Titanic Data Visualization and Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <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",
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       "    <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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       "    <tr>\n",
       "      <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                                                  NaN  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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"S:\\\\ML\\\\Datasets\\\\titanic.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PassengerId</th>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <th>4</th>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\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                                                NaN  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      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  "
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         880 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name            11\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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PassengerId</th>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
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       "    <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>S</td>\n",
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       "    <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",
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       "    <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>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",
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       "    <tr>\n",
       "      <th>890</th>\n",
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       "      <td>Dooley, Mr. Patrick</td>\n",
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       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 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                                                  NaN  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 Embarked  \n",
       "0        0         A/5 21171   7.2500        S  \n",
       "1        0          PC 17599  71.2833        C  \n",
       "2        0  STON/O2. 3101282   7.9250        S  \n",
       "3        0            113803  53.1000        S  \n",
       "4        0            373450   8.0500        S  \n",
       "..     ...               ...      ...      ...  \n",
       "886      0            211536  13.0000        S  \n",
       "887      0            112053  30.0000        S  \n",
       "888      2        W./C. 6607  23.4500        S  \n",
       "889      0            111369  30.0000        C  \n",
       "890      0            370376   7.7500        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.drop(columns=[\"Cabin\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>35.0</td>\n",
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       "      <th>4</th>\n",
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       "      <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>S</td>\n",
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       "      <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",
