Titanic Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a1025abe",
   "metadata": {},
   "source": [
    "# <U>EXPLORATORY DATA ANALYSIS</U>\n",
    "\n",
    "## 0. Pre-processing\n",
    "## 1. Analysis\n",
    "## 2. Feature Engineering\n",
    "## 3. Handling Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "028d97fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8b99ea67",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bffb97d7",
   "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>757</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>S</td>\n",
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       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
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       "      <th>149</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>42.0</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",
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       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
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       "      <th>857</th>\n",
       "      <td>1</td>\n",
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       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>26.5500</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>E</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>61.9792</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>B</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</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>583</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.1250</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>A</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.4000</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>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12.4750</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>E</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
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      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "757         0       2    male  18.0      0      0  11.5000        S  Second   \n",
       "149         0       2    male  42.0      0      0  13.0000        S  Second   \n",
       "507         1       1    male   NaN      0      0  26.5500        S   First   \n",
       "857         1       1    male  51.0      0      0  26.5500        S   First   \n",
       "54          0       1    male  65.0      0      1  61.9792        C   First   \n",
       "339         0       1    male  45.0      0      0  35.5000        S   First   \n",
       "583         0       1    male  36.0      0      0  40.1250        C   First   \n",
       "580         1       2  female  25.0      1      1  30.0000        S  Second   \n",
       "616         0       3    male  34.0      1      1  14.4000        S   Third   \n",
       "823         1       3  female  27.0      0      1  12.4750        S   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "757    man        True  NaN  Southampton    no   True  \n",
       "149    man        True  NaN  Southampton    no   True  \n",
       "507    man        True  NaN  Southampton   yes   True  \n",
       "857    man        True    E  Southampton   yes   True  \n",
       "54     man        True    B    Cherbourg    no  False  \n",
       "339    man        True  NaN  Southampton    no   True  \n",
       "583    man        True    A    Cherbourg    no   True  \n",
       "580  woman       False  NaN  Southampton   yes  False  \n",
       "616    man        True  NaN  Southampton    no  False  \n",
       "823  woman       False    E  Southampton   yes  False  "
      ]
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     "execution_count": 21,
     "metadata": {},
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   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9d0adf2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 15)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "70ec80dd",
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78c6edc1",
   "metadata": {},
   "source": [
    "## Categorical\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\n",
    "\n",
    "## mixed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "69143584",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sibsp'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c5e33a43",
   "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": "code",
   "execution_count": 26,
   "id": "4099af27",
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ed97d4c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['deck'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bc20b2a1",
   "metadata": {},
   "outputs": [
    {
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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",
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       "      <th>alone</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <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",
       "  </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  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  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3e0d4929",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age'].fillna(df['age'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a03875a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "embarked\n",
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "af09a073",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['embarked'].fillna('S',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "881cbecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['embark_town'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "76133901",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['survived']=df['survived'].astype('category')\n",
    "df['pclass']=df['pclass'].astype('category')\n",
    "df['sex']=df['sex'].astype('category')\n",
    "df['age']=df['age'].astype('int')\n",
    "df['embarked']=df['embarked'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "227b9bf0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   survived    891 non-null    category\n",
      " 1   pclass      891 non-null    category\n",
      " 2   sex         891 non-null    category\n",
      " 3   age         891 non-null    int32   \n",
      " 4   sibsp       891 non-null    int64   \n",
      " 5   parch       891 non-null    int64   \n",
      " 6   fare        891 non-null    float64 \n",
      " 7   embarked    891 non-null    category\n",
      " 8   class       891 non-null    category\n",
      " 9   who         891 non-null    object  \n",
      " 10  adult_male  891 non-null    bool    \n",
      " 11  alive       891 non-null    object  \n",
      " 12  alone       891 non-null    bool    \n",
      "dtypes: bool(2), category(5), float64(1), int32(1), int64(2), object(2)\n",
      "memory usage: 45.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6768a68c",
