main_project_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1fb1f837",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "#data analysis\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "\n",
    "#visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#machine learning\n",
    "from sklearn.linear_model import LogisticRegression, RidgeClassifierCV\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import (RandomForestClassifier, GradientBoostingClassifier)\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2a97bfb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import data\n",
    "train_df = pd.read_csv('train.csv')\n",
    "test_df = pd.read_csv('test.csv')\n",
    "combine = [train_df, test_df]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "36320582",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n",
      " 'Ticket' 'Fare' 'Cabin' 'Embarked']\n"
     ]
    }
   ],
   "source": [
    "#show columns in dataset\n",
    "print(train_df.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9f982f88",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Categorical: Survived, Sex, and Embarked. Ordinal: Pclass.\n",
    "#Continous: Age, Fare. Discrete: SibSp, Parch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e5f01ba4",
   "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",
       "    </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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview the data\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d42acc17",
   "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",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
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       "      <th>887</th>\n",
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       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bddc49d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n",
      "________________________________________\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()\n",
    "print('_'*40)\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cd3c7b9c",
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d9fad8ed",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>681</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>347082</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                           Name   Sex  Ticket    Cabin Embarked\n",
       "count                       891   891     891      204      889\n",
       "unique                      891     2     681      147        3\n",
       "top     Braund, Mr. Owen Harris  male  347082  B96 B98        S\n",
       "freq                          1   577       7        4      644"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "19b0e811",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
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     "execution_count": 10,
     "metadata": {},
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   "cell_type": "code",
   "execution_count": 11,
   "id": "ffa0ba09",
   "metadata": {},
   "outputs": [
    {
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       "      Sex  Survived\n",
       "0  female  0.742038\n",
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     "metadata": {},
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   "execution_count": 12,
   "id": "61deb8af",
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   "outputs": [
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       "   SibSp  Survived\n",
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       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
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     "execution_count": 12,
     "metadata": {},
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    "train_df[[\"SibSp\", \"Survived\"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ff8d59d2",
   "metadata": {},
   "outputs": [
    {
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       "   Parch  Survived\n",
       "3      3  0.600000\n",
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    "train_df[[\"Parch\", \"Survived\"]].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "15278de6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d8b3f719",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2397eebc820>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "g = sns.FacetGrid(train_df, col='Survived')\n",
    "g.map(plt.hist, 'Age', bins=20)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e2d54a97",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\my_env\\lib\\site-packages\\seaborn\\axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
    "grid.add_legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "305abc6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\my_env\\lib\\site-packages\\seaborn\\axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "E:\\my_env\\lib\\site-packages\\seaborn\\axisgrid.py:670: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "E:\\my_env\\lib\\site-packages\\seaborn\\axisgrid.py:675: UserWarning: Using the pointplot function without specifying `hue_order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2397f3de730>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 323.94x475.2 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, row='Embarked', size=2.2, aspect=1.6)\n",
    "grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2e1017fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\my_env\\lib\\site-packages\\seaborn\\axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "E:\\my_env\\lib\\site-packages\\seaborn\\axisgrid.py:670: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2397f298a00>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oATgQuKGqbqyqnwJnAEvHUIckSb01jgCwB/CtSeO3tdMkSdKILBzDNjNgWj1kpmQ5sLwd/VGS6zqtanweAdw57iKG9YpxF7B52ZKfu1VVdcQwM/aorcKW/Zxvybbk523otjpVqh7y2tupJAcBb66qZ7XjbwSoqrePtJDNRJI1VbVk3HVo5nzu+sfnfH7yeRtsHKcAvgLsnWSvJNsALwJWjqEOSZJ6a+SnAKrq/iSvAs4FFgCnVNU1o65DkqQ+G8dnAKiqTwOfHse2N0Mnj7sAbTKfu/7xOZ+ffN4GGPlnACRJ0vj5U8CSJPWQAUCSpB4yAEiS1EMGAEmSesgAIElSDxkAJEnqIQOAJEk9ZACQJKmHDACSJPWQAUCSpB4yAEiS1EMGgFlI8vMkV0z6O34Gyx6S5JOz3P6FSTbpGtdJTk3y/Nlsv13PY5J8OskNSdYmOSvJ7gPmu3kDyz8lyaXt/lub5M2zrWnSur8wR+uZq321LMn17d+yuahNw7O92l5nuJ5VSe6a7fO+ORvL1QC3IPdW1f7j2HCSBePY7pQatgU+BfxJVf17O+1QYBFw+5CrWQG8oKq+1j6mfWaw/dBc0OqBQfdX1W8Ou66uJdkVOAFYAhTw1SQrq+r7462sV2yvtteZOBHYHjhu3IV0xR6ADiS5OcnfJvlikjVJDkhybpJvJnnFpFl3SvLxJNcmeX+Srdrl39cud02Sv56y3jcluRg4etL0rZKsSPLWJAuSnJjkK0muTHJcO0+SvLfd1qeA3ebgof4+8MX1BxOAqrqgqq6ewTp2A9a1y/68qq5t631zktdPeoxXJ9mz/Vub5F+Ay4C/SvJ3k+Y7Jsk/tcM/am/PTPLbk+Y5NcnzRryvngWcX1Xfa1/0zweOmIP1apZsr7bXQapqNXD3XKxrc2UPwOxsl+SKSeNvr6oz2+FvVdVBSd4FnAocDGwLXAO8v53nQGBf4BZgFfB7wNnAX1bV99Ik7NVJ9quqK9tlflJVTwVoD04LgdOAq6vqbUmWAz+oqicn+SXgkiTnAU+kSeuPB3YHrgVOmfqAkvwZ8JIBj/WiqnrNlGmPA7467V7auHcB1yW