Titanic Predication.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing All Required Libaries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from warnings import filterwarnings\n",
    "filterwarnings(action='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>...</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>...</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>...</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>...</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>...</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass                                               Name     Sex  ...  Parch            Ticket     Fare Cabin  Embarked\n",
       "0            1         0       3                            Braund, Mr. Owen Harris    male  ...      0         A/5 21171   7.2500   NaN         S\n",
       "1            2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  ...      0          PC 17599  71.2833   C85         C\n",
       "2            3         1       3                             Heikkinen, Miss. Laina  female  ...      0  STON/O2. 3101282   7.9250   NaN         S\n",
       "3            4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  ...      0            113803  53.1000  C123         S\n",
       "4            5         0       3                           Allen, Mr. William Henry    male  ...      0            373450   8.0500   NaN         S\n",
       "\n",
       "[5 rows x 12 columns]"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading Datasets\n",
    "pd.set_option('display.max_columns',10,'display.width',1000)\n",
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display shape\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 11)"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "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": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for Null values\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "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": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>...</th>\n",
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       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>...</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Saalfeld, Mr. Adolphe</td>\n",
       "      <td>male</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>644</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId    Survived      Pclass                   Name   Sex  ...       Parch    Ticket        Fare    Cabin  Embarked\n",
       "count    891.000000  891.000000  891.000000                    891   891  ...  891.000000       891  891.000000      204       889\n",
       "unique          NaN         NaN         NaN                    891     2  ...         NaN       681         NaN      147         3\n",
       "top             NaN         NaN         NaN  Saalfeld, Mr. Adolphe  male  ...         NaN  CA. 2343         NaN  B96 B98         S\n",
       "freq            NaN         NaN         NaN                      1   577  ...         NaN         7         NaN        4       644\n",
       "mean     446.000000    0.383838    2.308642                    NaN   NaN  ...    0.381594       NaN   32.204208      NaN       NaN\n",
       "std      257.353842    0.486592    0.836071                    NaN   NaN  ...    0.806057       NaN   49.693429      NaN       NaN\n",
       "min        1.000000    0.000000    1.000000                    NaN   NaN  ...    0.000000       NaN    0.000000      NaN       NaN\n",
       "25%      223.500000    0.000000    2.000000                    NaN   NaN  ...    0.000000       NaN    7.910400      NaN       NaN\n",
       "50%      446.000000    0.000000    3.000000                    NaN   NaN  ...    0.000000       NaN   14.454200      NaN       NaN\n",
       "75%      668.500000    1.000000    3.000000                    NaN   NaN  ...    0.000000       NaN   31.000000      NaN       NaN\n",
       "max      891.000000    1.000000    3.000000                    NaN   NaN  ...    6.000000       NaN  512.329200      NaN       NaN\n",
       "\n",
       "[11 rows x 12 columns]"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Description of dataset\n",
    "train.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>447.016393</td>\n",
       "      <td>2.531876</td>\n",
       "      <td>30.626179</td>\n",
       "      <td>0.553734</td>\n",
       "      <td>0.329690</td>\n",
       "      <td>22.117887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>444.368421</td>\n",
       "      <td>1.950292</td>\n",
       "      <td>28.343690</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.464912</td>\n",
       "      <td>48.395408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PassengerId    Pclass        Age     SibSp     Parch       Fare\n",
       "Survived                                                                 \n",
       "0          447.016393  2.531876  30.626179  0.553734  0.329690  22.117887\n",
       "1          444.368421  1.950292  28.343690  0.473684  0.464912  48.395408"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('Survived').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.096067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch      Fare\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.036847 -0.057527 -0.001652  0.012658\n",
       "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629  0.257307\n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.369226  0.083081  0.018443 -0.549500\n",
       "Age             0.036847 -0.077221 -0.369226  1.000000 -0.308247 -0.189119  0.096067\n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.308247  1.000000  0.414838  0.159651\n",
