supervised/classification/titanic/titanic-survival-prediction-model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <div style=\"text-align: center; background-color: #2A3990; color: white; padding: 14px; line-height: 1;border-radius:14px\"> Survival in Titatnic</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# - About Titanic's Sinking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* At 11:40 pm (ship's time) on 14 April, lookout Frederick Fleet spotted an iceberg immediately ahead of Titanic and alerted the bridge. First Officer William Murdoch ordered the ship to be steered around the obstacle and the engines to be reversed, but it was too late; the starboard side of Titanic struck the iceberg, creating a series of holes below the waterline. (source: Aldridge, Rebecca (2008). The Sinking of the Titanic. New York: Infobase Publishing. ISBN 978-0-7910-9643-7.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <div style=\"text-align: center; background-color: #9C254D; color: white; padding: 14px; line-height: 1;border-radius:14px\"> Import Libraries</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Handle data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#Plot data\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#Style data\n",
    "sns.set()\n",
    "\n",
    "#Feature engineer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "#Models\n",
    "import xgboost as xgb\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from lightgbm.sklearn import LGBMClassifier\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "#Model selection\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>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",
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       "    <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",
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       "    <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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_csv('train.csv')\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <div style=\"text-align: center; background-color: #9C254D; color: white; padding: 14px; line-height: 1;border-radius:14px\"> Exploration Data Analysis</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
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       "      <td>891</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>3</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>347082</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>644</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "        PassengerId    Survived      Pclass                     Name   Sex  \\\n",
       "count    891.000000  891.000000  891.000000                      891   891   \n",
       "unique          NaN         NaN         NaN                      891     2   \n",
       "top             NaN         NaN         NaN  Braund, Mr. Owen Harris  male   \n",
       "freq            NaN         NaN         NaN                        1   577   \n",
       "mean     446.000000    0.383838    2.308642                      NaN   NaN   \n",
       "std      257.353842    0.486592    0.836071                      NaN   NaN   \n",
       "min        1.000000    0.000000    1.000000                      NaN   NaN   \n",
       "25%      223.500000    0.000000    2.000000                      NaN   NaN   \n",
       "50%      446.000000    0.000000    3.000000                      NaN   NaN   \n",
       "75%      668.500000    1.000000    3.000000                      NaN   NaN   \n",
       "max      891.000000    1.000000    3.000000                      NaN   NaN   \n",
       "\n",
       "               Age       SibSp       Parch  Ticket        Fare    Cabin  \\\n",
       "count   714.000000  891.000000  891.000000     891  891.000000      204   \n",
       "unique         NaN         NaN         NaN     681         NaN      147   \n",
       "top            NaN         NaN         NaN  347082         NaN  B96 B98   \n",
       "freq           NaN         NaN         NaN       7         NaN        4   \n",
       "mean     29.699118    0.523008    0.381594     NaN   32.204208      NaN   \n",
       "std      14.526497    1.102743    0.806057     NaN   49.693429      NaN   \n",
       "min       0.420000    0.000000    0.000000     NaN    0.000000      NaN   \n",
       "25%      20.125000    0.000000    0.000000     NaN    7.910400      NaN   \n",
       "50%      28.000000    0.000000    0.000000     NaN   14.454200      NaN   \n",