5kGYfrKiqn0yzzD7Ay6rqj5IsAr4IvKG974XA26bMf0Y7/dNJtgEOA/4QOJbR7as9gG9NGr+tnabRsb3aXofdV71gAJidjXUprmxvrwJ2rKq7gbuT/CTJzu19X66qGwGSnA48leaA8oL2wLAQWExz0Fl/QFl/wFrvJOCsqlrfiA4H9suD58AeDuwNPA04vap+DnwnyecGFV1VJ9J0fY1EVf1NktNo6v594MXAIdMsdktVfaldfiLJjUmeAlxPcyC4ZMr8nwH+sT1oHEHT4O9NMsp9lUGrGHJZzQ3b6yz1qL32ggGgO/e1tw9MGl4/vn6/T30BqCR7Aa8HnlxV309yKs07kfXumbLMF4BDk/xDm8QDvLqqzp08U9ulNu0LzgxT8jXAb023zulU1TeB9yX5ADCR5JeB+/nFU1Qb2wdnAi8Avg58vKp+4XFW1U/adyzPonlncXp71yj31W384oHyUcCF021DI2N7HVJP2ms/VJV/m/gH/GgD028GHtEOHwO8d+p9NC8G9wJ70TScc4HnAU8AvtZO253mwznHTF1vO34hzYfK/gT4d5oD1XLgE8DW7TyPAXag6a48F1hA8y7l+8DzZ/n4twNuAJ4zadoRwOMH7ZMNrOM5NB8MAvh14M62xpcCZ7TTDwB+DuzZ/l09ZR27ADcCFwAHDnp+2u18nKYbfpt22ij31a7ATW2tu7TDu477f7hPf7ZX2+sm7LNDgE+O+3+3qz97AGZn6jnFVVU19FeLaM6FvYPm3NVFNGn4gSSX06T1G3lo99hDVNU7kzwc+DeahLsncFmSABPAUTSN6ek0XZzfAD4/gzo3tN17kzwXeHeSdwM/o+n6fO0MVvMHwLuS/JjmXcRLqurnST4K/Pd2/36lrXlDdXw/ybXAvlX15Q3Mdh7w/wMrq+qn7bQPMrp99b0kb2kfC8DfVNX3ZrtezYjt1fY6tCT/F3gssGOS24Bja0rvw3y3PslJnUpyc1XtOe46JE3P9toPfg1QkqQeMgBoVN497gIkDe3d4y5A3fMUgCRJPWQPgCRJPWQAkCSph+bF1wCPOOKIWrVq1bjLkPpk0C8XTsu2Ko3cJrVVmCc9AHfeeee4S5A0BNuqNH/MiwAgSZLmlgFAkqQeMgBIktRD8+JDgJK2PCedd8W4S9jiHXf4/uMuQZuxTnsAkrwuyTVJrk5yepJtk+ya5Pwk17e3u3RZgyRJeqjOAkCSPYDXAEuq6nE0l2p8EXA8sLqq9gZWt+OSJGmEuv4MwEKaS3AuBLYHvgMsBVa096+guZyjJEkaoc4CQFV9G/h74FZgHfCDqjoP2L2q1rXzrAN266oGSZI0WJenAHahebe/F/BIYIckL53B8suTrEmyZmJioqsyJc2SbVWan7o8BfAM4KaqmqiqnwEfA34TuD3JYoD29o5BC1fVyVW1pKqWLFq0qMMyJc2GbVWan7oMALcCT0myfZIAhwFrgZXAsnaeZcA5HdYgSZIG6Ox3AKrq0iRnA5cB9wOXAycDOwJnJTmWJiQc3VUNkiRpsE5/CKiqTgBOmDL5PpreAEmSNCb+FLAkST1kAJAkqYcMAJIk9ZABQJKkHjIASJLUQwYASZJ6qNOvAUqStjwnnXfFuEvYoh13+P4j2Y49AJIk9ZABQJKkHjIASJLUQwYASZJ6yAAgSVIPGQAkSeohA4AkST1kAJAkqYcMAJIk9ZABQJKkHuo0ACTZOcnZSb6eZG2Sg5LsmuT8JNe3t7t0WYMkSXqornsA3gOsqqrHAk8A1gLHA6uram9gdTsuSZJGqLMAkGQn4GnAhwCq6qdVdRewFFjRzrYCOKqrGiRJ0mBd9gA8GpgA/jXJ5Uk+mGQHYPeqWgfQ3u7WYQ2SJGmALgPAQuAA4H1V9UTgHmbQ3Z9keZI1SdZMTEx0VaOkWbKtSvNTlwHgNuC2qrq0HT+bJhDcnmQxQHt7x6CFq+rkqlpSVUsWLVrUYZmSZsO2Ks1PnQWAqvoP4FtJ9mknHQZcC6wElrXTlgHndFWDJEkabGHH6381cFqSbYAbgZfRhI6zkhwL3Aoc3XENkiRpik4DQFVdASwZcNdhXW5XkiRtnL8EKElSDxkAJEnqIQOAJEk9ZACQJKmHDACSJPWQAUCSpB4yAEiS1EMGAEmSemjoAJDkqUle1g4vSrJXd2VJkqQuDRUAkpwA/DnwxnbS1sCHuypKkiR1a9gegN8FjqS5pC9V9R3gYV0VJUmSujVsAPhpVRVQAEl26K4kSZLUtWEDwFlJTgJ2TvJy4LPAB7orS5IkdWnaqwEmCXAm8Fjgh8A+wJuq6vyOa5MkSR2ZNgBUVSX5RFU9CfBFX5KkLcCwpwC+lOTJnVYiSZJGZtoegNahwHFJbqH5JkBoOgf266wySZLUmWEDwLM7rUKSJI3UUKcAquqWqroFuJfmq4D/+ZXA6SRZkOTyJJ9sx3dNcn6S69vbXTa1eEmStGmG/SXAI5NcD9wEfB64GfjMkNt4LbB20vjxwOqq2htY3Y5LkqQRGvZDgG8BngJ8o6r2Ag4DLpluoSSPAp4DfHDS5KXAinZ4BXDUsMVKkqS5MWwA+FlVfRfYKslWVXUBsP8Qy70beAPwwKRpu1fVOoD2drfhy5UkSXNh2ABwV5IdgYuA05K8B7h/YwskeS5wR1V9dVMKS7I8yZokayYmJjZlFZJGwLYqzU8bDQBJfrUdXAr8GHgdsAr4JvA706z7YODIJDcDZwBPT/Jh4PYki9v1LwbuGLRwVZ1cVUuqasmiRYuGfDiSRs22Ks1P030N8BPAAVV1T5KPVtXzePD8/UZV1RtpLx+c5BDg9VX10iQnAsuAd7S352xa6YOddN4Vc7k6TXHc4fuPuwRJ0hyY7hRAJg0/eo62+Q7gme23Cp7ZjkuSpBGargegNjA8I1V1IXBhO/xdmm8RSJKkMZkuADwhyQ9pegK2a4fhwZ8C3qnT6iRJUic2GgCqasGoCpEkSaMz7NcAJUnSFsQAIElSDxkAJEnqIQOAJEk9ZACQJKmHDACSJPWQAUCSpB4yAEiS1EMGAEmSesgAIElSDxkAJEnqIQOAJEk9ZACQJKmHDACSJPWQAUCSpB7qLAAk+ZUkFyRZm+SaJK9tp++a5Pwk17e3u3RVgyRJGqzLHoD7gT+tql8HngK8Msm+wPHA6qraG1jdjkuSpBHqLABU1bqquqwdvhtYC+wBLAVWtLOtAI7qqgZJkjTYSD4DkGRP4InApcDuVbUOmpAA7DaKGiRJ0oM6DwBJdgQ+CvxxVf1wBsstT7ImyZqJiYnuCpQ0K7ZVaX7qNAAk2Zrmxf+0qvpYO/n2JIvb+xcDdwxatqpOrqolVbVk0aJFXZYpaRZsq9L81OW3AAJ8CFhbVe+cdNdKYFk7vAw4p6saJEnSYAs7XPfBwB8AVyW5op32F8A7gLOSHAvcChzdYQ2SJGmAzgJAVV0MZAN3H9bVdiVJ0vS67AGQZuSk864YdwlbtOMO33/cJUjajPhTwJIk9ZABQJKkHjIASJLUQwYASZJ6yAAgSVIPGQAkSeohA4AkST1kAJAkqYcMAJIk9ZABQJKkHjIASJLUQwYASZJ6yAAgSVIPGQAkSeohA4AkST1kAJAkqYfGEgCSHJHkuiQ3JDl+HDVIktRnIw8ASRYA/ww8G9gXeHGSfUddhyRJfTaOHoADgRuq6saq+ilwBrB0DHVIktRb4wgAewDfmjR+WztNkiSNyMIxbDMDptVDZkqWA8vb0R8lua7TqsbnEcCd4y5iWK8YdwGbly35uVtVVUcMM2OP2ips2c/5lmxLft6GbqtTpeohr72dSnIQ8OaqelY7/kaAqnr7SAvZTCRZU1VLxl2HZs7nrn98zucnn7fBxnEK4CvA3kn2SrIN8CJg5RjqkCSpt0Z+CqCq7k/yKuBcYAFwSlVdM+o6JEnqs3F8BoCq+jTw6XFsezN08rgL0Cbzuesfn/P5yedtgJF/BkCSJI2fPwUsSVIPGQAkSeohA4AkST1kAJAkqYcMAJIk9ZABQJKkHjIASJLUQwYASZJ6yAAgSVIPGQAkSeohA4AkST1kAJAkqYcMALOQ5OdJrpj0d/wMlj0kySdnuf0LkyzZxGVPTfL82Wy/Xc9vJPlckm8k+WaSv04y8P8qyc0bmP7cJJcn+VqSa5McN9u62vU+MsnZc7SuTd7XU9bzxiQ3JLkuybPmojZNz7ZqW53hOn45yQVJfpTkvXNR1+ZoLJcD3oLcW1X7j2PDSRaMY7tTatgOWAn8YVWdl2R74KPAa4F3DbmOrWku1XlgVd2W5JeAPWdQw4Kq+vmg+6rqO8CsD5xzJcm+wIuA3wAeCXw2yWM2VL/mlG3VtjoTPwH+Cnhc+7dFsgegA0luTvK3Sb6YZE2SA5Kc26buV0yadackH2+T9PvXp/Ek72uXuybJX09Z75uSXAwcPWn6VklWJHlrkgVJTkzylSRXrk/oaby33dangN3m4KH+PnBJVZ0HUFU/Bl4F/NkM1vEwmiD63XYd91XVdW3Nv/DOJ8mP2ttD2nT+f4CrkvzvJH80ab43J/nTJHsmubqddmmS35g0z4VJnpRkhySntPvr8iRL2/u3S3JGuw/PBLbblB00xVLgjPYx3gTcABw4B+vVJrKt2lYHqap7qupimiCwxbIHYHa2S3LFpPG3V9WZ7fC3quqgJO8CTgUOBrYFrgHe385zILAvcAuwCvg94GzgL6vqe+07h9VJ9quqK9tlflJVTwVoD1ALgdOAq6vqbUmWAz+oqie3Cf2SJOcBTwT2AR4P7A5cC5wy9QEl+TPgJQMe60VV9Zop034D+OrkCVX1zbZB7lxVdw3ebb8w//eSrARuSbIa+CRwelU9MM2iBwKPq6qbkjwReDfwL+19LwCO4BcD7hnt9BOSLAYeWVVfTfK3wOeq6n8k2Rn4cpLPAscBP66q/ZLsB1w2qIj2+T10wF1nVNU7pkzbA/jSpPHb2mnqnm3VtjqTttoLBoDZ2Vi34sr29ipgx6q6G7g7yU/af16AL1fVjQBJTgeeSnNQeUF7cFgILKY58Kw/qKw/aK13EnBWVb2tHT8c2G9SGn84sDfwNJrG+nPgO0k+N6joqjoROHHaR94IUBuYPrSq+p9JHg88A3g98EzgmGkW+3L7LpqqujzJbkkeCSwCvl9VtybZc9L8ZwHnAyfQHFw+0k4/HDgyyevb8W2BX6XZX//Yrv/KJFcyQFW9bgYPddB+GbT/NPdsq7bVmbTVXjAAdOe+9vaBScPrx9fv96kNspLsRdOwnlxV309yKs0/+nr3TFnmC8ChSf6hqn5C06BfXVXnTp4pyW8P2N5DzPBdxTU0jW/y8o8G7hzmHcVkVXUVTRfhvwE30RxU7qd9Z5AkwDaTFpm6H86mOYf4X2jeQUxd/7eTfLd9h/BCmncN0Oyv563vypz0OGC4/TWTdxW3Ab8yafxRwHem24Y6Z1udgZ601V7wMwDjdWCSvdKcT3whcDGwE02D+UGS3YFnT7OODwGfBj6SZCFwLvCHaT6wQ5LHJNkBuAh4UZrzjosZ3BCoqhOrav8Bf1MPKNB0Zz41yTPabW1Hk8RPGHYHJNkxySGTJu1P080KcDPwpHZ4KbD1RlZ1Bs0H7J5Pc4DZ0DxvAB7eHsSg2V+vbg9atF2U0Oyvl7TTHgfsN2iFVfW6DeyvQQeUlTTPwS+1Lx57A1/eyGPS5sO22q+22gv2AMzO1POKq6pq6K8XAV8E3kFzru8i4ONV9UCSy2kS+43AJdOtpKremeThwL/RNIQ9gcvahjIBHAV8HHg6TTfnN4DPz6DODW333iRHAv+U5F9ozme/tapOm8FqArwhyUnAvTQH1GPa+z4AnJPky8BqHvpOYnIt1yR5GPDtqlq3gdnOBt4DvGXStLfQnJO8st1fNwPPBd4H/GvbnXgFc/BC3dZ4Fs053fuBV5bfABgV26ptdUbSfBVyJ2CbJEcBh1fVtXOx7s1FqjwFqbnRNpJ3AodW1S0D7r+5qvYcdV2SfpFtVWAA0Ah5UJHmB9tqP/gZAI3Su8ddgKShvHvcBah79gBIktRD9gBIktRD8+JbAEcccUStWrVq3GVIfTKjH4hZz7YqjdwmtVWYJz0Ad95557hLkDQE26o0f8yLACBJkuaWAUCSpB4yAEiS1EMGAEmSeqjTAJDkdUmuSXJ1ktOTbJtk1yTnJ7m+vd2lyxokSdJDdRYAkuwBvAZYUlWPAxbQXAHqeGB1Ve1Nc9GImVyQQ5IkzYGuTwEspLkK10Jge5prny8FVrT3r6C5+pUkSRqhzgJAVX0b+HvgVmAd8IOqOg/Yff0lINvb3bqqQZIkDdblKYBdaN7t7wU8EtghyUtnsPzyJGuSrJmYmOiqTEmzZFuV5qcuTwE8A7ipqiaq6mfAx4DfBG5Pshigvb1j0MJVdXJVLamqJYsWLeqwTEmzYVuV5qcuA8CtwFOSbJ8kwGHAWmAlsKydZxlwToc1SJKkATq7GFBVXZrkbOAy4H7gcuBkYEfgrCTH0oSEo7uqQZIkDdbp1QCr6gTghCmT76PpDZAkSWPiLwFKktRDBgBJknrIACBJUg8ZACRJ6iEDgCRJPWQAkCSphwwAkiT1kAFAkqQeMgBIktRDBgBJknrIACBJUg8ZACRJ6iEDgCRJPWQAkCSphwwAkiT1kAFAkqQeMgBIktRDBgBJknqo0wCQZOckZyf5epK1SQ5KsmuS85Nc397u0mUNkiTpobruAXgPsKqqHgs8AVgLHA+srqq9gdXtuCRJGqHOAkCSnYCnAR8CqKqfVtVdwFJgRTvbCuCormqQJEmDddkD8GhgAvjXJJcn+WCSHYDdq2odQHu7W4c1SJKkAboMAAuBA4D3VdUTgXuYQXd/kuVJ1iRZMzEx0VWNkmbJtirNT10GgNuA26rq0nb8bJpAcHuSxQDt7R2DFq6qk6tqSVUtWbRoUYdlSpoN26o0P3UWAKrqP4BvJdmnnXQYcC2wEljWTlsGnNNVDZIkabCFHa//1cBpSbYBbgReRhM6zkpyLHArcHTHNUiSpCk6DQBVdQWwZMBdh3W5XUmStHH+EqAkST1kAJAkqYcMAJIk9ZABQJKkHjIASJLUQwYASZJ6yAAgSVIPDR0Akjw1ycva4UVJ9uquLEmS1KWhAkCSE4A/B97YTtoa+HBXRUmSpG4N2wPwu8CRNFf0o6q+Azysq6IkSVK3hg0AP62qAgogyQ7dlSRJkro2bAA4K8lJwM5JXg58FvhAd2VJkqQuTXsxoCQBzgQeC/wQ2Ad4U1Wd33FtkiSpI9MGgKqqJJ+oqicBvuhLkrQFGPYUwJeSPLnTSiRJ0shM2wPQOhQ4LsktNN8ECE3nwH6dVSZJkjozbAB4dqdVSJKkkRrqFEBV3VJVtwD30nwV8D+/EjidJAuSXJ7kk+34rknOT3J9e7vLphYvSZI2zbC/BHhkkuuBm4DPAzcDnxlyG68F1k4aPx5YXVV7A6vbcUmSNELDfgjwLcBTgG9U1V7AYcAl0y2U5FHAc4APTpq8FFjRDq8Ajhq2WEmSNDeGDQA/q6rvAlsl2aqqLgD2H2K5dwNvAB6YNG33qloH0N7uNny5kiRpLgwbAO5KsiNwEXBakvcA929sgSTPBe6oqq9uSmFJlidZk2TNxMTEpqxC0gjYVqX5aaMBIMmvtoNLgR8DrwNWAd8EfmeadR8MHJnkZuAM4OlJPgzcnmRxu/7FwB2DFq6qk6tqSVUtWbRo0ZAPR9Ko2Val+Wm6HoBPAFTVPcBHqur+qlpRVf/YnhLYoKp6Y1U9qqr2BF4EfK6qXgqsBJa1sy0DzpnNA5AkSTM3XQDIpOFHz9E23wE8s/1WwTPbcUmSNELT/RBQbWB4RqrqQuDCdvi7NN8ikCRJYzJdAHhCkh/S9ARs1w7Dgz8FvFOn1UmSpE5sNABU1YJRFSJJkkZn2K8BSpKkLYgBQJKkHjIASJLUQwYASZJ6yAAgSVIPGQAkSeohA4AkST1kAJAkqYcMAJIk9ZABQJKkHjIASJLUQwYASZJ6yAAgSVIPGQAkSeohA4AkST3UWQBI8itJLkiyNsk1SV7bTt81yflJrm9vd+mqBkmSNFiXPQD3A39aVb8OPAV4ZZJ9geOB1VW1N7C6HZckSSPUWQCoqnVVdVk7fDewFtgDWAqsaGdbARzVVQ2SJGmwkXwGIMmewBOBS4Hdq2odNCEB2G0UNUiSpAd1HgCS7Ah8FPjjqvrhDJZbnmRNkjUTExPdFShpVmyr0vzUaQBIsjXNi/9pVfWxdvLtSRa39y8G7hi0bFWdXFVLqmrJokWLuixT0izYVqX5qctvAQT4ELC2qt456a6VwLJ2eBlwTlc1SJKkwRZ2uO6DgT8ArkpyRTvtL4B3AGclORa4FTi6wxokSdIAnQWAqroYyAbuPqyr7UqSpOn5S4CSJPWQAUCSpB4yAEiS1ENdfghwLE4674pxl7BFO+7w/cddgiRpDmxxAUDS/GBY756BXRtjAJAkzYjhrVujCm5+BkCSpB6yB0CbDd9VdMvuYEmT2QMgSVIPGQAkSeohA4AkST1kAJAkqYcMAJIk9ZABQJKkHjIASJLUQwYASZJ6yAAgSVIPGQAkSeqhsQSAJEckuS7JDUmOH0cNkiT12cgDQJIFwD8Dzwb2BV6cZN9R1yFJUp+NowfgQOCGqrqxqn4KnAEsHUMdkiT11jgCwB7AtyaN39ZOkyRJIzKOywFnwLR6yEzJcmB5O/qjJNd1WtX4PAK4c9xFDOsV4y5g87IlP3erquqIYWbsUVuFLfs535Jtyc/b0G11qlQ95LW3U0kOAt5cVc9qx98IUFVvH2khm4kka6pqybjr0Mz53PWPz/n85PM22DhOAXwF2DvJXkm2AV4ErBxDHZIk9dbITwFU1f1JXgWcCywATqmqa0ZdhyRJfTaOzwBQVZ8GPj2ObW+GTh53AdpkPnf943M+P/m8DTDyzwBIkqTx86eAJUnqIQPALCV5TZK1SU7raP1vTvL6LtatuZPkkCSfHHcd2jjbq8D2ut5YPgOwhfkj4NlVddO4C5E0Ldur1LIHYBaSvB94NLAyyV8mOSXJV5JcnmRpO88xST6R5N+T3JTkVUn+pJ3nS0l2bed7ebvs15J8NMn2A7b3a0lWJflqkv+b5LGjfcRbtiR7Jvl6kg8muTrJaUmekeSSJNcnObD9+0L7/H0hyT4D1rPDoP8FjZftdctie50DVeXfLP6Am2l+ZepvgZe203YGvgHsABwD3AA8DFgE/AB4RTvfu4A/bod/edI63wq8uh1+M/D6dng1sHc7/F+Bz4378W9Jf8CewP3A42nC8VeBU2h+vXIp8AlgJ2BhO/8zgI+2w4cAn2yHB/4vjPvx+Wd73ZL+bK+z//MUwNw5HDhy0vm/bYFfbYcvqKq7gbuT/AD493b6VcB+7fDjkryV5h9wR5rfSfhPSXYEfhP4SPKfv6b8Sx08jr67qaquAkhyDbC6qirJVTQHnIcDK5LsTfMT1lsPWMeG/hfWdl28hmZ73TLYXmfBADB3Ajyvqn7hd9CT/FfgvkmTHpg0/gAPPgenAkdV1deSHEOTUCfbCrirqvaf06o11XTP1VtoXiB+N8mewIUD1jHwf0GbFdvrlsH2Ogt+BmDunAu8Om3cT/LEGS7/MGBdkq2Bl0y9s6p+CNyU5Oh2/UnyhFnWrJl7OPDtdviYDcwz2/8Fdc/22g+2140wAMydt9B0L12Z5Op2fCb+CrgUOB/4+gbmeQlwbJKvAdfQnOfSaP0d8PYkl9D8lPUgs/1fUPdsr/1ge90IfwlQkqQesgdAkqQeMgBIktRDBgBJknrIACBJUg8ZACRJ6iEDgGal/U31a5JcmeSK9odUJG1mbKuayl8C1CZLchDwXOCAqrovySOAbcZclqQpbKsaxB4AzcZi4M6qug+gqu6squ8keVKSz7dXQTs3yeIkD09y3fqrcSU5PcnLx1q91B+2VT2EPwSkTdZe8ORiYHvgs8CZwBeAzwNLq2oiyQuBZ1XV/0jyTOBvgPcAx1TVEWMqXeoV26oG8RSANllV/SjJk4D/BhxKc1B5K/A44Pz2p7UXAOva+c9vfxv9nwF/F10aEduqBrEHQHMmyfOBVwLbVtVBA+7fiuYdx17Ab1fVlSMuURK2VTX8DIA2WZJ92utsr7c/zTW0F7UfOiLJ1kl+o73/de39LwZOaa+kJqljtlUNYg+ANlnbpfhPwM7A/cANwHLgUcA/0lyKcyHwbpp3E+cAB1bV3UneCdxdVSeMvnKpX2yrGsQAIElSD3kKQJKkHjIASJLUQwYASZJ6yAAgSVIPGQAkSeohA4AkST1kAJAkqYcMAJIk9dD/A1+T8Tar3XBsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, row='Embarked', col='Survived', size=2.2, aspect=1.6)\n",
    "grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3853e59c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before (891, 12) (418, 11) (891, 12) (418, 11)\n"
     ]
    }
   ],
   "source": [
    "print(\"Before\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9a5749ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "test_df = test_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "combine = [train_df, test_df]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7554c1fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After (891, 10) (418, 9) (891, 10) (418, 9)\n"
     ]
    }
   ],
   "source": [
    "print(\"After\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7f2d41dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4fff9393",
   "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>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     2\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dr             1     6\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    40\n",
       "Miss         182     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   517\n",
       "Mrs          125     0\n",
       "Ms             1     0\n",
       "Rev            0     6\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(train_df['Title'], train_df['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "94418ee1",
   "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>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Col</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dr</td>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lady</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rev</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sir</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "0     Col  0.500000\n",
       "1      Dr  0.428571\n",
       "2    Lady  0.666667\n",
       "3  Master  0.575000\n",
       "4    Miss  0.702703\n",
       "5      Mr  0.156673\n",
       "6     Mrs  0.793651\n",
       "7     Rev  0.000000\n",
       "8     Sir  0.400000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Jonkheer', 'Dona'], 'Lady')\n",
    "    dataset['Title'] = dataset['Title'].replace(['Capt', 'Don', 'Major', 'Sir'], 'Sir')\n",
    "    \n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "    \n",
    "train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9d4e48b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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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",
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       "      <th>Title</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <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>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>6</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>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>7</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>7.9250</td>\n",
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       "    <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>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>7</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>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>6</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     Fare Embarked  Title  \n",
