EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8a0af5eb",
   "metadata": {},
   "source": [
    "# EDA\n",
    "# Exploratory Data Analysis\n",
    "\n",
    "## Key Components\n",
    "#### * Summary Statistics\n",
    "#### * Data Visualization\n",
    "#### * Handling Missing Data\n",
    "#### * Data Transformation\n",
    "#### * Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "09037f77",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "188dade7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1e0af1ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "605058d7",
   "metadata": {},
   "outputs": [
    {
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       "<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",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</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",
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       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</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>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</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",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</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>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</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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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</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>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "0      man        True  NaN  Southampton    no  False  \n",
       "1    woman       False    C    Cherbourg   yes  False  \n",
       "2    woman       False  NaN  Southampton   yes   True  \n",
       "3    woman       False    C  Southampton   yes  False  \n",
       "4      man        True  NaN  Southampton    no   True  \n",
       "..     ...         ...  ...          ...   ...    ...  \n",
       "886    man        True  NaN  Southampton    no   True  \n",
       "887  woman       False    B  Southampton   yes   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "889    man        True    C    Cherbourg   yes   True  \n",
       "890    man        True  NaN   Queenstown    no   True  \n",
       "\n",
       "[891 rows x 15 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26670f0a",
   "metadata": {},
   "source": [
    "# Understanding Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ded49913",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
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       "      <th>sibsp</th>\n",
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       "      <th>783</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
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       "      <th>785</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>41.5792</td>\n",
       "      <td>C</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     survived  pclass   sex   age  sibsp  parch     fare embarked   class  \\\n",
       "783         0       3  male   NaN      1      2  23.4500        S   Third   \n",
       "785         0       3  male  25.0      0      0   7.2500        S   Third   \n",
       "709         1       3  male   NaN      1      1  15.2458        C   Third   \n",
       "382         0       3  male  32.0      0      0   7.9250        S   Third   \n",
       "685         0       2  male  25.0      1      2  41.5792        C  Second   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "783  man        True  NaN  Southampton    no  False  \n",
       "785  man        True  NaN  Southampton    no   True  \n",
       "709  man        True  NaN    Cherbourg   yes  False  \n",
       "382  man        True  NaN  Southampton    no   True  \n",
       "685  man        True  NaN    Cherbourg    no  False  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1131174a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 15)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0f17db17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare',\n",
       "       'embarked', 'class', 'who', 'adult_male', 'deck', 'embark_town',\n",
       "       'alive', 'alone'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef056d24",
   "metadata": {},
   "source": [
    "### Categorical Data\n",
    "* survived\n",
    "* pclass\n",
    "* sex\n",
    "* sibsp\n",
    "* parch\n",
    "* categorical\n",
    "* class\n",
    "* who\n",
    "* adult_male\n",
    "\n",
    "### Numerical\n",
    "* age\n",
    "* numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "52811cd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>male</td>\n",
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       "      <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>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</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",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</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>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b0a975d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 15 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   survived     891 non-null    int64   \n",
      " 1   pclass       891 non-null    int64   \n",
      " 2   sex          891 non-null    object  \n",
      " 3   age          714 non-null    float64 \n",
      " 4   sibsp        891 non-null    int64   \n",
      " 5   parch        891 non-null    int64   \n",
      " 6   fare         891 non-null    float64 \n",
      " 7   embarked     889 non-null    object  \n",
      " 8   class        891 non-null    category\n",
