  \n",
       "886        S  \n",
       "887        S  \n",
       "888        S  \n",
       "889        C  \n",
       "890        Q  \n",
       "\n",
       "[891 rows x 9 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Fare           0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting categorical Data to numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordi = OrdinalEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[['Sex', 'Embarked']] = ordi.fit_transform(train_data[['Sex', 'Embarked']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .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>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>1.0</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  Sex        Age  SibSp  Parch     Fare  \\\n",
       "0              1         0       3  1.0  22.000000      1      0   7.2500   \n",
       "1              2         1       1  0.0  38.000000      1      0  71.2833   \n",
       "2              3         1       3  0.0  26.000000      0      0   7.9250   \n",
       "3              4         1       1  0.0  35.000000      1      0  53.1000   \n",
       "4              5         0       3  1.0  35.000000      0      0   8.0500   \n",
       "..           ...       ...     ...  ...        ...    ...    ...      ...   \n",
       "886          887         0       2  1.0  27.000000      0      0  13.0000   \n",
       "887          888         1       1  0.0  19.000000      0      0  30.0000   \n",
       "888          889         0       3  0.0  29.699118      1      2  23.4500   \n",
       "889          890         1       1  1.0  26.000000      0      0  30.0000   \n",
       "890          891         0       3  1.0  32.000000      0      0   7.7500   \n",
       "\n",
       "     Embarked  \n",
       "0         2.0  \n",
       "1         0.0  \n",
       "2         2.0  \n",
       "3         2.0  \n",
       "4         2.0  \n",
       "..        ...  \n",
       "886       2.0  \n",
       "887       2.0  \n",
       "888       2.0  \n",
       "889       0.0  \n",
       "890       1.0  \n",
       "\n",
       "[891 rows x 9 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=train_data[[\"PassengerId\",\"Pclass\",\"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]]\n",
    "y=train_data[\"Survived\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>3</td>\n",
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       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>890</th>\n",
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       "      <td>1.0</td>\n",
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       "</table>\n",
       "<p>891 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "     PassengerId  Pclass  Sex        Age  SibSp  Parch     Fare  Embarked\n",
       "0              1       3  1.0  22.000000      1      0   7.2500       2.0\n",
       "1              2       1  0.0  38.000000      1      0  71.2833       0.0\n",
       "2              3       3  0.0  26.000000      0      0   7.9250       2.0\n",
       "3              4       1  0.0  35.000000      1      0  53.1000       2.0\n",
       "4              5       3  1.0  35.000000      0      0   8.0500       2.0\n",
       "..           ...     ...  ...        ...    ...    ...      ...       ...\n",
       "886          887       2  1.0  27.000000      0      0  13.0000       2.0\n",
       "887          888       1  0.0  19.000000      0      0  30.0000       2.0\n",
       "888          889       3  0.0  29.699118      1      2  23.4500       2.0\n",
       "889          890       1  1.0  26.000000      0      0  30.0000       0.0\n",
       "890          891       3  1.0  32.000000      0      0   7.7500       1.0\n",
       "\n",
       "[891 rows x 8 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "886    0\n",
       "887    1\n",
       "888    0\n",
       "889    1\n",
       "890    0\n",
       "Name: Survived, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning the Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
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       "      <td>894</td>\n",
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       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                                          Name  \\\n",
       "0            892       3                              Kelly, Mr. James   \n",
       "1            893       3              Wilkes, Mrs. James (Ellen Needs)   \n",
       "2            894       2                     Myles, Mr. Thomas Francis   \n",
       "3            895       3                              Wirz, Mr. Albert   \n",
       "4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
       "..           ...     ...                                           ...   \n",
       "413         1305       3                            Spector, Mr. Woolf   \n",
       "414         1306       1                  Oliva y Ocana, Dona. Fermina   \n",
       "415         1307       3                  Saether, Mr. Simon Sivertsen   \n",
       "416         1308       3                           Ware, Mr. Frederick   \n",
       "417         1309       3                      Peter, Master. Michael J   \n",
       "\n",
       "        Sex   Age  SibSp  Parch              Ticket      Fare Cabin Embarked  \n",
       "0      male  34.5      0      0              330911    7.8292   NaN        Q  \n",
       "1    female  47.0      1      0              363272    7.0000   NaN        S  \n",
       "2      male  62.0      0      0              240276    9.6875   NaN        Q  \n",
       "3      male  27.0      0      0              315154    8.6625   NaN        S  \n",
       "4    female  22.0      1      1             3101298   12.2875   NaN        S  \n",
       "..      ...   ...    ...    ...                 ...       ...   ...      ...  \n",