       "75%      38.000000    1.000000    0.000000     NaN   31.000000      NaN   \n",
       "max      80.000000    8.000000    6.000000     NaN  512.329200      NaN   \n",
       "\n",
       "       Embarked  \n",
       "count       889  \n",
       "unique        3  \n",
       "top           S  \n",
       "freq        644  \n",
       "mean        NaN  \n",
       "std         NaN  \n",
       "min         NaN  \n",
       "25%         NaN  \n",
       "50%         NaN  \n",
       "75%         NaN  \n",
       "max         NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's drop Cabin, it have a lot of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = train_data.drop(['Cabin'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have to fill the missing values of Age, there are a few, we can fill it with the median. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['Age'] = data1['Age'].fillna(data1['Age'].median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, In Embarked we will fill it with the mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['Embarked'] = data1['Embarked'].fillna(data1['Embarked'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</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</td>\n",
       "      <td>891</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>347082</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>646</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.361582</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.019697</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId    Survived      Pclass                     Name   Sex  \\\n",
       "count    891.000000  891.000000  891.000000                      891   891   \n",
       "unique          NaN         NaN         NaN                      891     2   \n",
       "top             NaN         NaN         NaN  Braund, Mr. Owen Harris  male   \n",
       "freq            NaN         NaN         NaN                        1   577   \n",
       "mean     446.000000    0.383838    2.308642                      NaN   NaN   \n",
       "std      257.353842    0.486592    0.836071                      NaN   NaN   \n",
       "min        1.000000    0.000000    1.000000                      NaN   NaN   \n",
       "25%      223.500000    0.000000    2.000000                      NaN   NaN   \n",
       "50%      446.000000    0.000000    3.000000                      NaN   NaN   \n",
       "75%      668.500000    1.000000    3.000000                      NaN   NaN   \n",
       "max      891.000000    1.000000    3.000000                      NaN   NaN   \n",
       "\n",
       "               Age       SibSp       Parch  Ticket        Fare Embarked  \n",
       "count   891.000000  891.000000  891.000000     891  891.000000      891  \n",
       "unique         NaN         NaN         NaN     681         NaN        3  \n",
       "top            NaN         NaN         NaN  347082         NaN        S  \n",
       "freq           NaN         NaN         NaN       7         NaN      646  \n",
       "mean     29.361582    0.523008    0.381594     NaN   32.204208      NaN  \n",
       "std      13.019697    1.102743    0.806057     NaN   49.693429      NaN  \n",
       "min       0.420000    0.000000    0.000000     NaN    0.000000      NaN  \n",
       "25%      22.000000    0.000000    0.000000     NaN    7.910400      NaN  \n",
       "50%      28.000000    0.000000    0.000000     NaN   14.454200      NaN  \n",
       "75%      35.000000    1.000000    0.000000     NaN   31.000000      NaN  \n",
       "max      80.000000    8.000000    6.000000     NaN  512.329200      NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Fare')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(ncols=2,figsize=(15,5))\n",
    "sns.boxplot(data1['Age'],ax= ax1).set_title('Age')\n",
    "sns.boxplot(data1['Fare'],ax=ax2).set_title('Fare')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There're many outliers in both columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '646'), Text(0, 0, '168'), Text(0, 0, '77')]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1,ax2,ax3) = plt.subplots(1,3, figsize=(15,3))\n",
    "sns.barplot(x=data1['SibSp'].unique(),y=data1['SibSp'].value_counts(),ax=ax1)\n",
    "ax1.bar_label(ax1.containers[0])\n",
    "sns.barplot(x=data1['Embarked'].unique(),y=data1['Embarked'].value_counts(),ax=ax2)\n",
    "ax2.bar_label(ax2.containers[0])\n",
    "sns.barplot(x=data1['Parch'].unique(),y=data1['Parch'].value_counts(),ax=ax3)\n",