       "0      0   7.2500        S      6  \n",
       "1      0  71.2833        C      7  \n",
       "2      0   7.9250        S      5  \n",
       "3      0  53.1000        S      7  \n",
       "4      0   8.0500        S      6  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_mapping = {\"Col\": 1, \"Dr\": 2, \"Lady\": 3, \"Master\": 4, \"Miss\": 5, \"Mr\": 6, \"Mrs\": 7, \"Rev\": 8, \"Sir\": 9}\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6ef958c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 9), (418, 9))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Name', 'PassengerId'], axis=1)\n",
    "test_df = test_df.drop(['Name'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "17b5b913",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
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       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
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       "      <th>888</th>\n",
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       "      <th>889</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
       "0           0       3    male  22.0      1      0   7.2500        S      6\n",
       "1           1       1  female  38.0      1      0  71.2833        C      7\n",
       "2           1       3  female  26.0      0      0   7.9250        S      5\n",
       "3           1       1  female  35.0      1      0  53.1000        S      7\n",
       "4           0       3    male  35.0      0      0   8.0500        S      6\n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...    ...\n",
       "886         0       2    male  27.0      0      0  13.0000        S      8\n",
       "887         1       1  female  19.0      0      0  30.0000        S      5\n",
       "888         0       3  female   NaN      1      2  23.4500        S      5\n",
       "889         1       1    male  26.0      0      0  30.0000        C      6\n",
       "890         0       3    male  32.0      0      0   7.7500        Q      6\n",
       "\n",
       "[891 rows x 9 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "748b635a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass     Sex   Age  SibSp  Parch      Fare Embarked  Title\n",
       "0            892       3    male  34.5      0      0    7.8292        Q      6\n",
       "1            893       3  female  47.0      1      0    7.0000        S      7\n",
       "2            894       2    male  62.0      0      0    9.6875        Q      6\n",
       "3            895       3    male  27.0      0      0    8.6625        S      6\n",
       "4            896       3  female  22.0      1      1   12.2875        S      7\n",
       "..           ...     ...     ...   ...    ...    ...       ...      ...    ...\n",
       "413         1305       3    male   NaN      0      0    8.0500        S      6\n",
       "414         1306       1  female  39.0      0      0  108.9000        C      3\n",
       "415         1307       3    male  38.5      0      0    7.2500        S      6\n",
       "416         1308       3    male   NaN      0      0    8.0500        S      6\n",
       "417         1309       3    male   NaN      1      1   22.3583        C      4\n",
       "\n",
       "[418 rows x 9 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "616a1f44",
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</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>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\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>S</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</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>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0  22.0      1      0   7.2500        S      6\n",
       "1         1       1    1  38.0      1      0  71.2833        C      7\n",
       "2         1       3    1  26.0      0      0   7.9250        S      5\n",
       "3         1       1    1  35.0      1      0  53.1000        S      7\n",
       "4         0       3    0  35.0      0      0   8.0500        S      6"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6f4e404a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\my_env\\lib\\site-packages\\seaborn\\axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2397f128760>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, row='Pclass', col='Sex', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "16db7caf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "guess_ages = np.zeros((2,3))\n",
    "guess_ages\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ac9ed3aa",
   "metadata": {
    "scrolled": true
   },
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0   22      1      0   7.2500        S      6\n",
       "1         1       1    1   38      1      0  71.2833        C      7\n",
       "2         1       3    1   26      0      0   7.9250        S      5\n",
       "3         1       1    1   35      1      0  53.1000        S      7\n",
       "4         0       3    0   35      0      0   8.0500        S      6"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            guess_df = dataset[(dataset['Sex'] == i) & \\\n",
    "                                  (dataset['Pclass'] == j+1)]['Age'].dropna()\n",
    "            \n",
    "            age_guess = guess_df.median()\n",
    "\n",
    "            # Convert random age float to nearest .5 age\n",
    "            guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5\n",
    "            \n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            dataset.loc[ (dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1),\\\n",
    "                    'Age'] = guess_ages[i,j]\n",
    "\n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f166c917",
   "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>AgeBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "      <td>0.337374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "      <td>0.412037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(48.0, 64.0]</td>\n",
       "      <td>0.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(64.0, 80.0]</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         AgeBand  Survived\n",
       "0  (-0.08, 16.0]  0.550000\n",
       "1   (16.0, 32.0]  0.337374\n",
       "2   (32.0, 48.0]  0.412037\n",
       "3   (48.0, 64.0]  0.434783\n",
       "4   (64.0, 80.0]  0.090909"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['AgeBand'] = pd.cut(train_df['Age'], 5)\n",
    "train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7072b93d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
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       "      <th>1</th>\n",