      " 9   who          891 non-null    object  \n",
      " 10  adult_male   891 non-null    bool    \n",
      " 11  deck         203 non-null    category\n",
      " 12  embark_town  889 non-null    object  \n",
      " 13  alive        891 non-null    object  \n",
      " 14  alone        891 non-null    bool    \n",
      "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
      "memory usage: 80.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eda2fc0e",
   "metadata": {},
   "source": [
    "## Checking null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f7a196b9",
   "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",
       "embarked         2\n",
       "class            0\n",
       "who              0\n",
       "adult_male       0\n",
       "deck           688\n",
       "embark_town      2\n",
       "alive            0\n",
       "alone            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95886ab3",
   "metadata": {},
   "source": [
    "# Checking the unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "92dd3a84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Southampton' 'Cherbourg' 'Queenstown' nan]\n",
      "['S' 'C' 'Q' nan]\n",
      "['male' 'female']\n",
      "[1 0 3 4 2 5 8]\n",
      "[0 1 2 5 3 4 6]\n",
      "[3 1 2]\n",
      "[0 1]\n",
      "['Third', 'First', 'Second']\n",
      "Categories (3, object): ['First', 'Second', 'Third']\n",
      "['man' 'woman' 'child']\n",
      "[ True False]\n",
      "['no' 'yes']\n",
      "[False  True]\n",
      "[NaN, 'C', 'E', 'G', 'D', 'A', 'B', 'F']\n",
      "Categories (7, object): ['A', 'B', 'C', 'D', 'E', 'F', 'G']\n"
     ]
    }
   ],
   "source": [
    "print(df[\"embark_town\"].unique())\n",
    "print(df[\"embarked\"].unique())\n",
    "print(df[\"sex\"].unique())\n",
    "print(df[\"sibsp\"].unique())\n",
    "print(df[\"parch\"].unique())\n",
    "print(df[\"pclass\"].unique())\n",
    "print(df[\"survived\"].unique())\n",
    "print(df[\"class\"].unique())\n",
    "print(df[\"who\"].unique())\n",
    "print(df[\"adult_male\"].unique())\n",
    "print(df[\"alive\"].unique())\n",
    "print(df[\"alone\"].unique())\n",
    "print(df[\"deck\"].unique())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6449ea98",
   "metadata": {},
   "source": [
    "# Checking the value count of unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5226ce1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sex\n",
      "male      577\n",
      "female    314\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "embarked\n",
      "S    644\n",
      "C    168\n",
      "Q     77\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "parch\n",
      "0    678\n",
      "1    118\n",
      "2     80\n",
      "5      5\n",
      "3      5\n",
      "4      4\n",
      "6      1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "survived\n",
      "0    549\n",
      "1    342\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "sibsp\n",
      "0    608\n",
      "1    209\n",
      "2     28\n",
      "4     18\n",
      "3     16\n",
      "8      7\n",
      "5      5\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "pclass\n",
      "3    491\n",
      "1    216\n",
      "2    184\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(df[\"sex\"].value_counts())\n",
    "print(\"\\n\")\n",
    "print(df[\"embarked\"].value_counts())\n",
    "print(\"\\n\")\n",
    "print(df[\"parch\"].value_counts())\n",
    "print(\"\\n\")\n",
    "print(df[\"survived\"].value_counts())\n",
    "print(\"\\n\")\n",
    "print(df[\"sibsp\"].value_counts())\n",
    "print(\"\\n\")\n",
    "print(df[\"pclass\"].value_counts())\n",
    "print(\"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ac2c690a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"deck\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7d022fb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>adult_male</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</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",
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       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</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>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</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",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</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>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</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>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male  embark_town alive  alone  \n",
       "0      man        True  Southampton    no  False  \n",
       "1    woman       False    Cherbourg   yes  False  \n",
       "2    woman       False  Southampton   yes   True  \n",
       "3    woman       False  Southampton   yes  False  \n",
       "4      man        True  Southampton    no   True  \n",
       "..     ...         ...          ...   ...    ...  \n",
       "886    man        True  Southampton    no   True  \n",
       "887  woman       False  Southampton   yes   True  \n",
       "888  woman       False  Southampton    no  False  \n",
       "889    man        True    Cherbourg   yes   True  \n",
       "890    man        True   Queenstown    no   True  \n",
       "\n",
