notebook.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('data/train.csv')\n",
    "df_test = pd.read_csv('data/test.csv')\n",
    "\n",
    "df = pd.concat([df_train.drop(['Survived'], axis=1), df_test], axis=0, sort=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the id, name, ticket columns\n",
    "\n",
    "df = df.drop(['PassengerId', 'Name', 'Ticket'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cabin\n",
    "\n",
    "# if the cabin is missing, replace it with 'X', otherwise extract the first letter of the cabin\n",
    "df['Cabin_Letter'] = df['Cabin'].apply(lambda x: 'X' if pd.isnull(x) else x[0])\n",
    "\n",
    "df.drop('Cabin', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cabin_Letter</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>T</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>1.130435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>1.243902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>2.380952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>X</td>\n",
       "      <td>2.617357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cabin_Letter    Pclass\n",
       "0            A  1.000000\n",
       "1            B  1.000000\n",
       "2            C  1.000000\n",
       "7            T  1.000000\n",
       "3            D  1.130435\n",
       "4            E  1.243902\n",
       "5            F  2.380952\n",
       "8            X  2.617357\n",
       "6            G  3.000000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cabin letter correlation with class\n",
    "df[['Cabin_Letter', 'Pclass']].groupby(['Cabin_Letter'], as_index=False).mean().sort_values(by='Pclass', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cabin_Letter</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>0.757576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>0.744681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>0.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0.593220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>X</td>\n",
       "      <td>0.299854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>T</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cabin_Letter  Survived\n",
       "3            D  0.757576\n",
       "4            E  0.750000\n",
       "1            B  0.744681\n",
       "5            F  0.615385\n",
       "2            C  0.593220\n",
       "6            G  0.500000\n",
       "0            A  0.466667\n",
       "8            X  0.299854\n",
       "7            T  0.000000"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get df_train from df\n",
    "df_train = pd.concat([df[:len(df_train)], df_train['Survived']], axis=1, sort=False)\n",
    "# Cabin letter correlation with survival\n",
    "df_train[['Cabin_Letter', 'Survived']].groupby(['Cabin_Letter'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3838383838383838"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Medium survival rate for all passengers\n",
    "df_train['Survived'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that people that had their cabin number registered were more likely to survive.\n",
    "This can be explained by the fact that they don't have recovered the cabin number of many of the people that died."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cabin letter information is redundant with class, so we only make two classes: cabin mapped and cabin not mapped\n",
    "\n",
    "df['Cabin_Mapped'] = df['Cabin_Letter'].apply(lambda x: 0 if x == 'X' else 1)\n",
    "\n",
    "# Drop Cabin_Letter\n",
    "\n",
    "df.drop('Cabin_Letter', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Missing Age\n",
    "df['Age_Missing'] = df['Age'].apply(lambda x: 1 if pd.isnull(x) else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2937853107344633"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get df_train from df\n",
    "df_train = pd.concat([df[:len(df_train)], df_train['Survived']], axis=1, sort=False)\n",
    "# Percentage of people with missing age that survived\n",
    "df_train[df_train['Age_Missing'] == 1]['Survived'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25949367088607594"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentage of people with missing age and cabin that survived\n",
    "\n",
    "df_train[(df_train['Age_Missing'] == 1) & (df_train['Cabin_Mapped'] == 0)]['Survived'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "158"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of people with missing age and cabin\n",
    "\n",
    "df_train[(df_train['Age_Missing'] == 1) & (df_train['Cabin_Mapped'] == 0)].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having a missing age and a missing cabin is an even better indicator than having just one of the two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new feature: age missing and cabin missing\n",
    "df['Age_Missing_Cabin_Missing'] = df.apply(lambda x: 1 if x['Age_Missing'] == 1 and x['Cabin_Mapped'] == 0 else 0, axis=1)\n",
    "\n",
    "# drop the age missing column and the cabin mapped column\n",
    "\n",
    "df.drop(['Age_Missing', 'Cabin_Mapped'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Age_Missing_Cabin_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex   Age  SibSp  Parch     Fare Embarked  \\\n",
       "0       3    male  22.0      1      0   7.2500        S   \n",
       "1       1  female  38.0      1      0  71.2833        C   \n",
       "2       3  female  26.0      0      0   7.9250        S   \n",
       "3       1  female  35.0      1      0  53.1000        S   \n",
       "4       3    male  35.0      0      0   8.0500        S   \n",
       "\n",
       "   Age_Missing_Cabin_Missing  \n",
       "0                          0  \n",
       "1                          0  \n",
       "2                          0  \n",
       "3                          0  \n",
       "4                          0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dummy variables for the categorical columns\n",