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       "      <td>6</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title  \\\n",
       "0         0       3    0    1      1      0   7.2500        S      6   \n",
       "1         1       1    1    2      1      0  71.2833        C      7   \n",
       "2         1       3    1    1      0      0   7.9250        S      5   \n",
       "3         1       1    1    2      1      0  53.1000        S      7   \n",
       "4         0       3    0    2      0      0   8.0500        S      6   \n",
       "\n",
       "        AgeBand  \n",
       "0  (16.0, 32.0]  \n",
       "1  (32.0, 48.0]  \n",
       "2  (16.0, 32.0]  \n",
       "3  (32.0, 48.0]  \n",
       "4  (32.0, 48.0]  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "for dataset in combine:    \n",
    "    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n",
    "    dataset.loc[ dataset['Age'] > 64, 'Age'] = 4\n",
    "train_df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a711de8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0    1      1      0   7.2500        S      6\n",
       "1         1       1    1    2      1      0  71.2833        C      7\n",
       "2         1       3    1    1      0      0   7.9250        S      5\n",
       "3         1       1    1    2      1      0  53.1000        S      7\n",
       "4         0       3    0    2      0      0   8.0500        S      6"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['AgeBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7c3e5239",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.552795</td>\n",
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       "      <th>8</th>\n",
       "      <td>11</td>\n",
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      ],
      "text/plain": [
       "   FamilySize  Survived\n",
       "3           4  0.724138\n",
       "2           3  0.578431\n",
       "1           2  0.552795\n",
       "6           7  0.333333\n",
       "0           1  0.303538\n",
       "4           5  0.200000\n",
       "5           6  0.136364\n",
       "7           8  0.000000\n",
       "8          11  0.000000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n",
    "\n",
    "train_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "12ad8cdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   IsAlone  Survived\n",
       "0        0  0.505650\n",
       "1        1  0.303538"
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     "execution_count": 37,
     "metadata": {},
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   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['IsAlone'] = 0\n",
    "    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\n",
    "\n",
    "train_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a1f976d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>S</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp     Fare Embarked  Title  FamilySize  \\\n",
       "0         0       3    0    1      1   7.2500        S      6           2   \n",
       "1         1       1    1    2      1  71.2833        C      7           2   \n",
       "2         1       3    1    1      0   7.9250        S      5           1   \n",
       "3         1       1    1    2      1  53.1000        S      7           2   \n",
       "4         0       3    0    2      0   8.0500        S      6           1   \n",
       "\n",
       "   IsAlone  \n",
       "0        0  \n",
       "1        0  \n",
       "2        1  \n",
       "3        0  \n",
       "4        1  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Parch',], axis=1)\n",
    "test_df = test_df.drop(['Parch'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b85795fb",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age*Class</th>\n",
       "      <th>Age</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age*Class  Age  Pclass\n",
       "0          3    1       3\n",
       "1          2    2       1\n",
       "2          3    1       3\n",
       "3          2    2       1\n",
       "4          6    2       3\n",
       "5          3    1       3\n",
       "6          3    3       1\n",
       "7          0    0       3\n",
       "8          3    1       3\n",
       "9          0    0       2"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Age*Class'] = dataset.Age * dataset.Pclass\n",
    "\n",
    "train_df.loc[:, ['Age*Class', 'Age', 'Pclass']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6f57365d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_port = train_df.Embarked.dropna().mode()[0]\n",
    "freq_port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cb4a7bc6",
   "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",
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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>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>0.339009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.339009"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)\n",
    "    \n",
    "train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "67e3b288",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>3</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp     Fare  Embarked  Title  FamilySize  \\\n",
       "0         0       3    0    1      1   7.2500         0      6           2   \n",
       "1         1       1    1    2      1  71.2833         1      7           2   \n",
       "2         1       3    1    1      0   7.9250         0      5           1   \n",
       "3         1       1    1    2      1  53.1000         0      7           2   \n",
       "4         0       3    0    2      0   8.0500         0      6           1   \n",
       "\n",
       "   IsAlone  Age*Class  \n",
       "0        0          3  \n",
       "1        0          2  \n",
       "2        1          3  \n",
       "3        0          2  \n",
       "4        1          6  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7aa455c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
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       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  SibSp     Fare  Embarked  Title  FamilySize  \\\n",
       "0          892       3    0    2      0   7.8292         2      6           1   \n",
       "1          893       3    1    2      1   7.0000         0      7           2   \n",
       "2          894       2    0    3      0   9.6875         2      6           1   \n",
       "3          895       3    0    1      0   8.6625         0      6           1   \n",
       "4          896       3    1    1      1  12.2875         0      7           3   \n",
       "\n",
       "   IsAlone  Age*Class  \n",
       "0        1          6  \n",
       "1        0          6  \n",
       "2        1          6  \n",
