Titanic Data EDA Project 2.1.ipynb

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   "source": [
    "# Titanic Exploratory Data Analysis (EDA)\n",
    "## Overview\n",
    "## Understand the Data\n",
    "### Numerical data\n",
    "### Categorical data\n",
    "### Relationships using Charts\n",
    "## Explore\n",
    "### Charts\n",
    "### Correlation Matrix\n",
    "## Data preprocessing\n",
    "### Divide\n",
    "### Imputation\n",
    "### Encoding\n",
    "\n",
    "Data Source: https://data.worldbank.org/indicator/SP.POP.TOTL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e839785a-5e0e-4738-808a-79f9719611c9",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
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   "id": "f4c02ad7-c5df-4f7a-831e-11310152f549",
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   "source": [
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from decimal import Decimal\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from category_encoders import OneHotEncoder\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf85b11a-027a-47e1-8868-f3b37e82fcb6",
   "metadata": {},
   "source": [
    "## Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6bc094c4-39a4-41ca-b46b-2aa91bf16206",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df = pd.read_csv(r\"C:\\Users\\Administrator\\Desktop\\train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "87c277b3-6e28-49ec-8965-2f1c1dbd5755",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <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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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <td>22.0</td>\n",
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       "      <td>A/5 21171</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>26.0</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
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       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
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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>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <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",
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       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
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       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "<p>891 rows × 12 columns</p>\n",
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       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "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]"
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   "source": [
    "df"
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   "cell_type": "markdown",
   "id": "a9e3f673-4cc0-42f3-8d97-a3b252005523",
   "metadata": {},
   "source": [
    "## Understand the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "7a68a652-aa34-4a53-9ad7-67e0136ced1d",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 8 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Sex       891 non-null    object \n",
      " 3   Age       714 non-null    float64\n",
      " 4   SibSp     891 non-null    int64  \n",
      " 5   Parch     891 non-null    int64  \n",
      " 6   Fare      891 non-null    float64\n",
      " 7   Embarked  889 non-null    object \n",
      "dtypes: float64(2), int64(4), object(2)\n",
      "memory usage: 55.8+ KB\n"
     ]
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   "source": [
    "df.info()"
   ]
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  {
   "cell_type": "markdown",
   "id": "ca0d4280-697a-4be5-9b52-8d3bd639e2ac",
   "metadata": {},
   "source": [
    "There are categorical and numeric data\n",
    "Categorical columns  - Name, Sex, Ticket, Cabin, Embarked\n",
    "Numeric columns - Survived, Pclass, Age, SibSp, Parch, Fare\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a985eb45-b890-4626-98dd-7c3400d770b4",
   "metadata": {},
   "source": [
