titanic_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "da73f2cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#writing the necessary imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "50eddfab",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the titanic data as a csv file\n",
    "titanic_data = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dbb9aa84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#observing th etop 5 values from the dataset\n",
    "titanic_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "75f9a857",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total number of rows and columns\n",
    "titanic_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "25319659",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "487d0351",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total null values each column contains\n",
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "06d7e9ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2409b0e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describing titanic dataset\n",
    "titanic_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "74061d6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0500     43\n",
       "13.0000    42\n",
       "7.8958     38\n",
       "7.7500     34\n",
       "26.0000    31\n",
       "           ..\n",
       "35.0000     1\n",
       "28.5000     1\n",
       "6.2375      1\n",
       "14.0000     1\n",
       "10.5167     1\n",
       "Name: Fare, Length: 248, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data['Fare'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cd4dcdc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_columns = ['PassengerId','Survived','Age','SibSp','Parch','Fare']\n",
    "\n",
    "fig, axs = plt.subplots(2, 3, sharex=False, sharey=False, figsize=(20, 15))\n",
    "\n",
    "counter = 0\n",
    "for num_column in num_columns:\n",
    "    \n",
    "    trace_x = counter // 3\n",
    "    trace_y = counter % 3\n",
    "    \n",
    "    axs[trace_x, trace_y].hist(titanic_data[num_column])\n",
    "    \n",
    "    axs[trace_x, trace_y].set_title(num_column)\n",
    "    \n",
    "    counter += 1\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c987a0de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.905723905723907"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(len (titanic_data[titanic_data['Parch'] > 0] ) / len(titanic_data)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "55427602",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8978675645342313"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(len (titanic_data[titanic_data['Age'] > 65] ) / len(titanic_data)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d6774564",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.794612794612794"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(len (titanic_data[titanic_data['Fare'] > 67] ) / len(titanic_data)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c8e16eea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.16273849607183"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(len (titanic_data[titanic_data['SibSp'] > 2.5] ) / len(titanic_data)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3938b653",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data=titanic_data.drop(columns='Cabin',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e354eca9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "86edc629",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data['Age'].fillna(titanic_data['Age'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0cf53a43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Embarked       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d95e0a86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    S\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(titanic_data['Embarked'].mode())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cb4de891",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S\n"
     ]
    }
   ],
   "source": [
    "print(titanic_data['Embarked'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d24ed8db",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data['Embarked'].fillna(titanic_data['Embarked'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "13a09c43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "af04c588",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>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>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>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>446.000000</td>\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>668.500000</td>\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>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   13.002015    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   22.000000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   29.699118    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   35.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0efc43c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c6753289",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7fc05f71",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Roaming\\Python\\Python39\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Survived',data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "852c6d6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Roaming\\Python\\Python39\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sex', ylabel='count'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Sex',data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "99a68aee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Roaming\\Python\\Python39\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sex', ylabel='count'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Sex',hue='Survived',data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7f58d147",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Roaming\\Python\\Python39\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='count'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Pclass',data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "83fa7edd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Roaming\\Python\\Python39\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='count'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Pclass',hue='Survived',data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4b833f96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ca76d774",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    646\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "7f092a29",
   "metadata": {},
   "outputs": [],
   "source": [
    "le=LabelEncoder()\n",
    "label2=le.fit_transform(titanic_data[\"Sex\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "52df0478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0,\n",
       "       1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1,\n",
       "       1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1,\n",
