P1-Data Exploration and Cleaning Titanic Dataset .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "929fdc76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os \n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "31087ef7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the data \n",
    "df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1428d971",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
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       "      <td>S</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
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       "      <th>4</th>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "  </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       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at the dataset \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b09445b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "# number of rows and columns in our dataset \n",
    "df.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "443a784b",
   "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         891 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": [
    "# Complete information of the dataset \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ad8fd23a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the datatypes of each columns \n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8fa2a33c",
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   "outputs": [
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       "      <th>PassengerId</th>\n",
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       "   PassengerId  Survived  Pclass                     Name   Sex   Age  SibSp  \\\n",
       "0            1         0       3  Braund, Mr. Owen Harris  male  22.0      1   \n",
       "\n",
       "   Parch     Ticket  Fare Cabin Embarked  \n",
       "0      0  A/5 21171  7.25   NaN        S  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   "source": [
    "df.head(1)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "id": "8d72aa28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Having a look on the all the columns present in our dataset \n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cdbaf141",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "  </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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the stastical calculation of the numerical columns of the dataset \n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "227cca3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "Empty DataFrame\n",
       "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n",
       "Index: []"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the duplicate rows or data \n",
    "df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "32b498bb",
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the missing values in the dataset \n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "830cdde6",
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b88cb68b",
   "metadata": {},
   "source": [
    "After looking at the data for the first time, we should ask yourself a few questions:\n",
    "\n",
    "* Do I need all of the variables?\n",
    "* Should I transform any variables?\n",
    "* Are there NA values, outliers or other strange values?\n",
    "* Should I create new variables?\n",
    "\n",
    "For the rest of this lesson we will address each of these questions in the context of this data set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c960272c",
   "metadata": {},
   "source": [
    "**Do I need all of the variables?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "43968ad6",
   "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>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \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",
       "\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  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4465656e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     2\n",
       "2     3\n",
       "3     4\n",
       "4     5\n",
       "5     6\n",
       "6     7\n",
       "7     8\n",
       "8     9\n",
       "9    10\n",
       "Name: PassengerId, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first column we are having passengerId, lets have a closer look on this column\n",
    "\n",
    "df['PassengerId'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0b57489a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean     446.000000\n",
       "std      257.353842\n",
       "min        1.000000\n",
       "25%      223.500000\n",
       "50%      446.000000\n",
       "75%      668.500000\n",
       "max      891.000000\n",
       "Name: PassengerId, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PassengerId'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "685547f0",
   "metadata": {},
   "source": [
    "* As we can see that passengerId does not contain any pattern by it is just acting like a index, so we can delete it "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "cda64339",
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['PassengerId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "46b8be53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>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>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2         1       3                             Heikkinen, Miss. Laina   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4         0       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44e99baf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As our goalis to predict the survial, so we should definetly keep the Survived, Pclass,Sex, Age, and Fare \n",
    "# Rest of the columns , we can have a look on that in order to decide whether to keep it or not\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "66b8c5c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                              Braund, Mr. Owen Harris\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "2                               Heikkinen, Miss. Laina\n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