       "[891 rows x 14 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a392c23f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"alive\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c6d0dcb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alone</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",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>False</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>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>False</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",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>True</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>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>True</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>True</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>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male  embark_town  alone  \n",
       "0      man        True  Southampton  False  \n",
       "1    woman       False    Cherbourg  False  \n",
       "2    woman       False  Southampton   True  \n",
       "3    woman       False  Southampton  False  \n",
       "4      man        True  Southampton   True  \n",
       "..     ...         ...          ...    ...  \n",
       "886    man        True  Southampton   True  \n",
       "887  woman       False  Southampton   True  \n",
       "888  woman       False  Southampton  False  \n",
       "889    man        True    Cherbourg   True  \n",
       "890    man        True   Queenstown   True  \n",
       "\n",
       "[891 rows x 13 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "47884ca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"embark_town\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "99274b23",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>woman</td>\n",
       "      <td>False</td>\n",
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       "      <td>0</td>\n",
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       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male  alone  \n",
       "0      man        True  False  \n",
       "1    woman       False  False  \n",
       "2    woman       False   True  \n",
       "3    woman       False  False  \n",
       "4      man        True   True  \n",
       "..     ...         ...    ...  \n",
       "886    man        True   True  \n",
       "887  woman       False   True  \n",
       "888  woman       False  False  \n",
       "889    man        True   True  \n",
       "890    man        True   True  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b3be7ab",
   "metadata": {},
   "source": [
    "# Question?\n",
    "\n",
    "# Can we drop any more columns?\n",
    "## Without affecting the dataset\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f7f0abb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"class\"], inplace = True)\n",
    "df.drop(columns=[\"sex\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b18e84c4",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>True</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass   age  sibsp  parch     fare embarked    who  \\\n",
       "0           0       3  22.0      1      0   7.2500        S    man   \n",
       "1           1       1  38.0      1      0  71.2833        C  woman   \n",
       "2           1       3  26.0      0      0   7.9250        S  woman   \n",
       "3           1       1  35.0      1      0  53.1000        S  woman   \n",
       "4           0       3  35.0      0      0   8.0500        S    man   \n",
       "..        ...     ...   ...    ...    ...      ...      ...    ...   \n",
       "886         0       2  27.0      0      0  13.0000        S    man   \n",
       "887         1       1  19.0      0      0  30.0000        S  woman   \n",
       "888         0       3   NaN      1      2  23.4500        S  woman   \n",
       "889         1       1  26.0      0      0  30.0000        C    man   \n",
       "890         0       3  32.0      0      0   7.7500        Q    man   \n",
       "\n",
       "     adult_male  alone  \n",
       "0          True  False  \n",
       "1         False  False  \n",
       "2         False   True  \n",
       "3         False  False  \n",
       "4          True   True  \n",
       "..          ...    ...  \n",
       "886        True   True  \n",
       "887       False   True  \n",
       "888       False  False  \n",
       "889        True   True  \n",
       "890        True   True  \n",
       "\n",
       "[891 rows x 10 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "66d7420a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived        0\n",
       "pclass          0\n",
       "age           177\n",
       "sibsp           0\n",
       "parch           0\n",
       "fare            0\n",
       "embarked        2\n",
       "who             0\n",
       "adult_male      0\n",
       "alone           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0124a00b",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['deck'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[53], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df\u001b[38;5;241m.\u001b[39mdrop(columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdeck\u001b[39m\u001b[38;5;124m\"\u001b[39m], inplace \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m      2\u001b[0m df\u001b[38;5;241m.\u001b[39mdrop(columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124membark_town\u001b[39m\u001b[38;5;124m\"\u001b[39m], inplace \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m      3\u001b[0m df\u001b[38;5;241m.