       "Parch          -0.001652  0.081629  0.018443 -0.189119  0.414838  1.000000  0.216225\n",
       "Fare            0.012658  0.257307 -0.549500  0.096067  0.159651  0.216225  1.000000"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Males in Titanic: 577\n"
     ]
    }
   ],
   "source": [
    "male_ind = len(train[train['Sex'] == 'male'])\n",
    "print(\"No of Males in Titanic:\",male_ind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Females in Titanic: 314\n"
     ]
    }
   ],
   "source": [
    "female_ind = len(train[train['Sex'] == 'female'])\n",
    "print(\"No of Females in Titanic:\",female_ind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Plotting\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "gender = ['Male','Female']\n",
    "index = [577,314]\n",
    "ax.bar(gender,index)\n",
    "plt.xlabel(\"Gender\")\n",
    "plt.ylabel(\"No of people onboarding ship\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [],
   "source": [
    "alive = len(train[train['Survived'] == 1])\n",
    "dead = len(train[train['Survived'] == 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Sex             \n",
       "female  0.742038\n",
       "male    0.188908"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('Sex')[['Survived']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "status = ['Survived','Dead']\n",
    "ind = [alive,dead]\n",
    "ax.bar(status,ind)\n",
    "plt.xlabel(\"Status\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Bar graph of people accrding to ticket class in which people couldn't survive\")"
      ]
     },
     "execution_count": 369,
     "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"
    },
    {
     "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": [
    "plt.figure(1)\n",
    "train.loc[train['Survived'] == 1, 'Pclass'].value_counts().sort_index().plot.bar()\n",
    "plt.title('Bar graph of people accrding to ticket class in which people survived')\n",
    "\n",
    "\n",
    "plt.figure(2)\n",
    "train.loc[train['Survived'] == 0, 'Pclass'].value_counts().sort_index().plot.bar()\n",
    "plt.title('Bar graph of people accrding to ticket class in which people couldn\\'t survive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x19a393b54c8>,\n",
       "  <matplotlib.axis.XTick at 0x19a393b2b48>,\n",
       "  <matplotlib.axis.XTick at 0x19a393b2708>,\n",
       "  <matplotlib.axis.XTick at 0x19a393f6d08>,\n",
       "  <matplotlib.axis.XTick at 0x19a393fa4c8>,\n",
       "  <matplotlib.axis.XTick at 0x19a393fab88>,\n",
       "  <matplotlib.axis.XTick at 0x19a393ff388>,\n",
       "  <matplotlib.axis.XTick at 0x19a393ffb48>,\n",
       "  <matplotlib.axis.XTick at 0x19a39403348>,\n",
       "  <matplotlib.axis.XTick at 0x19a39403e48>],\n",
       " <a list of 10 Text xticklabel objects>)"
      ]
     },
     "execution_count": 370,
     "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"
    },
    {
     "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": [
    "plt.figure(1)\n",
    "age  = train.loc[train.Survived == 1, 'Age']\n",
    "plt.title('The histogram of the age groups of the people that had survived')\n",
    "plt.hist(age, np.arange(0,100,10))\n",
    "plt.xticks(np.arange(0,100,10))\n",
    "\n",
    "\n",
    "plt.figure(2)\n",
    "age  = train.loc[train.Survived == 0, 'Age']\n",
    "plt.title('The histogram of the age groups of the people that coudn\\'t survive')\n",
    "plt.hist(age, np.arange(0,100,10))\n",
    "plt.xticks(np.arange(0,100,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "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",
       "        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>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[[\"SibSp\", \"Survived\"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "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>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[[\"Pclass\", \"Survived\"]].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.42</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.67</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.83</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.92</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>70.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>70.50</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>71.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>74.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>80.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Survived\n",
       "0    0.42       1.0\n",
       "1    0.67       1.0\n",
       "2    0.75       1.0\n",
       "3    0.83       1.0\n",
       "4    0.92       1.0\n",
       "..    ...       ...\n",
       "83  70.00       0.0\n",
       "84  70.50       0.0\n",
       "85  71.00       0.0\n",
       "86  74.00       0.0\n",
       "87  80.00       1.0\n",
       "\n",
       "[88 rows x 2 columns]"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[[\"Age\", \"Survived\"]].groupby(['Age'], as_index=False).mean().sort_values(by='Age', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>0.336957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.336957"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[[\"Embarked\", \"Survived\"]].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.axis('equal')\n",
    "l = ['C = Cherbourg', 'Q = Queenstown', 'S = Southampton']\n",
    "s = [0.553571,0.389610,0.336957]\n",