       "4                             Allen, Mr. William Henry\n",
       "5                                     Moran, Mr. James\n",
       "6                              McCarthy, Mr. Timothy J\n",
       "7                       Palsson, Master. Gosta Leonard\n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)\n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's have a look on the name column\n",
    "\n",
    "df['Name'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ea0b0142",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                         891\n",
       "unique                        891\n",
       "top       Braund, Mr. Owen Harris\n",
       "freq                            1\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Name'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ad29311",
   "metadata": {},
   "source": [
    "* Since name column is haiving 891 uniue value so we can't consider it to change into the categorical value, so it is better to delete it "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "58b3d323",
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "de1e1c8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch            Ticket     Fare  \\\n",
       "0         0       3    male  22.0      1      0         A/5 21171   7.2500   \n",
       "1         1       1  female  38.0      1      0          PC 17599  71.2833   \n",
       "2         1       3  female  26.0      0      0  STON/O2. 3101282   7.9250   \n",
       "3         1       1  female  35.0      1      0            113803  53.1000   \n",
       "\n",
       "  Cabin Embarked  \n",
       "0   NaN        S  \n",
       "1   C85        C  \n",
       "2   NaN        S  \n",
       "3  C123        S  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "81ee5cc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           A/5 21171\n",
       "1            PC 17599\n",
       "2    STON/O2. 3101282\n",
       "3              113803\n",
       "4              373450\n",
       "5              330877\n",
       "6               17463\n",
       "7              349909\n",
       "8              347742\n",
       "9              237736\n",
       "Name: Ticket, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now have a closer inspection on the Ticket column\n",
    "\n",
    "df['Ticket'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c9ae6fd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        891\n",
       "unique       681\n",
       "top       347082\n",
       "freq           7\n",
       "Name: Ticket, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Ticket'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60678c25",
   "metadata": {},
   "source": [
    "* So it is having 681 unique value and also it is not following any pattern so it is better to del it "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a7fcd511",
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['Ticket']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1e5b7d0c",
   "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",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Cabin Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500   NaN        S\n",
       "1         1       1  female  38.0      1      0  71.2833   C85        C\n",
       "2         1       3  female  26.0      0      0   7.9250   NaN        S"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e31bf197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     NaN\n",
       "1     C85\n",
       "2     NaN\n",
       "3    C123\n",
       "4     NaN\n",
       "5     NaN\n",
       "6     E46\n",
       "7     NaN\n",
       "8     NaN\n",
       "9     NaN\n",
       "Name: Cabin, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now cheking the cabin column\n",
    "\n",
    "df['Cabin'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "095edf1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         204\n",
       "unique        147\n",
       "top       B96 B98\n",
       "freq            4\n",
       "Name: Cabin, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cabin'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f5f20bf",
   "metadata": {},
   "source": [
    "* Cabin column is also not following any pattern and haiving the 147 unique values , so it is better to drop it "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8812bd70",
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['Cabin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5f1cb928",
   "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",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500        S\n",
       "1         1       1  female  38.0      1      0  71.2833        C\n",
       "2         1       3  female  26.0      0      0   7.9250        S"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61a5971f",
   "metadata": {},
   "source": [
    "* Checking for the each columns unique value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d3d02f4",
   "metadata": {},
   "source": [
    "**Should I transform any variables?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c5392255",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Pclass'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f50dcc56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SibSp'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "115cec10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Parch'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a3349158",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Embarked'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e1adffc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      int64\n",
       "Pclass        int64\n",
       "Sex          object\n",
       "Age         float64\n",
       "SibSp         int64\n",
       "Parch         int64\n",
       "Fare        float64\n",
       "Embarked     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2bee94c1",