       "       0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1,\n",
       "       1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0,\n",
       "       1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0,\n",
       "       1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0,\n",
       "       0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,\n",
       "       0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n",
       "       0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
       "       1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1,\n",
       "       1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ffcbe8ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>1</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name  Sex        Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    0  22.000000      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...    1  38.000000      1   \n",
       "2                               Heikkinen, Miss. Laina    1  26.000000      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)    1  35.000000      1   \n",
       "4                             Allen, Mr. William Henry    0  35.000000      0   \n",
       "..                                                 ...  ...        ...    ...   \n",
       "886                              Montvila, Rev. Juozas    0  27.000000      0   \n",
       "887                       Graham, Miss. Margaret Edith    1  19.000000      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"    1  29.699118      1   \n",
       "889                              Behr, Mr. Karl Howell    0  26.000000      0   \n",
       "890                                Dooley, Mr. Patrick    0  32.000000      0   \n",
       "\n",
       "     Parch            Ticket     Fare  Embarked  \n",
       "0        0         A/5 21171   7.2500         2  \n",
       "1        0          PC 17599  71.2833         0  \n",
       "2        0  STON/O2. 3101282   7.9250         2  \n",
       "3        0            113803  53.1000         2  \n",
       "4        0            373450   8.0500         2  \n",
       "..     ...               ...      ...       ...  \n",
       "886      0            211536  13.0000         2  \n",
       "887      0            112053  30.0000         2  \n",
       "888      2        W./C. 6607  23.4500         2  \n",
       "889      0            111369  30.0000         0  \n",
       "890      0            370376   7.7500         1  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data[\"Sex\"]=label2\n",
    "titanic_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "38138cf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "le=LabelEncoder()\n",
    "label=le.fit_transform(titanic_data[\"Embarked\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f95132fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 2, 2, 2, 1, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 1, 2, 2, 0, 2, 2,\n",
       "       1, 2, 2, 2, 0, 2, 1, 2, 0, 0, 1, 2, 0, 2, 0, 2, 2, 0, 2, 2, 0, 0,\n",
       "       1, 2, 1, 1, 0, 2, 2, 2, 0, 2, 0, 2, 2, 0, 2, 2, 0, 2, 2, 2, 0, 0,\n",
       "       2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1,\n",
       "       2, 0, 2, 2, 0, 2, 1, 2, 0, 2, 2, 2, 0, 2, 2, 0, 1, 2, 0, 2, 0, 2,\n",
       "       2, 2, 2, 0, 2, 2, 2, 0, 0, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 0, 2,\n",
       "       2, 0, 2, 2, 2, 0, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 0, 0, 1, 2,\n",
       "       1, 2, 2, 2, 2, 0, 2, 2, 2, 0, 1, 0, 2, 2, 2, 2, 1, 0, 2, 2, 0, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 1,\n",
       "       2, 2, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 0, 2, 1, 2, 2, 2,\n",
       "       1, 2, 2, 2, 2, 2, 2, 2, 2, 0, 1, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 0,\n",
       "       2, 2, 2, 1, 2, 0, 0, 2, 2, 0, 0, 2, 2, 0, 1, 1, 2, 1, 2, 2, 0, 0,\n",
       "       0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 1, 2, 2, 0, 2, 2, 2, 0,\n",
       "       1, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       0, 2, 0, 2, 2, 2, 1, 1, 2, 0, 0, 2, 1, 2, 0, 0, 1, 0, 0, 2, 2, 0,\n",
       "       2, 0, 2, 0, 0, 2, 0, 0, 2, 2, 2, 2, 2, 2, 1, 0, 2, 2, 2, 0, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2,\n",
       "       2, 2, 0, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 0, 0, 2, 0, 2, 2, 2, 1, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 1, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       0, 2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 0, 0, 2, 2, 2, 2, 1, 1, 2, 2, 0,\n",
       "       2, 2, 2, 2, 1, 2, 2, 0, 2, 2, 2, 1, 2, 2, 2, 2, 0, 0, 0, 1, 2, 2,\n",
       "       2, 2, 2, 0, 0, 0, 2, 2, 2, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 0, 2, 2,\n",
       "       0, 2, 1, 0, 2, 2, 0, 0, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2,\n",
       "       2, 1, 2, 2, 2, 2, 0, 2, 2, 0, 2, 0, 0, 2, 2, 0, 2, 2, 2, 0, 2, 1,\n",
       "       2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 1, 1, 2, 2,\n",
       "       2, 2, 2, 2, 0, 2, 0, 2, 2, 2, 1, 2, 2, 1, 2, 2, 0, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 0, 2, 2, 0, 0, 2, 0, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 0,\n",
       "       2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 1, 0,\n",
       "       2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 0, 2, 2, 2, 1, 0, 2, 0, 2, 0, 1,\n",
       "       2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 2, 1, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 1, 2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2,\n",
       "       2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 0, 1, 1, 2,\n",
       "       2, 2, 2, 0, 2, 2, 1, 2, 1, 2, 0, 2, 2, 2, 2, 2, 2, 1, 2, 0, 1, 2,\n",
       "       2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 1, 2, 0, 1, 2, 0, 2, 0, 2, 2, 0,\n",
       "       2, 2, 2, 0, 2, 2, 0, 0, 2, 2, 2, 0, 2, 0, 2, 2, 0, 2, 2, 2, 2, 2,\n",
       "       0, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 0,\n",
       "       2, 2, 2, 2, 2, 1, 2, 2, 2, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "359c2df2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "edb280b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>1</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name  Sex        Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    0  22.000000      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...    1  38.000000      1   \n",
       "2                               Heikkinen, Miss. Laina    1  26.000000      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)    1  35.000000      1   \n",
       "4                             Allen, Mr. William Henry    0  35.000000      0   \n",
       "..                                                 ...  ...        ...    ...   \n",
       "886                              Montvila, Rev. Juozas    0  27.000000      0   \n",
       "887                       Graham, Miss. Margaret Edith    1  19.000000      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"    1  29.699118      1   \n",
       "889                              Behr, Mr. Karl Howell    0  26.000000      0   \n",
       "890                                Dooley, Mr. Patrick    0  32.000000      0   \n",
       "\n",
       "     Parch            Ticket     Fare  Embarked  \n",
       "0        0         A/5 21171   7.2500         2  \n",
       "1        0          PC 17599  71.2833         0  \n",
       "2        0  STON/O2. 3101282   7.9250         2  \n",
       "3        0            113803  53.1000         2  \n",
       "4        0            373450   8.0500         2  \n",
       "..     ...               ...      ...       ...  \n",
       "886      0            211536  13.0000         2  \n",
       "887      0            112053  30.0000         2  \n",
       "888      2        W./C. 6607  23.4500         2  \n",
       "889      0            111369  30.0000         0  \n",
       "890      0            370376   7.7500         1  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data[\"Embarked\"]=label\n",
    "titanic_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "b566bc82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    0  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    1  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    0  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare  Embarked  \n",
       "0         A/5 21171   7.2500         2  \n",
       "1          PC 17599  71.2833         0  \n",
       "2  STON/O2. 3101282   7.9250         2  \n",
       "3            113803  53.1000         2  \n",
       "4            373450   8.0500         2  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b4009b76",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=titanic_data.drop(columns=['PassengerId','Name','Ticket','Survived'],axis=1)\n",
    "y=titanic_data['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "372c9d48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Pclass  Sex        Age  SibSp  Parch     Fare  Embarked\n",
      "0         3    0  22.000000      1      0   7.2500         2\n",
      "1         1    1  38.000000      1      0  71.2833         0\n",
      "2         3    1  26.000000      0      0   7.9250         2\n",
      "3         1    1  35.000000      1      0  53.1000         2\n",
      "4         3    0  35.000000      0      0   8.0500         2\n",
      "..      ...  ...        ...    ...    ...      ...       ...\n",
      "886       2    0  27.000000      0      0  13.0000         2\n",
      "887       1    1  19.000000      0      0  30.0000         2\n",
      "888       3    1  29.699118      1      2  23.4500         2\n",
      "889       1    0  26.000000      0      0  30.0000         0\n",
      "890       3    0  32.000000      0      0   7.7500         1\n",
      "\n",
      "[891 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "5a96de70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      0\n",
      "1      1\n",
      "2      1\n",
      "3      1\n",
      "4      0\n",
      "      ..\n",
      "886    0\n",
      "887    1\n",
      "888    0\n",
      "889    1\n",
      "890    0\n",
      "Name: Survived, Length: 891, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2b66eaec",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a19c560e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 7) (712, 7) (179, 7)\n"
     ]
    }
   ],
   "source": [
    "print(x.shape,x_train.shape,x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b98b1238",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d6cd3741",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\python 39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "96e31068",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_prediction=model.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "1d778870",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1\n",
      " 0 0 0 0 0 0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 1 0 1 0 0 1\n",
      " 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 1 1 0 1 0 0 0 0 0 1 0 0 0\n",
      " 1 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 1 1 1 0 0 1 0 0\n",
      " 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 1 1 1\n",
      " 0 0 0 1 0 0 0 1 0 0 1 0 0 1 1 1 0 1 0 0 0 0 0 1 1 0 1 1 1 1 0 0 0 0 0 0 0\n",
      " 0 1 0 0 1 1 1 0 0 1 0 1 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0\n",
      " 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0\n",
      " 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 1 0 0 0 1 0 1 0 0 0 0 0 0 1 1 0 1 1\n",
      " 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 1 1 1 0 1 0 0 0 0 1 1 0 0 0 1 0 1 1 1 0 0\n",
      " 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 1 1 0 1 1 0 0 0\n",
      " 0 1 0 1 0 0 1 1 0 0 0 0 1 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0\n",
      " 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 1 1 0 1 0 0 1 0 0 0 1 1 0 1 0\n",
      " 0 0 0 0 1 0 0 1 0 1 1 0 0 1 0 0 1 0 0 0 1 0 1 1 0 0 1 1 0 1 0 1 1 1 0 1 0\n",
      " 0 1 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0\n",
      " 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 1 1 0 0 0 1 0 1 0 0 0 1 1 1 0 0 1 1\n",
      " 0 0 0 1 0 1 0 0 0 0 0 1 1 0 1 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0\n",
      " 1 0 0 1 0 1 0 0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0\n",
      " 0 0 0 1 1 0 0 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(x_train_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "508378d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data_accuracy=accuracy_score(x_train_prediction,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "4f5cfa25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81.17977528089888\n"
     ]
    }
   ],
   "source": [
    "print(training_data_accuracy*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e6455ecd",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_prediction=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "4cc891bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 1 1 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1\n",
      " 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0\n",
      " 1 0 0 0 1 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 1 0 1 1 0 1 1 0 1 1 0 0 0 0\n",
      " 0 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 1 0 1 0 0\n",
      " 0 1 0 0 0 0 1 0 0 1 1 0 1 0 0 0 1 1 0 0 1 0 0 1 1 1 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(x_test_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "c96d9b50",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_accuracy=accuracy_score(x_test_prediction,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "bd576f81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78.77094972067039\n"
     ]
    }
   ],
   "source": [
    "print(test_data_accuracy*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b69f66e9",
   "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}