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       "    <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>S</td>\n",
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       "      <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",
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       "      <th>890</th>\n",
       "      <td>891</td>\n",
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       "      <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>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 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                                              Unknown  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 Embarked  \n",
       "0        0         A/5 21171   7.2500        S  \n",
       "1        0          PC 17599  71.2833        C  \n",
       "2        0  STON/O2. 3101282   7.9250        S  \n",
       "3        0            113803  53.1000        S  \n",
       "4        0            373450   8.0500        S  \n",
       "..     ...               ...      ...      ...  \n",
       "886      0            211536  13.0000        S  \n",
       "887      0            112053  30.0000        S  \n",
       "888      2        W./C. 6607  23.4500        S  \n",
       "889      0            111369  30.0000        C  \n",
       "890      0            370376   7.7500        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Name\"]=df[\"Name\"].fillna(\"Unknown\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age'>"
      ]
     },
     "execution_count": 9,
     "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=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filling missing values of age with median because there is presence of outliers in age column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Age\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Age\"]=df[\"Age\"].fillna(df[\"Age\"].median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For missing values in Embark we are using Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>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>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>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>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",
       "      <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>Unknown</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>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>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>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>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>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</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>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>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 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                                              Unknown  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  28.0      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 Embarked  \n",
       "0        0         A/5 21171   7.2500        S  \n",
       "1        0          PC 17599  71.2833        C  \n",
       "2        0  STON/O2. 3101282   7.9250        S  \n",
       "3        0            113803  53.1000        S  \n",