   "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",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500        S\n",
       "1         1       1  female  38.0      1      0  71.2833        C"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65990cce",
   "metadata": {},
   "source": [
    "As we can see that the Survived column haiving only two values 0 and 1,\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2b6324b0",
   "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>counts</th>\n",
       "      <th>freqs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>categories</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Died</th>\n",
       "      <td>549</td>\n",
       "      <td>0.616162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>342</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            counts     freqs\n",
       "categories                  \n",
       "Died           549  0.616162\n",
       "Survived       342  0.383838"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_Sur = pd.Categorical(df['Survived'])\n",
    "\n",
    "n_Sur = n_Sur.rename_categories(['Died','Survived'])\n",
    "\n",
    "n_Sur.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bd484b06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Pclass'].unique()  #pclass is also having only three categorical value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "5f7c196f",
   "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>counts</th>\n",
       "      <th>freqs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>categories</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>class1</th>\n",
       "      <td>216</td>\n",
       "      <td>0.242424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class2</th>\n",
       "      <td>184</td>\n",
       "      <td>0.206510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class3</th>\n",
       "      <td>491</td>\n",
       "      <td>0.551066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            counts     freqs\n",
       "categories                  \n",
       "class1         216  0.242424\n",
       "class2         184  0.206510\n",
       "class3         491  0.551066"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_Pclass  = pd.Categorical(df['Pclass'],ordered = True)\n",
    "\n",
    "n_Pclass = n_Pclass.rename_categories(['class1','class2','class3'])\n",
    "\n",
    "n_Pclass.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "14293afb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Pclass'] = n_Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bea57864",
   "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",
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       "    }\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",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Cabin Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500   NaN        S\n",
       "1         1       1  female  38.0      1      0  71.2833   C85        C\n",
       "2         1       3  female  26.0      0      0   7.9250   NaN        S"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2784875a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n",
       "       'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',\n",
       "       'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',\n",
       "       'F E69', 'D47', 'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4',\n",
       "       'A32', 'B4', 'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35',\n",
       "       'C87', 'B77', 'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19',\n",
       "       'B49', 'D', 'C22 C26', 'C106', 'C65', 'E36', 'C54',\n",
       "       'B57 B59 B63 B66', 'C7', 'E34', 'C32', 'B18', 'C124', 'C91', 'E40',\n",
       "       'T', 'C128', 'D37', 'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44',\n",
       "       'A34', 'C104', 'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14',\n",
       "       'B37', 'C30', 'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38',\n",
       "       'B39', 'B22', 'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68',\n",
       "       'B41', 'A20', 'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48',\n",
       "       'E58', 'C126', 'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63',\n",
       "       'C62 C64', 'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30',\n",
       "       'E121', 'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36',\n",
       "       'B102', 'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42',\n",
       "       'C148'], dtype=object)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we are going to inspect the cabin column.\n",
    "df['Cabin'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "694c3a6b",
   "metadata": {},
   "source": [
    "* By looking at the result we can say that cabin column can be group together on the basis oof their first letter, Let's make this change "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "fbd9a3a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      int64\n",
       "Pclass        int64\n",
       "Sex          object\n",
       "Age         float64\n",
       "SibSp         int64\n",
       "Parch         int64\n",
       "Fare        float64\n",
       "Cabin        object\n",
       "Embarked     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7728f96f",
   "metadata": {},
   "outputs": [],
   "source": [
    "char_cabin = df['Cabin'].astype(str) # Converting the object data type to string \n",
    "new_cabin = np.array([cabin[0] for cabin in char_cabin])  # Taking the first character "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5739e8e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['n', 'C', 'n', 'C', 'n', 'n', 'E', 'n', 'n', 'n', 'G', 'C', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'D', 'n', 'A', 'n', 'n',\n",
       "       'n', 'C', 'n', 'n', 'n', 'B', 'n', 'n', 'n', 'n', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n',\n",