   "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>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.544332</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>13.013778</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\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>29.000000</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>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>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": [
       "              age       sibsp       parch        fare\n",
       "count  891.000000  891.000000  891.000000  891.000000\n",
       "mean    29.544332    0.523008    0.381594   32.204208\n",
       "std     13.013778    1.102743    0.806057   49.693429\n",
       "min      0.000000    0.000000    0.000000    0.000000\n",
       "25%     22.000000    0.000000    0.000000    7.910400\n",
       "50%     29.000000    0.000000    0.000000   14.454200\n",
       "75%     35.000000    1.000000    0.000000   31.000000\n",
       "max     80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9c4c641",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "663d10c6",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "67ea7ae1",
   "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": [
    "#Survived\n",
    "sns.countplot(df['survived'])\n",
    "print('No of people survived are',round(df['survived'].value_counts().values[0]*100/891),'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a3de541b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "549"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['survived'].value_counts().values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bd70d0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pclass\n",
    "sns.countplot(df['pclass'])\n",
    "print(round(df['pclass'].value_counts()*100/891),'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d9a5cad",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['survived'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a5dea14",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d30f367b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sex\n",
    "sns.countplot(df['sex'])\n",
    "print(round(df['sex'].value_counts()*100/891),'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3965994",
   "metadata": {},
   "outputs": [],
   "source": [
    "#age\n",
    "sns.distplot(df.age)\n",
    "print(df.age.skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01c4991f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.boxplot(df.age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a4b094a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sibsp\n",
    "sns.countplot(df['sibsp'])\n",
    "print(round(df['sibsp'].value_counts()*100/891),'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7deb7c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#parch\n",
    "sns.countplot(df['parch'])\n",
    "print(round(df['parch'].value_counts()*100/891),'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf7b3d15",
   "metadata": {},
   "outputs": [],
   "source": [
    " #fare\n",
    "sns.distplot(df.fare)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "328b4654",
   "metadata": {},
   "source": [
    "### Multivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "285a9f59",
   "metadata": {},
   "outputs": [],
   "source": [
    "#survival with pclass\n",
    "sns.countplot(df.survived,hue=df.pclass)\n",
    "\n",
    "import pandas as pd\n",
    "pd.crosstab(df.pclass,df.survived).apply(lambda r: r*100/r.sum(),axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3419fa8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#survival with sex\n",
    "sns.countplot(df.survived,hue=df.sex)\n",
    "\n",
    "pd.crosstab(df.sex,df.survived).apply(lambda r: r*100/r.sum(),axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7dc10bbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#survival with embarked\n",
    "sns.countplot(df.survived,hue=df.embarked)\n",
    "\n",
    "pd.crosstab(df.embarked,df.survived).apply(lambda r: r*100/r.sum(),axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ca34d55",
   "metadata": {},
   "outputs": [],
   "source": [
    "#survival with age\n",
    "sns.distplot(df[df.survived==0].age)\n",
    "sns.distplot(df[df.survived==1].age)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51c4b467",
   "metadata": {},
   "outputs": [],
   "source": [
    "#survival with fare\n",
    "sns.distplot(df[df.survived==0].fare)\n",
    "sns.distplot(df[df.survived==1].fare)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c771681d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.heatmap(df.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f14301e2",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "547d3532",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['family_size']=df['parch']+df['sibsp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5a7714f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "091bac8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   survived    891 non-null    category\n",
      " 1   pclass      891 non-null    category\n",
      " 2   sex         891 non-null    category\n",
      " 3   age         891 non-null    int32   \n",
      " 4   sibsp       891 non-null    int64   \n",
      " 5   parch       891 non-null    int64   \n",
      " 6   fare        891 non-null    float64 \n",
      " 7   embarked    891 non-null    category\n",
      " 8   class       891 non-null    category\n",
      " 9   who         891 non-null    object  \n",
      " 10  adult_male  891 non-null    bool    \n",
      " 11  alive       891 non-null    object  \n",
      " 12  alone       891 non-null    bool    \n",
      "dtypes: bool(2), category(5), float64(1), int32(1), int64(2), object(2)\n",
      "memory usage: 45.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7ff4e6ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['survived']=df['survived'].astype('category')\n",
    "df['pclass']=df['pclass'].astype('category')\n",
    "df['sex']=df['sex'].astype('category')\n",
    "df['age']=df['age'].astype('int')\n",
    "df['embarked']=df['embarked'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "59b2b262",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   survived    891 non-null    category\n",
      " 1   pclass      891 non-null    category\n",
      " 2   sex         891 non-null    category\n",
      " 3   age         891 non-null    int32   \n",
      " 4   sibsp       891 non-null    int64   \n",
      " 5   parch       891 non-null    int64   \n",
      " 6   fare        891 non-null    float64 \n",
      " 7   embarked    891 non-null    category\n",
      " 8   class       891 non-null    category\n",
      " 9   who         891 non-null    object  \n",
      " 10  adult_male  891 non-null    bool    \n",
      " 11  alive       891 non-null    object  \n",
      " 12  alone       891 non-null    bool    \n",
      "dtypes: bool(2), category(5), float64(1), int32(1), int64(2), object(2)\n",
      "memory usage: 45.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f46da87b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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