sklearn_log_reg_notes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "78fa5ad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c15e57e",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('titanic_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f20b4e50",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "81f46b4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e265d90",
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_age(cols):\n",
    "    Age = cols[0]\n",
    "    Pclass = cols[1]\n",
    "    \n",
    "    if pd.isnull(Age):\n",
    "\n",
    "        if Pclass == 1:\n",
    "            return 37\n",
    "\n",
    "        elif Pclass == 2:\n",
    "            return 29\n",
    "\n",
    "        else:\n",
    "            return 24\n",
    "\n",
    "    else:\n",
    "        return Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e1d7c3b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Age'] = train[['Age','Pclass']].apply(impute_age,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56f6ec49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "059de684",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Cabin',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7435887c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </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 Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "df362f41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.dropna(inplace=True)\n",
    "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3f6f6638",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 889 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  889 non-null    int64  \n",
      " 1   Survived     889 non-null    int64  \n",
      " 2   Pclass       889 non-null    int64  \n",
      " 3   Name         889 non-null    object \n",
      " 4   Sex          889 non-null    object \n",
      " 5   Age          889 non-null    float64\n",
      " 6   SibSp        889 non-null    int64  \n",
      " 7   Parch        889 non-null    int64  \n",
      " 8   Ticket       889 non-null    object \n",
      " 9   Fare         889 non-null    float64\n",
      " 10  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 83.3+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "94d3de7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
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       "2            3         1       3   \n",
       "3            4         1       1   \n",
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       "\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 Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
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     "execution_count": 12,
     "metadata": {},
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   "source": [
    "train.head()"
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  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "20eafa84",
   "metadata": {},
   "outputs": [],
   "source": [
    "sex = pd.get_dummies(train['Sex'],drop_first=True)\n",
    "embark = pd.get_dummies(train['Embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fb63c826",
   "metadata": {},
   "outputs": [
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       "   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 Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
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     "metadata": {},
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    "train.head()"
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  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8416daab",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6a3b9561",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.concat([train,sex,embark],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "122c2b7a",
   "metadata": {},
   "outputs": [
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       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
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       "4            5         0       3  35.0      0      0   8.0500     1  0  1"
      ]
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     "execution_count": 17,
     "metadata": {},
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    "train.head()"
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   "cell_type": "code",
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   "outputs": [
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     "execution_count": 19,
     "metadata": {},
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   "source": [
    "embark"
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  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ee8beea9",
   "metadata": {},
   "outputs": [],
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    "train.drop(\"PassengerId\", axis=1,inplace=True)"
   ]
  },
  {
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   "execution_count": 21,
   "id": "492ca936",
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0         0       3  22.0      1      0   7.2500     1  0  1\n",
       "1         1       1  38.0      1      0  71.2833     0  0  0\n",
       "2         1       3  26.0      0      0   7.9250     0  0  1\n",
       "3         1       1  35.0      1      0  53.1000     0  0  1\n",
       "4         0       3  35.0      0      0   8.0500     1  0  1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33816262",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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