    "\n",
    "df = pd.get_dummies(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age         177\n",
      "Embarked      2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Number of missing values in each column of training data\n",
    "\n",
    "missing_val_count_by_column = (df_train.isnull().sum())\n",
    "print(missing_val_count_by_column[missing_val_count_by_column > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age       86\n",
      "Fare       1\n",
      "Cabin    327\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Number of missing values in each column of test data\n",
    "\n",
    "missing_val_count_by_column = (df_test.isnull().sum())\n",
    "print(missing_val_count_by_column[missing_val_count_by_column > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill the missing values with the mean of the column\n",
    "\n",
    "df = df.fillna(df.mean())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get df_train from df\n",
    "df_train = pd.concat([df[:len(df_train)], df_train['Survived']], axis=1, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Age_Missing_Cabin_Missing</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age  SibSp  Parch     Fare  Age_Missing_Cabin_Missing  Sex_female  \\\n",
       "0       3  22.0      1      0   7.2500                          0           0   \n",
       "1       1  38.0      1      0  71.2833                          0           1   \n",
       "2       3  26.0      0      0   7.9250                          0           1   \n",
       "3       1  35.0      1      0  53.1000                          0           1   \n",
       "4       3  35.0      0      0   8.0500                          0           0   \n",
       "\n",
       "   Sex_male  Embarked_C  Embarked_Q  Embarked_S  Survived  \n",
       "0         1           0           0           1         0  \n",
       "1         0           1           0           0         1  \n",
       "2         0           0           0           1         1  \n",
       "3         0           0           0           1         1  \n",
       "4         1           0           0           1         0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histograms of each column\n",
    "\n",
    "for col in df_train.columns:\n",
    "    df_train.hist(column=col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the correlation matrix\n",
    "\n",
    "sns.heatmap(df_train.corr())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and validation sets\n",
    "\n",
    "X = df_train.drop(['Survived'], axis=1)\n",
    "Y = df_train['Survived']\n",
    "\n",
    "X_train, X_val, Y_train, Y_val = train_test_split(X, Y, test_size=0.2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a baseline model\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logistic_regression = LogisticRegression(solver='lbfgs', max_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7541899441340782"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model\n",
    "\n",
    "logistic_regression.fit(X_train, Y_train)\n",
    "\n",
    "logistic_regression.score(X_val, Y_val)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = xgb.XGBClassifier(\n",
    "    n_estimators=10000,\n",
    "    max_depth=5,\n",
    "    learning_rate=0.1,\n",
    "    early_stopping_rounds=5,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-logloss:0.65167\n",
      "[1]\tvalidation_0-logloss:0.61798\n",
      "[2]\tvalidation_0-logloss:0.59014\n",
      "[3]\tvalidation_0-logloss:0.56725\n",
      "[4]\tvalidation_0-logloss:0.54687\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5]\tvalidation_0-logloss:0.53038\n",
      "[6]\tvalidation_0-logloss:0.51667\n",
      "[7]\tvalidation_0-logloss:0.50488\n",
      "[8]\tvalidation_0-logloss:0.49458\n",
      "[9]\tvalidation_0-logloss:0.48589\n",
      "[10]\tvalidation_0-logloss:0.47882\n",
      "[11]\tvalidation_0-logloss:0.47016\n",
      "[12]\tvalidation_0-logloss:0.46352\n",
      "[13]\tvalidation_0-logloss:0.46020\n",
      "[14]\tvalidation_0-logloss:0.45512\n",
      "[15]\tvalidation_0-logloss:0.45120\n",
      "[16]\tvalidation_0-logloss:0.44882\n",
      "[17]\tvalidation_0-logloss:0.44656\n",
      "[18]\tvalidation_0-logloss:0.44335\n",
      "[19]\tvalidation_0-logloss:0.44234\n",
      "[20]\tvalidation_0-logloss:0.44223\n",
      "[21]\tvalidation_0-logloss:0.44139\n",
      "[22]\tvalidation_0-logloss:0.44046\n",
      "[23]\tvalidation_0-logloss:0.44003\n",
      "[24]\tvalidation_0-logloss:0.44078\n",
      "[25]\tvalidation_0-logloss:0.44044\n",
      "[26]\tvalidation_0-logloss:0.44186\n",
      "[27]\tvalidation_0-logloss:0.44197\n",
      "[28]\tvalidation_0-logloss:0.44206\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=5,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=10000, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=5,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=10000, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=5,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=10000, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model\n",
    "xgb_model.fit(X_train, Y_train, eval_set=[(X_val, Y_val)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8100558659217877"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test the model\n",
    "\n",
    "xgb_model.score(X_val, Y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Submission file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the test set\n",
    "df_test = df[df_train.shape[0]:]\n",
    "predictions = xgb_model.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the predictions to a CSV file\n",
    "\n",
    "output = pd.DataFrame({'PassengerId': pd.read_csv('data/test.csv').PassengerId, 'Survived': predictions})\n",
    "pd.DataFrame.to_csv(output, 'data/predictions.csv', index=False)"
   ]
  }
 ],
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