       "4        0            373450   8.0500        S  \n",
       "..     ...               ...      ...      ...  \n",
       "886      0            211536  13.0000        S  \n",
       "887      0            112053  30.0000        S  \n",
       "888      2        W./C. 6607  23.4500        S  \n",
       "889      0            111369  30.0000        C  \n",
       "890      0            370376   7.7500        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Embarked\"]=df[\"Embarked\"].fillna(df[\"Embarked\"].mode()[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Missing Data count after cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sex (Gender analysis of passengers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x147bbc4cbe0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Sex\",data=df,kind=\"count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion: The count of male passenger is greater than female passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x147bbc4c730>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaMAAAFuCAYAAAAs43eeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUdElEQVR4nO3df/TdBX3f8eeLgDDFKhyCjQQGuhxb8Ac0MVbdD1rbmXbOsLbYWJ1hcqQ7Q6tbtQd0U6fN6plbT5mTbllLDZ0VYm1L9PTAWBx2VSeGlV8BmTmNg5SUJDoLrFu6hPf+uB/sFb6Bb7753u/75pvn45zv+d77uZ/7ue/v4Z48+dwfn0+qCkmSOh3XPYAkScZIktTOGEmS2hkjSVI7YyRJand89wBHYs2aNXXjjTd2jyFJk5TuARbCUb1ntG/fvu4RJEnz4KiOkSRpcTBGkqR2xkiS1M4YSZLaGSNJUjtjJElqZ4wkSe2MkSSpnTGSJLUzRpKkdsZIktTOGEmS2hkjSVI7YyRJandUn8/oSK18z7XdI7S57aNv6R5Bkr7DPSNJUjtjJElqZ4wkSe2MkSSpnTGSJLUzRpKkdsZIktTOGEmS2hkjSVI7YyRJameMJEntjJEkqZ0xkiS1M0aSpHbGSJLUzhhJktoZI0lSO2MkSWpnjCRJ7YyRJKmdMZIktTNGkqR2xkiS1M4YSZLaGSNJUjtjJElqZ4wkSe2MkSSpnTGSJLUzRpKkdsZIktTOGEmS2hkjSVI7YyRJameMJEntjJEkqZ0xkiS1M0aSpHbGSJLUzhhJktpNPEZJliT5oySfG66fmuTmJF8ffp8ytu6VSXYkuS/Jayc9myRpOizEntE7gXvHrl8BbK2qFcDW4TpJzgXWAecBa4CrkyxZgPkkSc0mGqMky4G/A/za2OK1wKbh8ibgorHl11XV/qraCewAVk9yPknSdJj0ntGvAL8APDa27HlVtRtg+H36sPwM4IGx9XYNy75LksuSbEuybe/evRMZWpK0sCYWoySvA/ZU1W2zvcsMy+pJC6o2VtWqqlq1dOnSI5pRkjQdjp/gtl8NvD7JjwMnAd+T5D8CDyVZVlW7kywD9gzr7wLOHLv/cuDBCc4nSZoSE9szqqorq2p5VZ3N6IMJn6+qNwNbgPXDauuBG4bLW4B1SU5Mcg6wArh1UvNJkqbHJPeMDuUjwOYklwL3AxcDVNX2JJuBe4ADwOVVdbBhPknSAluQGFXVLcAtw+VvAq85xHobgA0LMZMkaXp4BAZJUjtjJElqZ4wkSe2MkSSpnTGSJLUzRpKkdsZIktTOGEmS2hkjSVI7YyRJameMJEntjJEkqZ0xkiS1M0aSpHbGSJLUzhhJktoZI0lSO2MkSWpnjCRJ7YyRJKmdMZIktTNGkqR2xkiS1M4YSZLaGSNJUjtjJElqZ4wkSe2MkSSpnTGSJLUzRpKkdsZIktTOGEmS2hkjSVI7YyRJameMJEntjJEkqZ0xkiS1M0aSpHbGSJLUzhhJktoZI0lSO2MkSWpnjCRJ7YyRJKmdMZIktTNGkqR2xkiS1M4YSZLaGSNJUjtjJElqZ4wkSe2MkSSpnTGSJLUzRpKkdsZIktTOGEmS2k0sRklOSnJrkjuSbE/yz4flpya5OcnXh9+njN3nyiQ7ktyX5LWTmk2SNF0muWe0H/jhqnoZcD6wJskPAlcAW6tqBbB1uE6Sc4F1wHnAGuDqJEsmOJ8kaUpMLEY18uhw9YThp4C1wKZh+SbgouHyWuC6qtpfVTuBHcDqSc0nSZoeE33PKMmSJLcDe4Cbq+orwPOqajfA8Pv0YfUzgAfG7r5rWCZJWuQmGqOqOlhV5wPLgdVJXvwUq2emTTxppeSyJNuSbNu7d+88TSpJ6rQgn6arqm8DtzB6L+ihJMsAht97htV2AWeO3W058OAM29pYVauqatXSpUsnObYkaYFM8tN0S5M8d7j8V4AfAb4GbAHWD6utB24YLm8B1iU5Mck5wArg1knNJ0maHsdPcNvLgE3DJ+KOAzZX1eeSfBnYnORS4H7gYoCq2p5kM3APcAC4vKoOTnA+SdKUmFiMqupO4IIZln8TeM0h7rMB2DCpmSRJ08kjMEiS2hkjSVI7YyRJameMJEntjJEkqZ0xkiS1M0aSpHbGSJLUzhhJktoZI0lSO2MkSWpnjCRJ7YyRJKmdMZIktTNGkqR2xkiS1M4YSZLaGSNJUjtjJElqN6sYJdk6m2WSJM3F8U91Y5KTgGcCpyU5Bchw0/cAz5/wbJKkY8RTxgj4WeBdjMJzG38Zo4eBj09uLEnSseQpY1RVVwFXJXlHVX1sgWaSJB1jnm7PCICq+liSVwFnj9+nqq6d0FySpGPIrGKU5DeBFwK3AweHxQUYI0nSEZtVjIBVwLlVVZMcRpJ0bJrt94zuBr53koNIkg5Pkvcl2Z7kziS3J3nFPGzz9UmumKf5Hp3turPdMzoNuCfJrcD+xxdW1esPczZJ0jxI8krgdcAPVNX+JKcBz5jlfY+vqgMz3VZVW4At8zfp7Mw2Rh+c5BCSpMO2DNhXVfsBqmofQJJvAKuqal+SVcC/qqoLk3yQ0dd0zgb2JXkh8Naq2j7c7xbg54GXMHpr5n3AHcALquqxJM8E7gNeAJzF6Os9S4E/B95WVV9Lcg7wW4zacuPh/DGzepmuqr4w08/hPJAkaV79J+DMJP8jydVJ/tYs7rMSWFtVPwNcB7wBIMky4PlVddvjK1bVnzGK0ePb/bvATVX1/4CNwDuqaiXwbuDqYZ2rgF+tqpcDf3o4f8xsDwf0SJKHh5//m+RgkocP54EkSfOnqh5lFJfLgL3A9UkueZq7bamq/zNc3gxcPFx+A/DpGda/Hvjp4fK64TFOBl4FfDrJ7cC/Z7SXBvBq4FPD5d88nL9ntt8zevb49SQXAasP54EkSfOrqg4CtwC3JLkLWA8c4C93NE56wl3+99h9/yTJN5O8lFFwfnaGh9gC/FKSUxmF7/PAs4BvV9X5hxprLn/LnI7aXVW/B/zwXO4rSTpySV6UZMXYovOB/wl8g1E4AH7yaTZzHfALwHOq6q4n3jjsfd3K6OW3z1XVwap6GNiZ5OJhjiR52XCXLzLagwJ40+H8PbP90utPjF09jtGbW37nSJL6nAx8LMlzGe0N7WD0kt33A7+e5L3AV55mG7/NKDQffop1rmf0Et6FY8veBPxqkn8KnMAoancA7wR+K8k7gc8czh+T2XyPNclvjF09wKi8/6Gq9hzOg823VatW1bZt2+Z8/5XvOXYPIHHbR9/SPYKk2cnTr3L0m+17Rv9g0oNIko5ds/003fIkv5tkT5KHknwmyfJJDydJOjbM9gMMv8HoUxXPB84APjsskyTpiM32CAxLq2o8Pp9I8q4JzCNpkbr/Qy/pHqHFWe9/0ofUNIPZ7hntS/LmJEuGnzcD35zkYJKkY8dsY/RWRt/Q/VNgN/BTgB9qkCTNi9nG6MPA+qpaWlWnM4rTByc2lSTpqJBkTZL7kuw4klNPzPY9o5dW1f96/EpVfSvJBXN9UEnS/Fr5nmvn9UAEt330LU/7/aYkSxgdvftHgV3AV5Nsqap7DvfxZrtndFySU8YGOJXZh0yStDitBnZU1R9X1V8wOhLD2rlsaLZB+dfAl5L8NqPDAL0B2DCXB5QkLRpnAA+MXd8FzOlss7M9AsO1SbYxOjhqgJ+Yy26YJGlRmemlvDm9XDjrl9qG+BggSdLjdgFnjl1fDjw4lw3N6RQSkiQBXwVWJDknyTMYnT5iy1w25IcQJElzUlUHkrwduAlYAlxTVdvnsi1jJEmLwGw+ij0JVfX7wO8f6XZ8mU6S1M4YSZLaGSNJUjtjJElqZ4wkSe2MkSSpnTGSJM1JkmuS7Ely95Fua2LfM0pyJnAt8L3AY8DGqrpqOOL39cDZwDeANzx+eookVwKXAgeBn6uqmyY1nyQtJvd/6CXzegqJs95/12y+t/QJ4N8y+rf+iExyz+gA8PNV9f3ADwKXJzkXuALYWlUrgK3DdYbb1gHnAWuAq4dzZUiSplBV/QHwrfnY1sRiVFW7q+q/D5cfAe5ldLjxtcCmYbVNwEXD5bXAdVW1v6p2AjsYnStDkrTILch7RknOBi4AvgI8r6p2wyhYwOnDajOdF+OMGbZ1WZJtSbbt3bt3onNLkhbGxGOU5GTgM8C7qurhp1p1hmVPeg20qjZW1aqqWrV06dL5GlOS1GiiMUpyAqMQfbKqfmdY/FCSZcPty4A9w/J5Oy+GJOnoMrEYJQnw68C9VfXLYzdtAdYPl9cDN4wtX5fkxCTnACuAWyc1nyTpyCT5FPBl4EVJdiW5dK7bmuQpJF4N/H3griS3D8veC3wE2DwMfT9wMUBVbU+ymdHZZA8Al1fVwQnOJ0mLxiw/ij2vquqN87WticWoqv6Qmd8HAnjNIe6zAdgwqZkkSdPJIzBIktoZI0lSO2MkSWpnjCRJ7YyRJKmdMZIktTNGkqR2xkiS1M4YSZLaGSNJUjtjJElqZ4wkSe2MkSSpnTGSJLUzRpKkdsZIktTOGEmS2hkjSVI7YyRJameMJEntjJEkqZ0xkiS1M0aSpHbGSJLUzhhJktoZI0lSO2MkSWpnjCRJ7YyRJKmdMZIktTNGkqR2xkiS1M4YSZLaGSNJUjtjJElqZ4wkSe2MkSSpnTGSJLUzRpKkdsZIktTOGEmS2hkjSVI7YyRJameMJEntjJEkqZ0xkiS1M0aSpHbGSJLUzhhJktoZI0lSO2MkSWpnjCRJ7YyRJKmdMZIktTu+ewD1uP9DL+keocVZ77+rewRJM3DPSJLUbmIxSnJNkj1J7h5bdmqSm5N8ffh9ythtVybZkeS+JK+d1FySpOkzyT2jTwBrnrDsCmBrVa0Atg7XSXIusA44b7jP1UmWTHA2SdIUmViMquoPgG89YfFaYNNweRNw0djy66pqf1XtBHYAqyc1myRpuiz0e0bPq6rdAMPv04flZwAPjK23a1j2JEkuS7Ityba9e/dOdFhJ0sKYlg8wZIZlNdOKVbWxqlZV1aqlS5dOeCxJ0kJY6Bg9lGQZwPB7z7B8F3Dm2HrLgQcXeDZJUpOFjtEWYP1weT1ww9jydUlOTHIOsAK4dYFnkyQ1mdiXXpN8CrgQOC3JLuADwEeAzUkuBe4HLgaoqu1JNgP3AAeAy6vq4KRmkyRNl4nFqKreeIibXnOI9TcAGyY1jyRpek3LBxgkSccwYyRJameMJEntjJEkqZ0xkiS1M0aSpHbGSJLUzjO9Sgto5Xuu7R6hze8+u3sCTTP3jCRJ7YyRJKmdMZIktTNGkqR2xkiS1M4YSZLaGSNJUjtjJElqZ4wkSe2MkSSpnTGSJLUzRpKkdsZIktTOGEmS2hkjSVI7YyRJameMJEntjJEkqZ0xkiS1M0aSpHbGSJLUzhhJktoZI0lSO2MkSWpnjCRJ7YyRJKmdMZIktTNGkqR2xkiS1M4YSZLaGSNJUjtjJElqZ4wkSe2MkSSpnTGSJLUzRpKkdsZIktTOGEmS2hkjSVI7YyRJameMJEntjJEkqZ0xkiS1M0aSpHbGSJLUzhhJktoZI0lSO2MkSWpnjCRJ7aYuRknWJLkvyY4kV3TPI0mavKmKUZIlwMeBHwPOBd6Y5NzeqSRJkzZVMQJWAzuq6o+r6i+A64C1zTNJkibs+O4BnuAM4IGx67uAV4yvkOQy4LLh6qNJ7lug2RaVvwqnAfu651hwH0j3BMcsn3NzdmNVrZmPUabZtMVopv9q9V1XqjYCGxdmnMUrybaqWtU9h44dPuf0VKbtZbpdwJlj15cDDzbNIklaINMWo68CK5Kck+QZwDpgS/NMkqQJm6qX6arqQJK3AzcBS4Brqmp781iLlS91aqH5nNMhpaqefi1JkiZo2l6mkyQdg4yRJKmdMRJJLkzyue45NN2S/FySe5N8ckLb/2CSd09i25p+U/UBBklT7R8BP1ZVO7sH0eLjntEikeTsJF9L8mtJ7k7yySQ/kuSLSb6eZPXw86UkfzT8ftEM23lWkmuSfHVYz8MxiST/DngBsCXJ+2Z6jiS5JMnvJflskp1J3p7knwzr/Lckpw7rvW247x1JPpPkmTM83guT3JjktiT/Ncn3LexfrIVmjBaXvwZcBbwU+D7gZ4C/DrwbeC/wNeBvVtUFwPuBfzHDNt4HfL6qXg78EPDRJM9agNk1xarqHzL6AvoPAc/i0M+RFzN63q0GNgB/Pjzfvgy8ZVjnd6rq5VX1MuBe4NIZHnIj8I6qWsno+Xv1ZP4yTQtfpltcdlbVXQBJtgNbq6qS3AWcDTwH2JRkBaPDLJ0wwzb+NvD6sdfuTwLOYvSPhgSHfo4A/JeqegR4JMmfAZ8dlt/F6H+SAF6c5BeB5wInM/pe4XckORl4FfDp5DtHCDtxAn+HpogxWlz2j11+bOz6Y4z+W3+Y0T8Wfy/J2cAtM2wjwE9WlQeg1aHM+BxJ8gqe/jkI8Angoqq6I8klwIVP2P5xwLer6vx5nVpTzZfpji3PAf5kuHzJIda5CXhHhv8lTXLBAsylo8uRPkeeDexOcgLwpifeWFUPAzuTXDxsP0ledoQza8oZo2PLvwR+KckXGR1uaSYfZvTy3Z1J7h6uS+OO9Dnyz4CvADczeh9zJm8CLk1yB7Adz2u26Hk4IElSO/eMJEntjJEkqZ0xkiS1M0aSpHbGSJLUzhhJTzAce217kjuT3D58mVPSBHkEBmlMklcCrwN+oKr2JzkNeEbzWNKi556R9N2WAfuqaj9AVe2rqgeTrEzyheEo0jclWZbkOUnue/zo50k+leRtrdNLRym/9CqNGQ7S+YfAM4H/DFwPfAn4ArC2qvYm+WngtVX11iQ/CnyI0dHSL6mqNU2jS0c1X6aTxlTVo0lWAn+D0ekRrgd+kdGpEW4eDse2BNg9rH/zcAy1jwMeP02aI/eMpKeQ5KeAy4GTquqVM9x+HKO9pnOAH6+qOxd4RGlR8D0jaUySFw3ne3rc+YzO5bR0+HADSU5Ict5w+z8ebn8jcM1wJGpJh8k9I2nM8BLdxxid+O0AsAO4DFgO/BtGp+E4HvgVRntENwCrq+qRJL8MPFJVH1j4yaWjmzGSJLXzZTpJUjtjJElqZ4wkSe2MkSSpnTGSJLUzRpKkdsZIktTu/wOiDZuGEzjzwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 411.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Sex\",data=df,kind=\"count\",hue=\"Survived\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion: Compared to females , male death rate is higher"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x147bbd314c0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Pclass\",data=df,kind=\"count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### conclusion : In the ship majority passengers had  3 class ticket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x147bbd5eb50>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 430.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Pclass\",data=df,kind=\"count\",hue=\"Sex\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### conclusion : there are more number of male passenger in the Passenger class 3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def checkAge(age):\n",
    "    if age >16 and age <50:\n",
    "        return \"Adult\"\n",
    "    elif age > 50:\n",
    "        return \"Elder\"\n",
    "    elif age<=16:\n",
    "        return \"child\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"age_type\"]=df[\"Age\"].apply(checkAge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x147bbe150a0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 422.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Sex\",data=df,hue=\"age_type\",kind=\"count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### conclusion : There is more passengers from age group 16 to 50 than childerns and elder passengers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x147bbe79e50>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 430.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Embarked\",data=df,hue=\"Sex\",kind=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x147bbef88e0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Embarked\",data=df,hue=\"Pclass\",kind=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    0\n",