    "ax2.bar_label(ax2.containers[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For SibSp the values are concentred in 1. (Number of siblings)\n",
    "\n",
    "For Embarked the values are concentred in S port.\n",
    "\n",
    "For Parch the values are concentred in 0. (Number of parents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '577'), Text(0, 0, '314')]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1,ax2,ax3) = plt.subplots(1,3, figsize=(15,3))\n",
    "sns.barplot(x=data1['Survived'].unique(),y=data1['Survived'].value_counts(),ax=ax1)\n",
    "ax1.bar_label(ax1.containers[0])\n",
    "sns.barplot(x=data1['Pclass'].unique(),y=data1['Pclass'].value_counts(),ax=ax2)\n",
    "ax2.bar_label(ax2.containers[0])\n",
    "sns.barplot(x=data1['Sex'].unique(),y=data1['Sex'].value_counts(),ax=ax3)\n",
    "ax3.bar_label(ax3.containers[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There're more people who died.\n",
    "\n",
    "There're more people in the 3rd class.\n",
    "\n",
    "There're more men than women."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/06/pvd15jn934z7w3kzys42mzlc0000gn/T/ipykernel_5048/3962102600.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  sns.heatmap(data1.corr(),annot=True,linewidths=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 13,
     "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": [
    "sns.heatmap(data1.corr(),annot=True,linewidths=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the matrix, it can be seen that Pclass and Fare have a strong relation with Survived (target). For Age, SibSp and Parch there is a relation but not strong."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <div style=\"text-align: center; background-color: #9C254D; color: white; padding: 14px; line-height: 1;border-radius:14px\"> Feature Engineering</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"text-align: left; background-color: #D23369; font-family:newtimeroman; color: white; padding: 10px; line-height: 1;border-radius:14px\"><b>Removing Outliers</b></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1 = np.percentile(data1['Age'],25)\n",
    "q3 = np.percentile(data1['Age'],75)\n",
    "iqr = q3 - q1\n",
    "data2 = data1[(data1['Age'] < (q3+1.5*iqr)) & (data1['Age'] > (q1-1.5*iqr))]\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "\n",
       "                                           Name     Sex   Age  SibSp  Parch  \\\n",
       "0                       Braund, Mr. Owen Harris    male  22.0      1      0   \n",
       "2                        Heikkinen, Miss. Laina  female  26.0      0      0   \n",
       "3  Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1      0   \n",
       "4                      Allen, Mr. William Henry    male  35.0      0      0   \n",
       "5                              Moran, Mr. James    male  28.0      0      0   \n",
       "\n",
       "             Ticket     Fare Embarked  \n",
       "0         A/5 21171   7.2500        S  \n",
       "2  STON/O2. 3101282   7.9250        S  \n",
       "3            113803  53.1000        S  \n",
       "4            373450   8.0500        S  \n",
       "5            330877   8.4583        Q  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1 = np.percentile(data2['Fare'],25)\n",
    "q3 = np.percentile(data2['Fare'],75)\n",
    "iqr = q3 - q1\n",
    "data3 = data2[(data2['Fare'] < (q3+1.5*iqr)) & (data2['Fare'] > (q1-1.5*iqr))]\n",
    "data3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Fare')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(ncols=2,figsize=(15,5))\n",
    "sns.boxplot(data3['Age'],ax= ax1).set_title('Age')\n",
    "sns.boxplot(data3['Fare'],ax=ax2).set_title('Fare')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"text-align: left; background-color: #D23369; font-family:newtimeroman; color: white; padding: 10px; line-height: 1;border-radius:14px\"><b>Encoding Gender</b></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/06/pvd15jn934z7w3kzys42mzlc0000gn/T/ipykernel_5048/26306163.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data3['gender_encode'] = gender_encode\n"
     ]
    },
    {
     "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",
       "      <th>gender_encode</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",
       "      <td>1</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",
       "      <td>0</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",
       "      <td>0</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "\n",