    "ax.pie(s, labels = l,autopct='%1.2f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>...</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>count</th>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418</td>\n",
       "      <td>418</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418</td>\n",
       "      <td>417.000000</td>\n",
       "      <td>91</td>\n",
       "      <td>418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>418</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>363</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Portaluppi, Mr. Emilio Ilario Giuseppe</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>2.265550</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.272590</td>\n",
       "      <td>...</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.627188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>120.810458</td>\n",
       "      <td>0.841838</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.181209</td>\n",
       "      <td>...</td>\n",
       "      <td>0.981429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.907576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>892.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>996.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1204.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId      Pclass                                    Name   Sex         Age  ...       Parch    Ticket        Fare            Cabin Embarked\n",
       "count    418.000000  418.000000                                     418   418  332.000000  ...  418.000000       418  417.000000               91      418\n",
       "unique          NaN         NaN                                     418     2         NaN  ...         NaN       363         NaN               76        3\n",
       "top             NaN         NaN  Portaluppi, Mr. Emilio Ilario Giuseppe  male         NaN  ...         NaN  PC 17608         NaN  B57 B59 B63 B66        S\n",
       "freq            NaN         NaN                                       1   266         NaN  ...         NaN         5         NaN                3      270\n",
       "mean    1100.500000    2.265550                                     NaN   NaN   30.272590  ...    0.392344       NaN   35.627188              NaN      NaN\n",
       "std      120.810458    0.841838                                     NaN   NaN   14.181209  ...    0.981429       NaN   55.907576              NaN      NaN\n",
       "min      892.000000    1.000000                                     NaN   NaN    0.170000  ...    0.000000       NaN    0.000000              NaN      NaN\n",
       "25%      996.250000    1.000000                                     NaN   NaN   21.000000  ...    0.000000       NaN    7.895800              NaN      NaN\n",
       "50%     1100.500000    3.000000                                     NaN   NaN   27.000000  ...    0.000000       NaN   14.454200              NaN      NaN\n",
       "75%     1204.750000    3.000000                                     NaN   NaN   39.000000  ...    0.000000       NaN   31.500000              NaN      NaN\n",
       "max     1309.000000    3.000000                                     NaN   NaN   76.000000  ...    9.000000       NaN  512.329200              NaN      NaN\n",
       "\n",
       "[11 rows x 11 columns]"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Droping Useless Columns\n",
    "train = train.drop(['Ticket'], axis = 1)\n",
    "test = test.drop(['Ticket'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(['Cabin'], axis = 1)\n",
    "test = test.drop(['Cabin'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(['Name'], axis = 1)\n",
    "test = test.drop(['Name'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature Selection\n",
    "column_train=['Age','Pclass','SibSp','Parch','Fare','Sex','Embarked']\n",
    "#training values\n",
    "X=train[column_train]\n",
    "#target value\n",
    "Y=train['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['Age'].isnull().sum()\n",
    "X['Pclass'].isnull().sum()\n",
    "X['SibSp'].isnull().sum()\n",
    "X['Parch'].isnull().sum()\n",
    "X['Fare'].isnull().sum()\n",
    "X['Sex'].isnull().sum()\n",
    "X['Embarked'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now we have to fill all the missing values\n",
    "#age have 177  missing values\n",
    "#either we fill missing values with mean or median form existing values \n",
    "X['Age']=X['Age'].fillna(X['Age'].median())\n",
    "X['Age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['Embarked'] = train['Embarked'].fillna(method ='pad')\n",
    "X['Embarked'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now we need to convert sex into integer value \n",
    "d={'male':0, 'female':1}\n",
    "X['Sex']=X['Sex'].apply(lambda x:d[x])\n",
    "X['Sex'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    0\n",
       "2    2\n",
       "3    2\n",
       "4    2\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e={'C':0, 'Q':1 ,'S':2}\n",
    "X['Embarked']=X['Embarked'].apply(lambda x:e[x])\n",
    "X['Embarked'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training Testing and Spliting the model\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.3,random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.7574626865671642\n"
     ]
    }
   ],
   "source": [
    "#Using LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train,Y_train)\n",
    "Y_pred = model.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(\"Accuracy Score:\",accuracy_score(Y_test,Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[130  26]\n",
      " [ 39  73]]\n"
     ]
    }
   ],
   "source": [
    "#Confusion Matrix\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix\n",