    "Divide data into numeric and categotical data to understand relationships"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eae12cd5-53a3-4b1a-9043-8dd25f169691",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_num = df[[\"Survived\", \"Pclass\", \"Age\", \"SibSp\", \"Parch\", \"Fare\"]]\n",
    "df_cat = df[[\"Name\", \"Sex\", \"Ticket\", \"Cabin\", \"Embarked\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "badf14e4-89b0-4170-b064-f3c9a4b88f76",
   "metadata": {},
   "source": [
    "### Numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fa432c08-4658-4514-ba32-76cf99bd766d",
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   "outputs": [
    {
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       "      <th>887</th>\n",
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       "      <th>888</th>\n",
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       "      <td>23.4500</td>\n",
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       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
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       "<p>891 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "     Survived  Pclass   Age  SibSp  Parch     Fare\n",
       "0           0       3  22.0      1      0   7.2500\n",
       "1           1       1  38.0      1      0  71.2833\n",
       "2           1       3  26.0      0      0   7.9250\n",
       "3           1       1  35.0      1      0  53.1000\n",
       "4           0       3  35.0      0      0   8.0500\n",
       "..        ...     ...   ...    ...    ...      ...\n",
       "886         0       2  27.0      0      0  13.0000\n",
       "887         1       1  19.0      0      0  30.0000\n",
       "888         0       3   NaN      1      2  23.4500\n",
       "889         1       1  26.0      0      0  30.0000\n",
       "890         0       3  32.0      0      0   7.7500\n",
       "\n",
       "[891 rows x 6 columns]"
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     },
     "execution_count": 39,
     "metadata": {},
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   "source": [
    "df_num"
   ]
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  {
   "cell_type": "markdown",
   "id": "9ff1d8f5-a4a9-4bf7-ba6f-654837368c57",
   "metadata": {},
   "source": [
    "### Categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6352621c-7ece-42bd-b476-eb1650c5c102",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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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>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>113803</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>373450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>211536</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>112053</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>111369</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>370376</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Name     Sex  \\\n",
       "0                              Braund, Mr. Owen Harris    male   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female   \n",
       "2                               Heikkinen, Miss. Laina  female   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   \n",
       "4                             Allen, Mr. William Henry    male   \n",
       "..                                                 ...     ...   \n",
       "886                              Montvila, Rev. Juozas    male   \n",
       "887                       Graham, Miss. Margaret Edith  female   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   \n",
       "889                              Behr, Mr. Karl Howell    male   \n",
       "890                                Dooley, Mr. Patrick    male   \n",
       "\n",
       "               Ticket Cabin Embarked  \n",
       "0           A/5 21171   NaN        S  \n",
       "1            PC 17599   C85        C  \n",
       "2    STON/O2. 3101282   NaN        S  \n",
       "3              113803  C123        S  \n",
       "4              373450   NaN        S  \n",
       "..                ...   ...      ...  \n",