       "                                           Name     Sex   Age  SibSp  Parch  \\\n",
       "0                       Braund, Mr. Owen Harris    male  22.0      1      0   \n",
       "2                        Heikkinen, Miss. Laina  female  26.0      0      0   \n",
       "3  Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1      0   \n",
       "4                      Allen, Mr. William Henry    male  35.0      0      0   \n",
       "5                              Moran, Mr. James    male  28.0      0      0   \n",
       "\n",
       "             Ticket     Fare Embarked  gender_encode  \n",
       "0         A/5 21171   7.2500        S              1  \n",
       "2  STON/O2. 3101282   7.9250        S              0  \n",
       "3            113803  53.1000        S              0  \n",
       "4            373450   8.0500        S              1  \n",
       "5            330877   8.4583        Q              1  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = LabelEncoder()\n",
    "gender_encode = encoder.fit_transform(data3['Sex'])\n",
    "data3['gender_encode'] = gender_encode\n",
    "data3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"text-align: left; background-color: #D23369; font-family:newtimeroman; color: white; padding: 10px; line-height: 1;border-radius:14px\"><b>Creating Dummies Variables</b></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we have to drop the columns that we won't be using, in this case PassengerId, Name, Ticket and Sex. The reason are the following:\n",
    "\n",
    "PassengerId: It is the index\n",
    "\n",
    "Name: There are only uniques values\n",
    "\n",
    "Ticket: There are only uniques values\n",
    "\n",
    "Sex: We created a gender encode before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_clean = data3.drop(['PassengerId','Name','Ticket','Sex'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>gender_encode</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch     Fare  gender_encode  Embarked_Q  \\\n",
       "0         0       3  22.0      1      0   7.2500              1           0   \n",
       "2         1       3  26.0      0      0   7.9250              0           0   \n",
       "3         1       1  35.0      1      0  53.1000              0           0   \n",
       "4         0       3  35.0      0      0   8.0500              1           0   \n",
       "5         0       3  28.0      0      0   8.4583              1           1   \n",
       "\n",
       "   Embarked_S  \n",
       "0           1  \n",
       "2           1  \n",
       "3           1  \n",
       "4           1  \n",
       "5           0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_dummies = pd.get_dummies(data_clean,drop_first=True)\n",
    "data_with_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It set the option drop_first = True, because it reduce the multicollinearity. If Embarked_Q = 0 and Embarked_S = 0, it mean that the passenger had embarked in C."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <div style=\"text-align: center; background-color: #9C254D; color: white; padding: 14px; line-height: 1;border-radius:14px\"> Model Selection</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = data_with_dummies.drop('Survived',axis=1)\n",
    "target = data_with_dummies['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X, test_X, train_y, test_y = train_test_split(inputs,target, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x12f563f40>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Random Forest\n",
    "rfc=RandomForestClassifier()\n",
    "rfc=rfc.fit(train_X,train_y)\n",
    "\n",
    "#LightGBM\n",
    "#boosting_type='gbdt',objective='binary',random_state=42\n",
    "lgbm = LGBMClassifier()\n",
    "lgbm.fit(train_X, train_y)\n",
    "\n",
    "#XGBoost\n",
    "xgbr = xgb.XGBClassifier()\n",
    "xgbr.fit(train_X, train_y)\n",
    "\n",
    "#SVC\n",
    "#probability=True,random_state=42\n",
    "svc = SVC(probability=True)\n",
    "svc.fit(train_X, train_y)\n",
    "\n",
    "#CatBoost        \n",
    "cboost = CatBoostClassifier()\n",
    "cboost.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.29269012, 0.35519625, 0.28773968, 0.31996898, 0.29136482,\n",
       "       0.30146499, 0.29532695, 0.70675168, 0.29334658, 0.31485962,\n",
       "       0.29436044, 0.29334658, 0.29250574, 0.28996963, 0.29240462,\n",
       "       0.30398463, 0.29268747, 0.29835048, 0.37236876, 0.29498019,\n",