       "       'D', 'n', 'B', 'C', 'n', 'n', 'n', 'n', 'n', 'B', 'C', 'n', 'n',\n",
       "       'n', 'F', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'F', 'n', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'C', 'n', 'n',\n",
       "       'n', 'E', 'n', 'n', 'n', 'A', 'D', 'n', 'n', 'n', 'n', 'D', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'n', 'C', 'n', 'n', 'n', 'n', 'n', 'n',\n",
       "       'n', 'B', 'n', 'n', 'n', 'n', 'E', 'D', 'n', 'n', 'n', 'F', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'n', 'D', 'C', 'n', 'B', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'F', 'n', 'n', 'C', 'n', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'E', 'n', 'n',\n",
       "       'n', 'B', 'n', 'n', 'n', 'A', 'n', 'n', 'C', 'n', 'n', 'n', 'n',\n",
       "       'n', 'F', 'n', 'A', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'F', 'B',\n",
       "       'B', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'G', 'n', 'n',\n",
       "       'n', 'A', 'n', 'n', 'n', 'n', 'n', 'D', 'n', 'n', 'D', 'n', 'n',\n",
       "       'n', 'n', 'n', 'C', 'n', 'n', 'n', 'n', 'n', 'C', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'C', 'n',\n",
       "       'n', 'D', 'n', 'n', 'G', 'C', 'n', 'n', 'n', 'n', 'B', 'n', 'n',\n",
       "       'n', 'n', 'E', 'B', 'n', 'n', 'n', 'n', 'C', 'C', 'n', 'n', 'n',\n",
       "       'C', 'n', 'D', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'A', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'B', 'D', 'n', 'n', 'n', 'n', 'C', 'C',\n",
       "       'B', 'n', 'n', 'n', 'E', 'n', 'C', 'n', 'C', 'n', 'E', 'C', 'B',\n",
       "       'n', 'n', 'n', 'n', 'n', 'n', 'C', 'E', 'n', 'n', 'n', 'n', 'n',\n",
       "       'C', 'n', 'D', 'n', 'B', 'n', 'C', 'C', 'n', 'n', 'n', 'C', 'E',\n",
       "       'n', 'T', 'F', 'C', 'n', 'n', 'n', 'F', 'n', 'n', 'n', 'n', 'n',\n",
       "       'C', 'n', 'n', 'n', 'n', 'E', 'n', 'n', 'n', 'n', 'n', 'n', 'n',\n",
       "       'n', 'n', 'D', 'n', 'n', 'B', 'E', 'n', 'n', 'n', 'n', 'n', 'n',\n",
       "       'C', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n',\n",
       "       'B', 'n', 'n', 'D', 'G', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'C', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n',\n",
       "       'E', 'C', 'n', 'n', 'n', 'E', 'B', 'n', 'n', 'C', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'A', 'n', 'n', 'n', 'C', 'n', 'n', 'C', 'C', 'n',\n",
       "       'n', 'E', 'D', 'n', 'n', 'E', 'n', 'E', 'n', 'n', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'D', 'n', 'A', 'n', 'n', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'B', 'n', 'C', 'B', 'n', 'n', 'n', 'n', 'C', 'n',\n",
       "       'n', 'n', 'D', 'n', 'C', 'n', 'n', 'n', 'n', 'n', 'B', 'C', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'E', 'n', 'n', 'D', 'F', 'n', 'n', 'n',\n",
       "       'B', 'n', 'n', 'B', 'n', 'n', 'n', 'C', 'n', 'n', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'B', 'n', 'n', 'B', 'B', 'n', 'n', 'n', 'C', 'n',\n",
       "       'n', 'n', 'n', 'n', 'C', 'n', 'n', 'n', 'n', 'n', 'A', 'n', 'E',\n",
       "       'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'C',\n",
       "       'E', 'n', 'n', 'n', 'n', 'E', 'n', 'n', 'n', 'C', 'n', 'A', 'n',\n",
       "       'E', 'n', 'B', 'n', 'n', 'n', 'D', 'n', 'n', 'n', 'n', 'n', 'n',\n",
       "       'n', 'A', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'C', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'n', 'n', 'F', 'n', 'n', 'D', 'n', 'n',\n",
       "       'n', 'D', 'n', 'D', 'n', 'n', 'A', 'n', 'B', 'n', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'n', 'B', 'n', 'n', 'n', 'D', 'n', 'A', 'n', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'D', 'n', 'n', 'E',\n",
       "       'n', 'n', 'n', 'n', 'n', 'n', 'C', 'n', 'B', 'n', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'B', 'n', 'D', 'n', 'n', 'n', 'n', 'n', 'n', 'n',\n",
       "       'B', 'B', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'C', 'F', 'C', 'E',\n",
       "       'n', 'n', 'n', 'n', 'n', 'E', 'n', 'n', 'C', 'C', 'C', 'n', 'n',\n",
       "       'F', 'C', 'E', 'n', 'n', 'n', 'n', 'n', 'n', 'E', 'n', 'n', 'n',\n",
       "       'n', 'n', 'B', 'n', 'n', 'n', 'n', 'n', 'n', 'B', 'n', 'n', 'D',\n",
       "       'C', 'B', 'n', 'n', 'B', 'n', 'n', 'D', 'n', 'n', 'E', 'n', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'B', 'n', 'n', 'n', 'B', 'n', 'D', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'E', 'n', 'n', 'n', 'F', 'n', 'n', 'B',\n",
       "       'n', 'B', 'D', 'n', 'n', 'n', 'n', 'n', 'n', 'B', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'D', 'n', 'n', 'n', 'n', 'n', 'B', 'n', 'n', 'n',\n",
       "       'A', 'n', 'n', 'E', 'n', 'n', 'n', 'n', 'n', 'B', 'n', 'n', 'n',\n",
       "       'n', 'B', 'n', 'n', 'E', 'n', 'n', 'n', 'n', 'n', 'B', 'n', 'n',\n",
       "       'n', 'n', 'n', 'E', 'n', 'n', 'n', 'C', 'n', 'n', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'n', 'C', 'n', 'n', 'n', 'D', 'n', 'n', 'n', 'E',\n",
       "       'n', 'n', 'n', 'n', 'D', 'n', 'n', 'n', 'n', 'A', 'n', 'n', 'n',\n",
       "       'D', 'B', 'n', 'n', 'n', 'n', 'n', 'n', 'C', 'n', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'B', 'n', 'C', 'n'], dtype='<U1')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_cabin "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "5b310b9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_cabin = pd.Categorical(new_cabin) # Changing the above result into category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8f85a701",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>C</th>\n",
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       "      <th>D</th>\n",
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       "      <td>0.037037</td>\n",
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       "    <tr>\n",
       "      <th>E</th>\n",
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      ],
      "text/plain": [
       "            counts     freqs\n",