       "886            211536   NaN        S  \n",
       "887            112053   B42        S  \n",
       "888        W./C. 6607   NaN        S  \n",
       "889            111369  C148        C  \n",
       "890            370376   NaN        Q  \n",
       "\n",
       "[891 rows x 5 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1468090-7c6b-44a8-a877-d8a0bd846904",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d7374a0d-7322-4209-acfd-3dedfafbc61f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb65051f-7099-4864-9bd5-07630382fd46",
   "metadata": {},
   "source": [
    "### Missing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fffa97c-9258-4fea-bd80-12357b1d5b7b",
   "metadata": {},
   "source": [
    "\n",
    "How to handle missing data - \n",
    "\n",
    "The rule of thumb is if 50% of data is missing, drop column, otherwise, impute with mean or median.\n",
    "\n",
    "Age column has 19% of its data missing, hence impute with fillna method.\n",
    "\n",
    "Cabin has 77% of its data missing - as shown below, hence \"Cabin\" column needs to be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "79d4ecc8-bb5f-4461-bac3-3f088652ef9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0.000000\n",
       "Survived       0.000000\n",
       "Pclass         0.000000\n",
       "Name           0.000000\n",
       "Sex            0.000000\n",
       "Age            0.198653\n",
       "SibSp          0.000000\n",
       "Parch          0.000000\n",
       "Ticket         0.000000\n",
       "Fare           0.000000\n",
       "Cabin          0.771044\n",
       "Embarked       0.002245\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()/len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff637d09-a0f1-4f65-b4f1-b2ff740fffa8",
   "metadata": {},
   "source": [
    "### Unnecessary Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90f16384-eaab-4a0c-a922-5995aef3a668",
   "metadata": {},
   "source": [
    "Drop the following unnecessary columns PassengerId, Name, Cabin, and Ticket\n",
    "\n",
    "Name and ticket have high cardinality as shown below, hence will be dropped\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d933d608-1996-4cc9-8c61-f5305a13782e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name        891\n",
       "Sex           2\n",
       "Ticket      681\n",
       "Cabin       147\n",
       "Embarked      3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(\"object\").nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ddadf55d-f082-4677-949d-c50dfea249e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop([\"PassengerId\", \"Name\", \"Ticket\", \"Cabin\"], axis=\"columns\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e447d9ed-0cff-405d-b954-29caed6d32db",
   "metadata": {},
   "source": [
    "## Explore"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41d6da8b-085f-4d0f-b0ad-ba42c49da8fd",
   "metadata": {},
   "source": [
    "It is time to explore to understand the distributions and the relationships between them\n",
    "\n",
    "Let's start with a histogram that shows the age distribution\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07106228-78c9-4c29-895a-583ba6847028",
   "metadata": {},
   "source": [
    "We find that the ages of the passengers are evenly distributed while the rest of the numerical variables are skewed as shown in the below charts.\n",
    "    \n",
    "This is interesting because it points to the need to normalize our entire data before we subject it to model building. We will handle that later on. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acd164e6-1cc2-4979-9965-b354b357b4de",
   "metadata": {},
   "source": [
    "### Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "047234d2-edf1-48ac-ac38-22943dfbc387",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Age\"].hist(bins=8)\n",
    "plt.xlabel(\"Ages\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Age Distribution\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "109e0e2a-d935-449b-943c-6f91313b6cfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"SibSp\"].hist()\n",