       "413    male   NaN      0      0           A.5. 3236    8.0500   NaN        S  \n",
       "414  female  39.0      0      0            PC 17758  108.9000  C105        C  \n",
       "415    male  38.5      0      0  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
       "416    male   NaN      0      0              359309    8.0500   NaN        S  \n",
       "417    male   NaN      1      1                2668   22.3583   NaN        C  \n",
       "\n",
       "[418 rows x 11 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filling Age missing Values with Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Age'] = test_data.fillna(test_data['Age'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fillling Fare Missiing Values with Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Fare'] = test_data.fillna(test_data['Fare'].mode())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Droping Cabin, Name and Ticket Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = test_data.drop(columns = ['Cabin', 'Name', 'Ticket'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>892</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>893</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>894</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>895</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>896</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1305</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1306</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1306</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1307</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1308</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1309</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1309</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass     Sex   Age  SibSp  Parch  Fare Embarked\n",
       "0            892       3    male   892      0      0   892        Q\n",
       "1            893       3  female   893      1      0   893        S\n",
       "2            894       2    male   894      0      0   894        Q\n",
       "3            895       3    male   895      0      0   895        S\n",
       "4            896       3  female   896      1      1   896        S\n",
       "..           ...     ...     ...   ...    ...    ...   ...      ...\n",
       "413         1305       3    male  1305      0      0  1305        S\n",
       "414         1306       1  female  1306      0      0  1306        C\n",
       "415         1307       3    male  1307      0      0  1307        S\n",
       "416         1308       3    male  1308      0      0  1308        S\n",
       "417         1309       3    male  1309      1      1  1309        C\n",
       "\n",
       "[418 rows x 8 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Pclass         0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Fare           0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Encoding the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "on = OrdinalEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data[['Sex', 'Embarked']] = on.fit_transform(test_data[['Sex', 'Embarked']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implimenting Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vishwatej\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Converting to .pkl File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'Titanic_Passenger_Survival_Pridiction.pkl'\n",
    "pickle.dump(lr, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=lr.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = pd.DataFrame(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\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>0</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0\n",
       "0    0\n",
       "1    1\n",
       "2    0\n",
       "3    0\n",
       "4    1\n",
       "..  ..\n",
       "413  0\n",
       "414  1\n",
       "415  0\n",
       "416  0\n",
       "417  0\n",
       "\n",
       "[418 rows x 1 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Count of Survived vs Not Survived passengers in test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    271\n",
       "1    147\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='0', ylabel='count'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 0, data = y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = pd.DataFrame(data = np.zeros(len(y_pred)), index = y_pred.index, columns= ['Survived_Categorical_Repr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>417</th>\n",
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       "</table>\n",
       "<p>418 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived_Categorical_Repr\n",
       "0                          0.0\n",
       "1                          0.0\n",
       "2                          0.0\n",
       "3                          0.0\n",
       "4                          0.0\n",
       "..                         ...\n",
       "413                        0.0\n",
       "414                        0.0\n",
       "415                        0.0\n",
       "416                        0.0\n",
       "417                        0.0\n",
       "\n",
       "[418 rows x 1 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Showing Survived or not in More User Friendly manner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0  Survived_Categorical_Repr\n",
       "0    0                        0.0\n",
       "1    1                        0.0\n",
       "2    0                        0.0\n",
       "3    0                        0.0\n",
       "4    1                        0.0\n",
       "..  ..                        ...\n",
       "413  0                        0.0\n",
       "414  1                        0.0\n",
       "415  0                        0.0\n",
       "416  0                        0.0\n",