\u001b[39mdrop(columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124malive\u001b[39m\u001b[38;5;124m\"\u001b[39m], inplace \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:5258\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   5110\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdrop\u001b[39m(\n\u001b[0;32m   5111\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   5112\u001b[0m     labels: IndexLabel \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5119\u001b[0m     errors: IgnoreRaise \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   5120\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   5121\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   5122\u001b[0m \u001b[38;5;124;03m    Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[0;32m   5123\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5256\u001b[0m \u001b[38;5;124;03m            weight  1.0     0.8\u001b[39;00m\n\u001b[0;32m   5257\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 5258\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mdrop(\n\u001b[0;32m   5259\u001b[0m         labels\u001b[38;5;241m=\u001b[39mlabels,\n\u001b[0;32m   5260\u001b[0m         axis\u001b[38;5;241m=\u001b[39maxis,\n\u001b[0;32m   5261\u001b[0m         index\u001b[38;5;241m=\u001b[39mindex,\n\u001b[0;32m   5262\u001b[0m         columns\u001b[38;5;241m=\u001b[39mcolumns,\n\u001b[0;32m   5263\u001b[0m         level\u001b[38;5;241m=\u001b[39mlevel,\n\u001b[0;32m   5264\u001b[0m         inplace\u001b[38;5;241m=\u001b[39minplace,\n\u001b[0;32m   5265\u001b[0m         errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[0;32m   5266\u001b[0m     )\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:4549\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4547\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m   4548\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 4549\u001b[0m         obj \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_drop_axis(labels, axis, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m   4551\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[0;32m   4552\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_inplace(obj)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:4591\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[1;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[0;32m   4589\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m   4590\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 4591\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m   4592\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mget_indexer(new_axis)\n\u001b[0;32m   4594\u001b[0m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[0;32m   4595\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6699\u001b[0m, in \u001b[0;36mIndex.drop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   6697\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[0;32m   6698\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m-> 6699\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(labels[mask])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in axis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   6700\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m indexer[\u001b[38;5;241m~\u001b[39mmask]\n\u001b[0;32m   6701\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelete(indexer)\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['deck'] not found in axis\""
     ]
    }
   ],
   "source": [
    "df.drop(columns=[\"deck\"], inplace = True)\n",
    "df.drop(columns=[\"embark_town\"], inplace = True)\n",
    "df.drop(columns=[\"alive\"], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fca976c",
   "metadata": {},
   "source": [
    "# Filling null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7b3aa454",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['embarked'].fillna('S',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8681f366",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age'].fillna(df['age'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ac55f360",
   "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>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>True</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass        age  sibsp  parch     fare embarked    who  \\\n",
       "0           0       3  22.000000      1      0   7.2500        S    man   \n",
       "1           1       1  38.000000      1      0  71.2833        C  woman   \n",
       "2           1       3  26.000000      0      0   7.9250        S  woman   \n",
       "3           1       1  35.000000      1      0  53.1000        S  woman   \n",
       "4           0       3  35.000000      0      0   8.0500        S    man   \n",
       "..        ...     ...        ...    ...    ...      ...      ...    ...   \n",
       "886         0       2  27.000000      0      0  13.0000        S    man   \n",
       "887         1       1  19.000000      0      0  30.0000        S  woman   \n",
       "888         0       3  29.699118      1      2  23.4500        S  woman   \n",
       "889         1       1  26.000000      0      0  30.0000        C    man   \n",
       "890         0       3  32.000000      0      0   7.7500        Q    man   \n",
       "\n",
       "     adult_male  alone  \n",
       "0          True  False  \n",
       "1         False  False  \n",
       "2         False   True  \n",
       "3         False  False  \n",
       "4          True   True  \n",
       "..          ...    ...  \n",
       "886        True   True  \n",
       "887       False   True  \n",
       "888       False  False  \n",
       "889        True   True  \n",
       "890        True   True  \n",
       "\n",
       "[891 rows x 10 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a49abb8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived      0\n",
       "pclass        0\n",
       "age           0\n",
       "sibsp         0\n",