       "3        1          3  \n",
       "4        0          3  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "43d18ccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FareBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <td>(-0.001, 7.91]</td>\n",
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       "      <th>1</th>\n",
       "      <td>(7.91, 14.454]</td>\n",
       "      <td>0.303571</td>\n",
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       "      <th>2</th>\n",
       "      <td>(14.454, 31.0]</td>\n",
       "      <td>0.454955</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(31.0, 512.329]</td>\n",
       "      <td>0.581081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          FareBand  Survived\n",
       "0   (-0.001, 7.91]  0.197309\n",
       "1   (7.91, 14.454]  0.303571\n",
       "2   (14.454, 31.0]  0.454955\n",
       "3  (31.0, 512.329]  0.581081"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)\n",
    "train_df[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "eed7b3c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Fare  Embarked  Title  FamilySize  \\\n",
       "0         0       3    0    1      1     0         0      6           2   \n",
       "1         1       1    1    2      1     3         1      7           2   \n",
       "2         1       3    1    1      0     1         0      5           1   \n",
       "3         1       1    1    2      1     3         0      7           2   \n",
       "4         0       3    0    2      0     1         0      6           1   \n",
       "5         0       3    0    1      0     1         2      6           1   \n",
       "6         0       1    0    3      0     3         0      6           1   \n",
       "7         0       3    0    0      3     2         0      4           5   \n",
       "8         1       3    1    1      0     1         0      7           3   \n",
       "9         1       2    1    0      1     2         1      7           2   \n",
       "\n",
       "   IsAlone  Age*Class  \n",
       "0        0          3  \n",
       "1        0          2  \n",
       "2        1          3  \n",
       "3        0          2  \n",
       "4        1          6  \n",
       "5        1          3  \n",
       "6        1          3  \n",
       "7        0          0  \n",
       "8        0          3  \n",
       "9        0          0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "for dataset in combine:\n",
    "    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0\n",
    "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n",
    "    dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)\n",
    "\n",
    "train_df = train_df.drop(['FareBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "    \n",
    "train_df.head(10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "986f0c3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  SibSp  Fare  Embarked  Title  FamilySize  \\\n",
       "0          892       3    0    2      0     0         2      6           1   \n",
       "1          893       3    1    2      1     0         0      7           2   \n",
       "2          894       2    0    3      0     1         2      6           1   \n",
       "3          895       3    0    1      0     1         0      6           1   \n",
       "4          896       3    1    1      1     1         0      7           3   \n",
       "5          897       3    0    0      0     1         0      6           1   \n",
       "6          898       3    1    1      0     0         2      5           1   \n",
       "7          899       2    0    1      1     2         0      6           3   \n",
       "8          900       3    1    1      0     0         1      7           1   \n",
       "9          901       3    0    1      2     2         0      6           3   \n",
       "\n",
       "   IsAlone  Age*Class  \n",
       "0        1          6  \n",
       "1        0          6  \n",
       "2        1          6  \n",
       "3        1          3  \n",
       "4        0          3  \n",
       "5        1          0  \n",
       "6        1          3  \n",
       "7        0          2  \n",
       "8        1          3  \n",
       "9        0          3  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Test dataset\n",
    "test_df.head(10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "68662080",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>8</th>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
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       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Age  SibSp  Fare  Embarked  Title  FamilySize  IsAlone  \\\n",
       "0       3    0    1      1     0         0      6           2        0   \n",
       "1       1    1    2      1     3         1      7           2        0   \n",
       "2       3    1    1      0     1         0      5           1        1   \n",
       "3       1    1    2      1     3         0      7           2        0   \n",
       "4       3    0    2      0     1         0      6           1        1   \n",
       "5       3    0    1      0     1         2      6           1        1   \n",
       "6       1    0    3      0     3         0      6           1        1   \n",
       "7       3    0    0      3     2         0      4           5        0   \n",
       "8       3    1    1      0     1         0      7           3        0   \n",
       "9       2    1    0      1     2         1      7           2        0   \n",
       "\n",
       "   Age*Class  \n",
       "0          3  \n",
       "1          2  \n",
       "2          3  \n",
       "3          2  \n",
       "4          6  \n",
       "5          3  \n",
       "6          3  \n",
       "7          0  \n",
       "8          3  \n",
       "9          0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train_df.drop(\"Survived\", axis=1)\n",
    "Y_train = train_df[\"Survived\"]\n",
    "X_test  = test_df.drop(\"PassengerId\", axis=1).copy()\n",
    "\n",
    "X_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "514a862b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Age  SibSp  Fare  Embarked  Title  FamilySize  IsAlone  \\\n",
       "0       3    0    2      0     0         2      6           1        1   \n",
       "1       3    1    2      1     0         0      7           2        0   \n",
       "2       2    0    3      0     1         2      6           1        1   \n",
       "3       3    0    1      0     1         0      6           1        1   \n",
       "4       3    1    1      1     1         0      7           3        0   \n",
       "\n",
       "   Age*Class  \n",
       "0          6  \n",
       "1          6  \n",
       "2          6  \n",