       "417  0                        0.0\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = y_pred.join(new_df, how ='right', lsuffix = ' ')\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>Survival_Numerical_Representation</th>\n",
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       "      <th>415</th>\n",
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       "      <th>416</th>\n",
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       "      <th>417</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survival_Numerical_Representation  Survived_Categorical_Repr\n",
       "0                                    0                        0.0\n",
       "1                                    1                        0.0\n",
       "2                                    0                        0.0\n",
       "3                                    0                        0.0\n",
       "4                                    1                        0.0\n",
       "..                                 ...                        ...\n",
       "413                                  0                        0.0\n",
       "414                                  1                        0.0\n",
       "415                                  0                        0.0\n",
       "416                                  0                        0.0\n",
       "417                                  0                        0.0\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = new_df.rename( columns = {0: 'Survival_Numerical_Representation'})\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-56-c6a5bf711eda>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df['Survived_Categorical_Repr'][_] = 'Not Survived'\n",
      "C:\\Users\\Vishwatej\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1637: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_block(indexer, value, name)\n",
      "<ipython-input-56-c6a5bf711eda>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df['Survived_Categorical_Repr'][_] = 'Survived'\n"
     ]
    }
   ],
   "source": [
    "for _ in range(len(new_df)):\n",
    "    if new_df['Survival_Numerical_Representation'][_] == 1:\n",
    "        new_df['Survived_Categorical_Repr'][_] = 'Survived'\n",
    "        \n",
    "    elif new_df['Survival_Numerical_Representation'][_] == 0:\n",
    "        new_df['Survived_Categorical_Repr'][_] = 'Not Survived'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Survival_Numerical_Representation</th>\n",
       "      <th>Survived_Categorical_Repr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Not Survived</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Survived</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Not Survived</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Not Survived</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Survived</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>0</td>\n",
       "      <td>Not Survived</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1</td>\n",
       "      <td>Survived</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>0</td>\n",
       "      <td>Not Survived</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>0</td>\n",
       "      <td>Not Survived</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>0</td>\n",
       "      <td>Not Survived</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survival_Numerical_Representation Survived_Categorical_Repr\n",
       "0                                    0              Not Survived\n",
       "1                                    1                  Survived\n",
       "2                                    0              Not Survived\n",
       "3                                    0              Not Survived\n",
       "4                                    1                  Survived\n",
       "..                                 ...                       ...\n",
       "413                                  0              Not Survived\n",
       "414                                  1                  Survived\n",
       "415                                  0              Not Survived\n",
       "416                                  0              Not Survived\n",
       "417                                  0              Not Survived\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Converting Predicted Results to .csv format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df.to_csv('Predictions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting Passenger Details from User."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please Enter Passenger Details: PassengerId, Pclass, Sex, Age, SibSp, Parch, Fare, Embarked\n",
      "805 2 0 34 1 2 50 2\n"
     ]
    }
   ],
   "source": [
    "print(\"Please Enter Passenger Details: PassengerId, Pclass, Sex, Age, SibSp, Parch, Fare, Embarked\")\n",
    "g = input()\n",
    "new_data = g.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = np.array(new_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['805'],\n",
       "       ['2'],\n",
       "       ['0'],\n",
       "       ['34'],\n",
       "       ['1'],\n",
       "       ['2'],\n",
       "       ['50'],\n",
       "       ['2']], dtype='<U3')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.reshape(8,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vishwatej\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "  return f(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "y_pred_new = lr.predict([new_data])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Passenger is Survived....\n"
     ]
    }
   ],
   "source": [
    "if y_pred_new[0]:\n",
    "    print(\"Passenger is Survived....\")\n",
    "else:\n",
    "    print(\"Passenger Not Survived.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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