       "categories                  \n",
       "A               15  0.016835\n",
       "B               47  0.052750\n",
       "C               59  0.066218\n",
       "D               33  0.037037\n",
       "E               32  0.035915\n",
       "F               13  0.014590\n",
       "G                4  0.004489\n",
       "T                1  0.001122\n",
       "n              687  0.771044"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_cabin.describe() # here we have grouped the cabin column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ca68f6e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cabin'] = new_cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d3d8b8e4",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Sex</th>\n",
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       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Cabin Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500     n        S\n",
       "1         1       1  female  38.0      1      0  71.2833     C        C\n",
       "2         1       3  female  26.0      0      0   7.9250     n        S"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d2f06aa",
   "metadata": {},
   "source": [
    "**Are there NA Values, Outliers or Other Strange Values?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5bf831a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Fare          0\n",
       "Cabin         0\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "188da31d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>n</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>n</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>n</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>n</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>n</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>n</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>n</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>n</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>n</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>n</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass     Sex  Age  SibSp  Parch     Fare Cabin Embarked\n",
       "5           0       3    male  NaN      0      0   8.4583     n        Q\n",
       "17          1       2    male  NaN      0      0  13.0000     n        S\n",
       "19          1       3  female  NaN      0      0   7.2250     n        C\n",
       "26          0       3    male  NaN      0      0   7.2250     n        C\n",
       "28          1       3  female  NaN      0      0   7.8792     n        Q\n",
       "..        ...     ...     ...  ...    ...    ...      ...   ...      ...\n",
       "859         0       3    male  NaN      0      0   7.2292     n        C\n",
       "863         0       3  female  NaN      8      2  69.5500     n        S\n",
       "868         0       3    male  NaN      0      0   9.5000     n        S\n",
       "878         0       3    male  NaN      0      0   7.8958     n        S\n",
       "888         0       3  female  NaN      1      2  23.4500     n        S\n",
       "\n",
       "[177 rows x 9 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# So in age column we are having 177 missing values, let's \n",
    "# check it out \n",
    "\n",
    "df[df['Age'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74c760d4",
   "metadata": {},
   "source": [
    "With 177 missing values it's probably not a good idea to throw all those records away. Here are a few ways we could deal with them:\n",
    "\n",
    "* Replace the null values with 0s\n",
    "* Replace the null values with some central value like the mean or median\n",
    "* Impute some other value\n",
    "* Split the data set into two parts: one set with where records have an Age value and another set where age is null."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1397ff8d",
   "metadata": {},
   "source": [
    "Setting missing values in numeric data to zero makes sense in some cases, but it doesn't make any sense here because a person's age can't be zero. Setting all ages to some central number like the median is a simple fix but there's no telling whether such a central number is a reasonable estimate of age without looking at the distribution of ages. For all we know each age is equally common. We can quickly get a sense of the distribution of ages by creating a histogram of the age variable with df.hist():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "fd243068",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    714.000000\n",
       "mean      29.699118\n",
       "std       14.526497\n",
       "min        0.420000\n",
       "25%       20.125000\n",
       "50%       28.000000\n",
       "75%       38.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "93c667e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column = 'Age',\n",
    "       figsize = (9,6),\n",
    "       bins = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33914103",
   "metadata": {},
   "source": [
    "* From the histogram we can say that most of the age is between 20 and 30, so we can consider replacing the none value with median value of 28 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "8ed4ae31",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_age_var = np.where(df['Age'].isnull(),\n",
    "                      28,\n",
    "                      df['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "3ca8419f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'] = new_age_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c8c2466f",
   "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",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>n</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>n</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Cabin Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500     n        S\n",
       "1         1       1  female  38.0      1      0  71.2833     C        C\n",
       "2         1       3  female  26.0      0      0   7.9250     n        S"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "cff7d7c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Cabin       0\n",