       "3          3  \n",
       "4          3  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ac719720",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "5    0\n",
       "6    0\n",
       "7    0\n",
       "8    1\n",
       "9    1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "17ea953a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.58"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, Y_train)\n",
    "Y_pred = logreg.predict(X_test)\n",
    "acc_log = round(logreg.score(X_train, Y_train) * 100, 2)\n",
    "acc_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d31d1ce0",
   "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>Feature</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>2.541738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fare</td>\n",
       "      <td>0.211437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Embarked</td>\n",
       "      <td>0.166123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Age*Class</td>\n",
       "      <td>-0.154625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Title</td>\n",
       "      <td>-0.198110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FamilySize</td>\n",
       "      <td>-0.243274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.291287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SibSp</td>\n",
       "      <td>-0.400577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>IsAlone</td>\n",
       "      <td>-0.728164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>-0.759891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Feature  Correlation\n",
       "1         Sex     2.541738\n",
       "4        Fare     0.211437\n",
       "5    Embarked     0.166123\n",
       "9   Age*Class    -0.154625\n",
       "6       Title    -0.198110\n",
       "7  FamilySize    -0.243274\n",
       "2         Age    -0.291287\n",
       "3       SibSp    -0.400577\n",
       "8     IsAlone    -0.728164\n",
       "0      Pclass    -0.759891"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(train_df.columns.delete(0))\n",
    "coeff_df.columns = ['Feature']\n",
    "coeff_df[\"Correlation\"] = pd.Series(logreg.coef_[0])\n",
    "\n",
    "coeff_df.sort_values(by='Correlation', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bdb82892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.49"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Support Vector Machines\n",
    "svc = SVC()\n",
    "svc.fit(X_train, Y_train)\n",
    "Y_pred = svc.predict(X_test)\n",
    "acc_svc = round(svc.score(X_train, Y_train) * 100, 2)\n",
    "acc_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5322bad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.29"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train, Y_train)\n",
    "Y_pred = knn.predict(X_test)\n",
    "acc_knn = round(knn.score(X_train, Y_train) * 100, 2)\n",
    "acc_knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "596221e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.79"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gaussian Naive Bayes\n",
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X_train, Y_train)\n",
    "Y_pred = gaussian.predict(X_test)\n",
    "acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)\n",
    "acc_gaussian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d1b57ed0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75.42"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perceptron\n",
    "perceptron = Perceptron()\n",
    "perceptron.fit(X_train, Y_train)\n",
    "Y_pred = perceptron.predict(X_test)\n",
    "acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)\n",
    "acc_perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "123bde11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89.11"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train, Y_train)\n",
    "Y_pred = decision_tree.predict(X_test)\n",
    "acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)\n",
    "acc_decision_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d00491b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89.11"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest\n",
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_pred = random_forest.predict(X_test)\n",
    "random_forest.score(X_train, Y_train)\n",
    "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n",
    "acc_random_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "991de2da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85.75"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gradient Boosting\n",
    "grad_boost = GradientBoostingClassifier(n_estimators = 100)\n",
    "grad_boost.fit(X_train, Y_train)\n",
    "Y_pred = grad_boost.predict(X_test)\n",
    "grad_boost.score(X_train, Y_train)\n",
    "acc_grad_boost = round(grad_boost.score(X_train, Y_train) * 100, 2)\n",
    "acc_grad_boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "758a2cb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.13"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RidgeClassifierCV\n",
    "Ridge= RidgeClassifierCV()\n",
    "Ridge.fit(X_train, Y_train)\n",
    "Y_pred = Ridge.predict(X_test)\n",
    "acc_Ridge= round(Ridge.score(X_train, Y_train) * 100, 2)\n",
    "acc_Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "488fefd4",
   "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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>89.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>89.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Grad boost</td>\n",
       "      <td>85.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>84.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>82.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>80.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>78.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Perceptron</td>\n",
       "      <td>75.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Model  Score\n",
       "3            Random Forest  89.11\n",
       "7            Decision Tree  89.11\n",
       "6               Grad boost  85.75\n",
       "1                      KNN  84.29\n",
       "0  Support Vector Machines  82.49\n",
       "2      Logistic Regression  80.58\n",
       "4              Naive Bayes  78.79\n",
       "5               Perceptron  75.42"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', 'Random Forest', 'Naive Bayes', 'Perceptron', 'Grad boost','Decision Tree'],\n",
    "    'Score': [acc_svc, acc_knn, acc_log, acc_random_forest, acc_gaussian, acc_perceptron, acc_grad_boost, acc_decision_tree]})\n",
    "\n",
    "models.sort_values(by='Score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a4efceb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Age  SibSp  Fare  Embarked  Title  FamilySize  IsAlone  \\\n",