       "       0.30052899, 0.3103789 , 0.31875582, 0.2994341 , 0.30138465,\n",
       "       0.2889948 , 0.31464515, 0.29334658, 0.31671862, 0.29843872,\n",
       "       0.3391576 , 0.31161006, 0.29244401, 0.29078017, 0.31486452,\n",
       "       0.69040461, 0.29044554, 0.30407959, 0.75527692, 0.28804985,\n",
       "       0.29239712, 0.64425316, 0.43555953, 0.33260479, 0.30998276,\n",
       "       0.29753368, 0.29673277, 0.29557249, 0.29846074, 0.44577769,\n",
       "       0.30146499, 0.4500689 , 0.28870846, 0.31168906, 0.29967016,\n",
       "       0.29072779, 0.29334658, 0.32935296, 0.29478734, 0.32233337,\n",
       "       0.30257429, 0.42764839, 0.28616875, 0.308246  , 0.29025718,\n",
       "       0.29321421, 0.32139461, 0.32041618, 0.34138312, 0.29614384,\n",
       "       0.31675266, 0.34291336, 0.29135713, 0.30349686, 0.29846074,\n",
       "       0.32024777, 0.31442401, 0.29846074, 0.33109018, 0.33080377,\n",
       "       0.41710857, 0.30334909, 0.31171961, 0.29844242, 0.34794431,\n",
       "       0.36652973, 0.29244401, 0.30026535, 0.2910011 , 0.30186355,\n",
       "       0.31950376, 0.34156417, 0.29558428, 0.31838373, 0.82704642,\n",
       "       0.72879476, 0.29334658, 0.29622841, 0.3159259 , 0.30138465,\n",
       "       0.30589129, 0.29566104, 0.30237529, 0.29839418, 0.29898096,\n",
       "       0.29353211, 0.32144184, 0.35995899, 0.39383837, 0.29048536,\n",
       "       0.38371126, 0.6899912 , 0.30219777, 0.29846074, 0.32022599,\n",
       "       0.77368116, 0.29459812, 0.34604054, 0.33920264, 0.29674446,\n",
       "       0.2897838 , 0.38067779, 0.32238013, 0.30468257, 0.29259446,\n",
       "       0.33469057, 0.77368116, 0.29047117, 0.29266602, 0.32934054,\n",
       "       0.32024777, 0.36549443, 0.29564893, 0.66382449, 0.31939545,\n",
       "       0.2927781 , 0.34621149, 0.28862777, 0.43365132, 0.32060906,\n",
       "       0.29385075, 0.37536749, 0.33277567, 0.31850302, 0.28122466,\n",
       "       0.70317774, 0.29129565, 0.30547405, 0.32773651, 0.32760214,\n",
       "       0.29364057, 0.29505542, 0.33470715, 0.29902848, 0.30893015,\n",
       "       0.29321421, 0.33562114, 0.29632249, 0.34872966, 0.29404848,\n",
       "       0.29231803, 0.33020082, 0.29598443, 0.29364057, 0.38067779,\n",
       "       0.30147963, 0.29334658, 0.29968957, 0.29752006, 0.2922309 ,\n",
       "       0.29852417, 0.308246  , 0.31312246, 0.29938364, 0.29047117,\n",
       "       0.32797076, 0.7499358 , 0.29401539, 0.38510942, 0.30126115])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.predict_proba(test_X)[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here it is the reason of why I'am using predict_proba.\n",
    "\n",
    "https://discuss.analyticsvidhya.com/t/what-is-the-difference-between-predict-and-predict-proba/67376/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.78, 0.83, 0.8, 0.83, 0.83]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifiers = []\n",
    "classifiers.append(svc)\n",
    "classifiers.append(rfc)\n",
    "classifiers.append(xgbr)\n",
    "classifiers.append(lgbm)\n",
    "classifiers.append(cboost)\n",
    "\n",
    "classifier_names = ['svc','rfc','xgbr','lgbm','cboost']\n",
    "\n",
    "accuracy_list = []\n",
    "auc_list=[]\n",
    "\n",
    "for classifier in classifiers :\n",
    "    y_pred=classifier.predict(test_X)\n",
    "    y_pred_proba=classifier.predict_proba(test_X)[:,1]\n",
    "    accuracy_list.append(round(accuracy_score(test_y, y_pred),2))\n",
    "    auc_list.append(round(roc_auc_score(test_y, y_pred_proba),2))\n",
    "auc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '0.78'),\n",
       " Text(0, 0, '0.83'),\n",
       " Text(0, 0, '0.8'),\n",
       " Text(0, 0, '0.83'),\n",
       " Text(0, 0, '0.83')]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1,2,figsize=(15,5))\n",
    "sns.barplot(x=classifier_names,y=accuracy_list,ax=ax1).set_title('Accuracy')\n",
    "ax1.bar_label(ax1.containers[0])\n",
    "sns.barplot(x=classifier_names,y=auc_list,ax=ax2).set_title('ROC AUC')\n",
    "ax2.bar_label(ax2.containers[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this problem, the best model is cboost."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definition of ROC-AUC\n",
    "\n",
    "https://www.analyticsvidhya.com/blog/2020/06/auc-roc-curve-machine-learning/"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.5 64-bit",
   "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": "3.10.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}