       "3    1\n",
       "4    0\n",
       "Name: checkFamily, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"checkFamily\"]=df[\"Parch\"]+df[\"SibSp\"]\n",
    "df[\"checkFamily\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def checkFam(val):\n",
    "    if val>0:\n",
    "        return \"With Family\"\n",
    "    else:\n",
    "        return \"Without Family\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       With Family\n",
       "1       With Family\n",
       "2    Without Family\n",
       "3       With Family\n",
       "4    Without Family\n",
       "Name: Family, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Family\"]=df[\"checkFamily\"].apply(checkFam)\n",
    "df[\"Family\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x147bbcfb6d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 470.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Sex\",data=df,hue=\"Family\",kind=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x147bbf7b100>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 470.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Pclass\",data=df,hue=\"Family\",kind=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x147bcffafa0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 470.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Survived\",data=df,hue=\"Family\",kind=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Count'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k602ut01CrqF8xyT59yTXTFiuu5J8M8ktSbZMSrYkj03yiSTfav6sPbPvXEme2Pye9n/dm+SivnM12V7X/LnfmuTy5r+HTnJNTSkMLaHx28AZwPokZ/QY6UPAeQeMXQxsrqpVwObm9bjtBd5QVU8GngFc2Pye+s52H/CcqnoKsBo4L8kzJiDXfq8Fbht6PSm5AJ5dVauHTl+chGx/C3y2qp4EPIXB767XXFV1e/N7Wg08Hfhf4FN950qyHPgzYE1VncXgJJx1neWqqqn4Ap4JXDf0+hLgkp4zrQS2Dr2+HVjWPF8G3D4Bv7dPM1iLamKyAY8EbgZ+fRJyMbieZjPwHOCaSfp3CdwFnHTAWK/ZgJ8D7qQ5pjkpuQ7I8jzgXychFw+s9HAig5ODrmnydZJravYUODqW0DilqnYBNI8n9xkmyUrgqcBXmYBszRTNLcBu4PqqmohcwLuAPwfuHxqbhFwABXwuyU3NagCTkO0XgT3APzRTbh9I8qgJyDVsHXB587zXXFX1PeAdwN3ALuDHVfW5rnJNUynMtdyop14dRJLjgU8CF1XVvX3nAaiqfTXYtT8VWJvkrJ4jkeR3gN1VdVPfWQ7inKp6GoNp0wuT/GbfgRj8bfdpwHur6qnA/9Dv9Nr/01w4+0Lg431nAWiOFZwPnA48DnhUkt/v6udNUynMu4TGBLgnyTKA5nF3HyGSPJRBIXy0qq6apGwAVfVfwL8wOCbTd65zgBcmuQu4AnhOkn+cgFwAVNXO5nE3g/nxtROQbRaYbfb0AD7BoCT6zrXfbwM3V9U9zeu+c/0WcGdV7amqnwFXAWd3lWuaSuFoWELjamBD83wDg/n8sUoS4IPAbVX1zknJlmQmyWOb549g8B/Kt/rOVVWXVNWpVbWSwZ+pL1TV7/edCyDJo5I8ev9zBvPQW/vOVlXfB3YkeWIzdC6D5fF7/5011vPA1BH0n+tu4BlJHtn893kugwPz3eTq60BOH1/AC4BvA/8BvLnnLJczmB/8GYO/Ob0K+HkGByzvaB5P7CHXbzCYVvsGcEvz9YK+swG/Cvx7k2sr8BfNeO+/s6GMz+KBA82952Iwd//15mvb/j/zE5JtNbCl+ff5T8AJE5LrkcAPgccMjU1Crrcx+EvQVuAjwHFd5fKKZklSa5qmjyRJ87AUJEktS0GS1LIUJEktS0GS1LIUpMOU5MVJKsmT+s4iLRZLQTp864EvM7hoTVoSLAXpMDRrQ53D4KLDdc3YQ5K8p1n3/pok1yZ5afPe05N8sVmY7rr9yxNIk8ZSkA7PixjcD+DbwI+SPA14CYPl0H8F+EMGy7XvX0vq3cBLq+rpwKXAxh4yS/M6tu8A0lFqPYMls2GwEN564KHAx6vqfuD7SW5o3n8icBZw/WDpGo5hsMSJNHEsBWmBkvw8gxvqnJWkGPxPvhisQjrnR4BtVfXMMUWUDpvTR9LCvRT4cFU9vqpWVtUKBncS+wHwu82xhVMYLJAHgztkzSRpp5OSnNlHcGk+loK0cOt58F7BJxncAGWWwUqW72Nwx7ofV9VPGRTJXyX5OoOVZ88eW1ppAVwlVVpESY6vqv9upphuZHDns+/3nUsalccUpMV1TXMzoIcBf2kh6GjjnoIkqeUxBUlSy1KQJLUsBUlSy1KQJLUsBUlSy1KQJLX+D0YOQ94+ACwfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x=\"Age\",data=df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}