    "confusion_mat = confusion_matrix(Y_test,Y_pred)\n",
    "print(confusion_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc= 0.6604477611940298\n"
     ]
    }
   ],
   "source": [
    "#Using Support Vector\n",
    "from sklearn.svm import SVC\n",
    "model1 = SVC()\n",
    "model1.fit(X_train,Y_train)\n",
    "\n",
    "pred_y = model1.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(\"Acc=\",accuracy_score(Y_test,pred_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[149   7]\n",
      " [ 84  28]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      0.96      0.77       156\n",
      "           1       0.80      0.25      0.38       112\n",
      "\n",
      "    accuracy                           0.66       268\n",
      "   macro avg       0.72      0.60      0.57       268\n",
      "weighted avg       0.71      0.66      0.61       268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "confusion_mat = confusion_matrix(Y_test,pred_y)\n",
    "print(confusion_mat)\n",
    "print(classification_report(Y_test,pred_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.6604477611940298\n"
     ]
    }
   ],
   "source": [
    "#Using KNN Neighbors\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model2 = KNeighborsClassifier(n_neighbors=5)\n",
    "model2.fit(X_train,Y_train)\n",
    "y_pred2 = model2.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(\"Accuracy Score:\",accuracy_score(Y_test,y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[126  30]\n",
      " [ 61  51]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.83      0.81       156\n",
      "           1       0.74      0.69      0.71       112\n",
      "\n",
      "    accuracy                           0.77       268\n",
      "   macro avg       0.76      0.76      0.76       268\n",
      "weighted avg       0.77      0.77      0.77       268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "confusion_mat = confusion_matrix(Y_test,y_pred2)\n",
    "print(confusion_mat)\n",
    "print(classification_report(Y_test,y_pred3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.7686567164179104\n"
     ]
    }
   ],
   "source": [
    "#Using GaussianNB\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "model3 = GaussianNB()\n",
    "model3.fit(X_train,Y_train)\n",
    "y_pred3 = model3.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(\"Accuracy Score:\",accuracy_score(Y_test,y_pred3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[129  27]\n",
      " [ 35  77]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.83      0.81       156\n",
      "           1       0.74      0.69      0.71       112\n",
      "\n",
      "    accuracy                           0.77       268\n",
      "   macro avg       0.76      0.76      0.76       268\n",
      "weighted avg       0.77      0.77      0.77       268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "confusion_mat = confusion_matrix(Y_test,y_pred3)\n",
    "print(confusion_mat)\n",
    "print(classification_report(Y_test,y_pred3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.7425373134328358\n"
     ]
    }
   ],
   "source": [
    "#Using Decision Tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model4 = DecisionTreeClassifier(criterion='entropy',random_state=7)\n",
    "model4.fit(X_train,Y_train)\n",
    "y_pred4 = model4.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(\"Accuracy Score:\",accuracy_score(Y_test,y_pred4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[132  24]\n",
      " [ 45  67]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.85      0.79       156\n",
      "           1       0.74      0.60      0.66       112\n",
      "\n",
      "    accuracy                           0.74       268\n",
      "   macro avg       0.74      0.72      0.73       268\n",
      "weighted avg       0.74      0.74      0.74       268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "confusion_mat = confusion_matrix(Y_test,y_pred4)\n",
    "print(confusion_mat)\n",
    "print(classification_report(Y_test,y_pred4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "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>Model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Score</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.76</th>\n",
       "      <td>Naive Bayes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>Logistic Regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.74</th>\n",
       "      <td>Decision Tree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.66</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.66</th>\n",
       "      <td>KNN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Model\n",
       "Score                         \n",
       "0.76               Naive Bayes\n",
       "0.75       Logistic Regression\n",
       "0.74             Decision Tree\n",
       "0.66   Support Vector Machines\n",
       "0.66                       KNN"
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame({\n",
    "    'Model': ['Logistic Regression','Support Vector Machines', 'Naive Bayes','KNN' ,'Decision Tree'],\n",
    "    'Score': [0.75,0.66,0.76,0.66,0.74]})\n",
    "\n",
    "result_df = results.sort_values(by='Score', ascending=False)\n",
    "result_df = result_df.set_index('Score')\n",
    "result_df.head(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hence I will use Naive Bayes algorithms for training my model.\n",
    "#This project was completely made by Amber Kakkar under Project Based Learning at ICT Academy IIT Kanpur"
   ]
  }
 ],
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