       "parch         0\n",
       "fare          0\n",
       "embarked      0\n",
       "who           0\n",
       "adult_male    0\n",
       "alone         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0f3dc058",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</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>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\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>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>13.002015</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>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>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.000000</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>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>29.699118</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>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>35.000000</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>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": [
       "         survived      pclass         age       sibsp       parch        fare\n",
       "count  891.000000  891.000000  891.000000  891.000000  891.000000  891.000000\n",
       "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
       "std      0.486592    0.836071   13.002015    1.102743    0.806057   49.693429\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   22.000000    0.000000    0.000000    7.910400\n",
       "50%      0.000000    3.000000   29.699118    0.000000    0.000000   14.454200\n",
       "75%      1.000000    3.000000   35.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "813f9152",
   "metadata": {},
   "source": [
    "# Conclusions\n",
    "\n",
    "In the dataset:\n",
    "* 38.38% survived\n",
    "* most people were in 3rd class\n",
    "* 50% passengers between age of 20 to 35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b7b5ce26",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>survived</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.069809</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pclass</th>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.331339</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.069809</td>\n",
       "      <td>-0.331339</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.232625</td>\n",
       "      <td>-0.179191</td>\n",
       "      <td>0.091566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sibsp</th>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.232625</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parch</th>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.179191</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fare</th>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.091566</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          survived    pclass       age     sibsp     parch      fare\n",
       "survived  1.000000 -0.338481 -0.069809 -0.035322  0.081629  0.257307\n",
       "pclass   -0.338481  1.000000 -0.331339  0.083081  0.018443 -0.549500\n",
       "age      -0.069809 -0.331339  1.000000 -0.232625 -0.179191  0.091566\n",
       "sibsp    -0.035322  0.083081 -0.232625  1.000000  0.414838  0.159651\n",
       "parch     0.081629  0.018443 -0.179191  0.414838  1.000000  0.216225\n",
       "fare      0.257307 -0.549500  0.091566  0.159651  0.216225  1.000000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select only numeric columns for correlation calculation\n",
    "numeric_columns = df.select_dtypes(include=['number'])\n",
    "correlation_matrix = numeric_columns.corr()\n",
    "correlation_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "273e92bd",
   "metadata": {},
   "source": [
    "See there is positive correlation between Survived and Fare, this make sense cause that more rich passanger were survived.\n",
    "and there is negative correlataion between Survived and Pclass, as the passenger class increases for first to second to third they were more likely to not survived.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "7cb98a2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of people survived are 62 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = \"survived\", data=df)\n",
    "print('No of people survived are',round(df['survived'].value_counts().values[0]*100/891),'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b5344adc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['who'] == 'man'].survived.groupby(df.survived).count().plot(kind='pie', figsize=(6, 6),explode=[0,0.05],autopct='%1.1f%%')\n",
    "plt.axis('equal')\n",
    "plt.legend([\"Perished\",\"Survived\"])\n",
    "plt.title(\"Male survival rate\")\n",
    "plt.show()\n",
    "\n",
    "df[df['who'] == 'woman'].survived.groupby(df.survived).count().plot(kind='pie', figsize=(6, 6),explode=[0,0.05],autopct='%1.1f%%')\n",
    "plt.axis('equal')\n",
    "plt.legend([\"Perished\",\"Survived\"])\n",
    "plt.title(\"Female survival rate\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "09324887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['pclass'] == 1].survived.groupby(df.survived).count().plot(kind='pie', figsize=(6, 6),explode=[0,0.05],autopct='%1.1f%%')\n",
    "plt.axis('equal')\n",
    "plt.legend([\"Perished\",\"Survived\"])\n",
    "plt.title(\"1st class survival rate\")\n",
    "plt.show()\n",
    "\n",
    "df[df['pclass'] == 2].survived.groupby(df.survived).count().plot(kind='pie', figsize=(6, 6),explode=[0,0.05],autopct='%1.1f%%')\n",
    "plt.axis('equal')\n",
    "plt.legend([\"Perished\",\"Survived\"])\n",
    "plt.title(\"2nd class survival rate\")\n",
    "plt.show()\n",
    "\n",