       "0       3    0    2      0     0         2      6           1        1   \n",
       "1       3    1    2      1     0         0      7           2        0   \n",
       "2       2    0    3      0     1         2      6           1        1   \n",
       "3       3    0    1      0     1         0      6           1        1   \n",
       "4       3    1    1      1     1         0      7           3        0   \n",
       "5       3    0    0      0     1         0      6           1        1   \n",
       "6       3    1    1      0     0         2      5           1        1   \n",
       "7       2    0    1      1     2         0      6           3        0   \n",
       "8       3    1    1      0     0         1      7           1        1   \n",
       "9       3    0    1      2     2         0      6           3        0   \n",
       "\n",
       "   Age*Class  \n",
       "0          6  \n",
       "1          6  \n",
       "2          6  \n",
       "3          3  \n",
       "4          3  \n",
       "5          0  \n",
       "6          3  \n",
       "7          2  \n",
       "8          3  \n",
       "9          3  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Split the training set into a development and an evaluation sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_dev, X_eval, y_dev, y_eval = train_test_split(X_train,\n",
    "                                                Y_train,\n",
    "                                                test_size=0.2,\n",
    "                                                random_state=42)\n",
    "\n",
    "X_test.head(10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b3ec072f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Random Forest\n",
    "import time\n",
    "dict_clf = {}\n",
    "\n",
    "\n",
    "paramgrid = {\n",
    "    'n_estimators':      [100, 150, 200, 250, 300, 400, 500],\n",
    "    'criterion':         ['gini', 'entropy'],\n",
    "    'max_features':      ['auto', 'log2'],\n",
    "    'min_samples_leaf':  list(range(2, 8))\n",
    "}\n",
    "GS = GridSearchCV(RandomForestClassifier(random_state=77),\n",
    "                  paramgrid,\n",
    "                  cv=4)\n",
    "\n",
    "# Fit the data and record time taking to train\n",
    "t0 = time.time()\n",
    "GS.fit(X_dev, y_dev)\n",
    "t = time.time() - t0\n",
    "\n",
    "# Store best parameters, score and estimator\n",
    "best_clf = GS.best_estimator_\n",
    "best_params = GS.best_params_\n",
    "best_score = GS.best_score_\n",
    "\n",
    "name = 'RF'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "39da35d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8156424581005587"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_clf.fit(X_dev, y_dev)\n",
    "acc_eval = accuracy_score(y_eval, best_clf.predict(X_eval))\n",
    "\n",
    "dict_clf[name] = {\n",
    "    'best_par': best_params,\n",
    "    'best_clf': best_clf,\n",
    "    'best_score': best_score,\n",
    "    'score_eval': acc_eval,\n",
    "    'fit_time': t,\n",
    "}\n",
    "\n",
    "acc_eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4a561fb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. GradientBoosting\n",
    "paramgrid = {\n",
    "    'n_estimators':      [100, 150, 200, 250, 300, 400, 500],\n",
    "    'max_features':      ['auto', 'log2'],\n",
    "    'min_samples_leaf':  list(range(2, 7)),\n",
    "    'loss' :             ['deviance', 'exponential'],\n",
    "    'learning_rate':     [0.025, 0.05, 0.075, 0.1],\n",
    "}\n",
    "GS = GridSearchCV(GradientBoostingClassifier(random_state=77),\n",
    "                  paramgrid,\n",
    "                  cv=4)\n",
    "\n",
    "# Fit the data and record time taking to train\n",
    "t0 = time.time()\n",
    "GS.fit(X_dev, y_dev)\n",
    "t = time.time() - t0\n",
    "\n",
    "# Store best parameters, score and estimator\n",
    "best_clf = GS.best_estimator_\n",
    "best_params = GS.best_params_\n",
    "best_score = GS.best_score_\n",
    "\n",
    "name = 'GB'\n",
    "best_clf.fit(X_dev, y_dev)\n",
    "acc_eval = accuracy_score(y_eval, best_clf.predict(X_eval))\n",
    "\n",
    "dict_clf[name] = {\n",
    "    'best_par': best_params,\n",
    "    'best_clf': best_clf,\n",
    "    'best_score': best_score,\n",
    "    'score_eval': acc_eval,\n",
    "    'fit_time': t,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "45d39e6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8044692737430168"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "423b856c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF classifier:\n",
      "\t- Best score = 84.41%\n",
      "\t- Score on evaluation set = 81.56%\n",
      "\t- Fitting time = 8.5 min\n",
      "\t- Best parameters:\n",
      "\t\t* criterion: entropy\n",
      "\t\t* max_features: auto\n",
      "\t\t* min_samples_leaf: 6\n",
      "\t\t* n_estimators: 500\n",
      "GB classifier:\n",
      "\t- Best score = 83.85%\n",
      "\t- Score on evaluation set = 80.45%\n",
      "\t- Fitting time = 13.8 min\n",
      "\t- Best parameters:\n",
      "\t\t* learning_rate: 0.05\n",
      "\t\t* loss: deviance\n",
      "\t\t* max_features: log2\n",
      "\t\t* min_samples_leaf: 4\n",
      "\t\t* n_estimators: 100\n"
     ]
    }
   ],
   "source": [
    "for clf in dict_clf.keys():\n",
    "    print(\"{0} classifier:\\n\\t- Best score = {1:.2%}\".format(clf, dict_clf[clf]['best_score']))\n",
    "    print(\"\\t- Score on evaluation set = {0:.2%}\".format(dict_clf[clf]['score_eval']))\n",
    "    print(\"\\t- Fitting time = {0:.1f} min\".format(round(dict_clf[clf]['fit_time']/60, 1)))\n",
    "    print(\"\\t- Best parameters:\")\n",
    "    for par in sorted(dict_clf[clf]['best_par'].keys()):\n",
    "        print(\"\\t\\t* {0}: {1}\".format(par, dict_clf[clf]['best_par'][par]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "08c8481a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "estimators = [('RF', dict_clf['RF']['best_clf']),\n",
    "              ('GB', dict_clf['GB']['best_clf']),\n",
    "              ('KNN', knn), ('svc', svc), ('trees', decision_tree)]\n",
    "\n",
    "# Instantiate the VotingClassifier using hard voting\n",
    "voter = VotingClassifier(estimators=estimators, voting='hard')\n",
    "voter.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred = voter.predict(X_test).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "03af921a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test_df[\"PassengerId\"],\n",
    "        \"Survived\": Y_pred\n",
    "    })\n",
    "submission.to_csv('submission.csv', index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86994c07",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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