       "Embarked    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "5d68684d",
   "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",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass     Sex   Age  SibSp  Parch  Fare Cabin Embarked\n",
       "61          1       1  female  38.0      0      0  80.0     B      NaN\n",
       "829         1       1  female  62.0      0      0  80.0     B      NaN"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Embarked'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "cb11849e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind  = 'box',\n",
    "       figsize = (9,9))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b13054e3",
   "metadata": {},
   "source": [
    "* From the box plot we can conclude that some of the values in the fare column containing the outliers,this is same as that of the missisn value we can keep it, delete it according to the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89f38d00",
   "metadata": {},
   "source": [
    "**Should I Create New Variables?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "0b9c3a10",
   "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",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>class3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>n</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>class1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>class3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>n</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Cabin Embarked\n",
       "0         0  class3    male  22.0      1      0   7.2500     n        S\n",
       "1         1  class1  female  38.0      1      0  71.2833     C        C\n",
       "2         1  class3  female  26.0      0      0   7.9250     n        S"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05f90a4c",
   "metadata": {},
   "source": [
    "The variables present when you load a data set aren't always the most useful variables for analysis. Creating new variables that are derivations or combinations existing ones is a common step to take before jumping into an analysis or modeling task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "c6a2fc69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new column as Family containig all the family members on board:\n",
    "\n",
    "df['Family'] = df['SibSp'] + df['Parch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "88c4e944",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      0\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "886    0\n",
       "887    0\n",
       "888    3\n",
       "889    0\n",
       "890    0\n",
       "Name: Family, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "172b1722",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "    }\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",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Family</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>0</td>\n",
       "      <td>class3</td>\n",
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       "      <td>28.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>69.55</td>\n",
       "      <td>n</td>\n",
       "      <td>S</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0</td>\n",
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       "      <td>28.0</td>\n",
       "      <td>8</td>\n",
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       "      <td>69.55</td>\n",
       "      <td>n</td>\n",
       "      <td>S</td>\n",
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       "      <th>324</th>\n",
       "      <td>0</td>\n",
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       "      <td>28.0</td>\n",
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       "      <td>n</td>\n",
       "      <td>S</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>0</td>\n",
       "      <td>class3</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
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       "      <td>n</td>\n",
       "      <td>S</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>0</td>\n",
       "      <td>class3</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>0</td>\n",
       "      <td>class3</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>69.55</td>\n",
       "      <td>n</td>\n",
       "      <td>S</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass     Sex   Age  SibSp  Parch   Fare Cabin Embarked  \\\n",
       "159         0  class3    male  28.0      8      2  69.55     n        S   \n",
       "180         0  class3  female  28.0      8      2  69.55     n        S   \n",
       "201         0  class3    male  28.0      8      2  69.55     n        S   \n",
       "324         0  class3    male  28.0      8      2  69.55     n        S   \n",
       "792         0  class3  female  28.0      8      2  69.55     n        S   \n",
       "846         0  class3    male  28.0      8      2  69.55     n        S   \n",
       "863         0  class3  female  28.0      8      2  69.55     n        S   \n",
       "\n",
       "     Family  \n",
       "159      10  \n",
       "180      10  \n",
       "201      10  \n",
       "324      10  \n",
       "792      10  \n",
       "846      10  \n",
       "863      10  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the Family having maximum number of members on board\n",
    "\n",
    "max_family = np.where(df['Family'] == max(df['Family']))\n",
    "\n",
    "df.loc[max_family]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83c72e59",
   "metadata": {},
   "source": [
    "* There are 7 family having 10 members on the board"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6124b6e",
   "metadata": {},
   "source": [
    "* At this point now our dataset is ready for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d5f6644",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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