    "df[df['pclass'] == 3].survived.groupby(df.survived).count().plot(kind='pie', figsize=(6, 6),explode=[0,0.05],autopct='%1.1f%%')\n",
    "plt.axis('equal')\n",
    "plt.legend([\"Perished\",\"Survived\"])\n",
    "plt.title(\"3rd class survival rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1cdbcfe2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prash\\AppData\\Local\\Temp\\ipykernel_21780\\2771114101.py:10: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(age_surv[\"age\"], color='g')\n",
      "C:\\Users\\prash\\AppData\\Local\\Temp\\ipykernel_21780\\2771114101.py:11: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(age_died[\"age\"], color='r')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_died = df[(df[\"age\"] > 0) & \n",
    "                              (df[\"survived\"] == 0)]\n",
    "age_surv = df[(df[\"age\"] > 0) & \n",
    "                              (df[\"survived\"] == 1)]\n",
    "\n",
    "#figure size\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "# Ploting the 2 variables that we create and compare the two\n",
    "sns.distplot(age_surv[\"age\"], color='g')\n",
    "sns.distplot(age_died[\"age\"], color='r')\n",
    "plt.title(\"Distribuition and density by Age\",fontsize=20)\n",
    "plt.xlabel(\"Age\",fontsize=15)\n",
    "plt.ylabel(\"Distribuition Died and Survived\",fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "be2a12b3",
   "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>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass   age  sibsp  parch     fare embarked    who  adult_male  \\\n",
       "0         0       3  22.0      1      0   7.2500        S    man        True   \n",
       "1         1       1  38.0      1      0  71.2833        C  woman       False   \n",
       "2         1       3  26.0      0      0   7.9250        S  woman       False   \n",
       "3         1       1  35.0      1      0  53.1000        S  woman       False   \n",
       "4         0       3  35.0      0      0   8.0500        S    man        True   \n",
       "\n",
       "   alone  \n",
       "0  False  \n",
       "1  False  \n",
       "2   True  \n",
       "3  False  \n",
       "4   True  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e2e8763a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived_sex_counts = df.groupby(['survived', 'who']).size().unstack()\n",
    "survived_sex_counts.plot(kind='bar', stacked=True)\n",
    "plt.xlabel('Survived')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Survival Count by Sex')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "56794a9f",
   "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>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass   age  sibsp  parch     fare embarked    who  adult_male  \\\n",
       "0         0       3  22.0      1      0   7.2500        S    man        True   \n",
       "1         1       1  38.0      1      0  71.2833        C  woman       False   \n",
       "2         1       3  26.0      0      0   7.9250        S  woman       False   \n",
       "3         1       1  35.0      1      0  53.1000        S  woman       False   \n",
       "4         0       3  35.0      0      0   8.0500        S    man        True   \n",
       "\n",
       "   alone  \n",
       "0  False  \n",
       "1  False  \n",
       "2   True  \n",
       "3  False  \n",
       "4   True  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "7136d1be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of females who survived: 205\n",
      "Number of females who survived: 88\n"
     ]
    }
   ],
   "source": [
    "# Count the number of females who survived\n",
    "num_females_survived = df[(df[\"who\"] == \"woman\") & (df[\"survived\"] == 1)].shape[0]\n",
    "num_males_survived = df[(df[\"who\"] == \"man\") & (df[\"survived\"] == 1)].shape[0]\n",
    "\n",
    "\n",
    "print(\"Number of females who survived:\", num_females_survived)\n",
    "print(\"Number of females who survived:\", num_males_survived)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "11596fb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['survived']=df['survived'].astype('category')\n",
    "df['pclass']=df['pclass'].astype('category')\n",
    "df['who']=df['who'].astype('category')\n",
    "df['age']=df['age'].astype('int')\n",
    "df['embarked']=df['embarked'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "5d5f2fe9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 10 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   survived    891 non-null    category\n",
      " 1   pclass      891 non-null    category\n",
      " 2   age         891 non-null    int32   \n",
      " 3   sibsp       891 non-null    int64   \n",
      " 4   parch       891 non-null    int64   \n",
      " 5   fare        891 non-null    float64 \n",
      " 6   embarked    891 non-null    category\n",
      " 7   who         891 non-null    category\n",
      " 8   adult_male  891 non-null    bool    \n",
      " 9   alone       891 non-null    bool    \n",
      "dtypes: bool(2), category(4), float64(1), int32(1), int64(2)\n",
      "memory usage: 30.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "beb4f576",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<__array_function__ internals>:200: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
      "<__array_function__ internals>:200: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
      "C:\\Users\\prash\\anaconda3\\Lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1afd07bf950>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5661101",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}