    "plt.xlabel(\"SibSp Distribution\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"SibSp\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4c4d3442-ecdd-421c-afb9-06a7d1a2f1c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUwtq2HE4HLJYLF5fdrvd87jb7ZbD4VBSUpKioqI0btw4HTlyxGsOp9OpefPmqW/fvoqJidHUqVN16tSprr4UAAAQooK+snPjjTeqpqbG83Xo0CHPYytWrFBxcbHWrFmjqqoq2e12ZWdnq7Gx0TMmLy9P27dvV2lpqfbt26empiZNnjxZbW1twbgcAAAQYsKDXkB4uNdqTge3263Vq1dr8eLFmjZtmiRp06ZNSkhI0NatWzVnzhydPXtW69ev13PPPacJEyZIkrZs2aLk5GTt2bNHEydO7NJrAQAAoSfoYee9995TUlKSbDabRo4cqcLCQn3ta1/T8ePHVVtbq5ycHM9Ym82msWPHqrKyUnPmzFF1dbVcLpfXmKSkJKWlpamysvKiYcfpdMrpdHq2GxoaJEkul0sul8tv19Yxl62H229zdhV/9uFKzxmMc3c39Mo4emUcvTKOXhkXyF4ZnTOoYWfkyJHavHmzhgwZoo8//ljLli3T6NGjdeTIEdXW1kqSEhISvI5JSEjQiRMnJEm1tbWKiIhQ7969O43pOP5CioqKtGTJkk77y8rKFB0dfbWX1cnSzHa/zxloO3fuDNq5y8vLg3bu7oZeGUevjKNXxtEr4wLRq+bmZkPjghp2Jk2a5Pk+PT1do0aN0vXXX69NmzbpW9/6liTJYrF4HeN2uzvt+7LLjVm0aJHy8/M92w0NDUpOTlZOTo7i4uJ8uZQLcrlcKi8v1xMHesjZfumaQ81hR9c/BdjRr+zsbFmt1i4/f3dCr4yjV8bRK+PolXGB7FXHMzOXE/Snsf5dTEyM0tPT9d577+mee+6R9MXqTWJiomdMXV2dZ7XHbrertbVV9fX1Xqs7dXV1Gj169EXPY7PZZLPZOu23Wq0B+aV1tlvkbOteYSeYf7yB+jmYEb0yjl4ZR6+Mo1fGBaJXRucL+qux/p3T6dTRo0eVmJiolJQU2e12r2Wv1tZWVVRUeIJMRkaGrFar15iamhodPnz4kmEHAABcO4K6slNQUKApU6Zo4MCBqqur07Jly9TQ0KDZs2fLYrEoLy9PhYWFSk1NVWpqqgoLCxUdHa0ZM2ZIknr16qXc3FwtWLBA8fHx6tOnjwoKCpSenu55dRYAALi2BTXsnDp1St/97nf16aefql+/fvrWt76l119/XYMGDZIkLVy4UC0tLZo7d67q6+s1cuRIlZWVKTY21jPHqlWrFB4erunTp6ulpUVZWVnauHGjwsLCgnVZAAAghAQ17JSWll7ycYvFIofDIYfDcdExkZGRKikpUUlJiZ+rAwAAZhBS9+wAAAD4G2EHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYmk9h5/jx4/6uAwAAICB8CjuDBw/W+PHjtWXLFp0/f97fNQEAAPiNT2Hnrbfe0ogRI7RgwQLZ7XbNmTNH+/fv93dtAAAAV82nsJOWlqbi4mKdPn1aGzZsUG1trW699VbdeOONKi4u1ieffOLvOgEAAHxyVTcoh4eH695779Uf/vAHPfnkk/rnP/+pgoICDRgwQLNmzVJNTY2/6gQAAPDJVYWdAwcOaO7cuUpMTFRxcbEKCgr0z3/+U3v37tXp06d19913+6tOAAAAn/gUdoqLi5Wenq7Ro0fro48+0ubNm3XixAktW7ZMKSkpGjNmjH7729/qjTfeMDxnUVGRLBaL8vLyPPvcbrccDoeSkpIUFRWlcePG6ciRI17HOZ1OzZs3T3379lVMTIymTp2qU6dO+XJZAADAhHwKO2vXrtWMGTN08uRJ7dixQ5MnT1aPHt5TDRw4UOvXrzc0X1VVldatW6fhw4d77V+xYoWKi4u1Zs0aVVVVyW63Kzs7W42NjZ4xeXl52r59u0pLS7Vv3z41NTVp8uTJamtr8+XSAACAyfgUdt577z0tWrRIdrv9omMiIiI0e/bsy87V1NSkmTNn6tlnn1Xv3r09+91ut1avXq3Fixdr2rRpSktL06ZNm9Tc3KytW7dKks6ePav169dr5cqVmjBhgkaMGKEtW7bo0KFD2rNnjy+XBgAATCbcl4M2bNignj176r777vPa/8c//lHNzc2GQk6HRx99VHfddZcmTJigZcuWefYfP35ctbW1ysnJ8eyz2WwaO3asKisrNWfOHFVXV8vlcnmNSUpKUlpamiorKzVx4sQLntPpdMrpdHq2GxoaJEkul0sul8tw7ZfTMZeth9tvc3YVf/bhSs8ZjHN3N/TKOHplHL0yjl4ZF8heGZ3Tp7CzfPly/eY3v+m0v3///vrhD39oOOyUlpbqjTfeUFVVVafHamtrJUkJCQle+xMSEnTixAnPmIiICK8VoY4xHcdfSFFRkZYsWdJpf1lZmaKjow3VfiWWZrb7fc5A27lzZ9DOXV5eHrRzdzf0yjh6ZRy9Mo5eGReIXjU3Nxsa51PYOXHihFJSUjrtHzRokE6ePGlojg8//FDz589XWVmZIiMjLzrOYrF4bbvd7k77vuxyYxYtWqT8/HzPdkNDg5KTk5WTk6O4uDhD9RvhcrlUXl6uJw70kLP90jWHmsOOC6+KBVJHv7Kzs2W1Wrv8/N0JvTKOXhlHr4yjV8YFslcdz8xcjk9hp3///nr77bd13XXXee1/6623FB8fb2iO6upq1dXVKSMjw7Ovra1Nr776qtasWaNjx45J+mL1JjEx0TOmrq7Os9pjt9vV2tqq+vp6r9Wduro6jR49+qLnttlsstlsnfZbrdaA/NI62y1ytnWvsBPMP95A/RzMiF4ZR6+Mo1fG0SvjAtEro/P5dIPyd77zHf3oRz/Syy+/rLa2NrW1tWnv3r2aP3++vvOd7xiaIysrS4cOHdLBgwc9X5mZmZo5c6YOHjyor33ta7Lb7V7LXq2traqoqPAEmYyMDFmtVq8xNTU1Onz48CXDDgAAuHb4tLKzbNkynThxQllZWQoP/2KK9vZ2zZo1S4WFhYbmiI2NVVpamte+mJgYxcfHe/bn5eWpsLBQqampSk1NVWFhoaKjozVjxgxJUq9evZSbm6sFCxYoPj5effr0UUFBgdLT0zVhwgRfLg0AAJiMT2EnIiJCzz//vJYuXaq33npLUVFRSk9P16BBg/xa3MKFC9XS0qK5c+eqvr5eI0eOVFlZmWJjYz1jVq1apfDwcE2fPl0tLS3KysrSxo0bFRYW5tdaAABA9+RT2OkwZMgQDRkyxF+16JVXXvHatlgscjgccjgcFz0mMjJSJSUlKikp8VsdAADAPHwKO21tbdq4caP++te/qq6uTu3t3i+t3rt3r1+KAwAAuFo+hZ358+dr48aNuuuuu5SWlnbZl4IDAAAEi09hp7S0VH/4wx905513+rseAAAAv/LppecREREaPHiwv2sBAADwO5/CzoIFC/T000/L7e5+n/kEAACuLT49jbVv3z69/PLL2rVrl2688cZO72C4bds2vxQHAABwtXwKO1/5yld07733+rsWAAAAv/Mp7GzYsMHfdQAAAASET/fsSNLnn3+uPXv26Le//a0aGxslSR999JGampr8VhwAAMDV8mll58SJE/r2t7+tkydPyul0Kjs7W7GxsVqxYoXOnz+v3/zmN/6uEwAAwCc+rezMnz9fmZmZqq+vV1RUlGf/vffeq7/+9a9+Kw4AAOBq+fxqrL/97W+KiIjw2j9o0CCdPn3aL4UBAAD4g08rO+3t7Wpra+u0/9SpU16fSA4AABBsPoWd7OxsrV692rNtsVjU1NSkn//853yEBAAACCk+PY21atUqjR8/XsOGDdP58+c1Y8YMvffee+rbt69+//vf+7tGAAAAn/kUdpKSknTw4EH9/ve/1xtvvKH29nbl5uZq5syZXjcsAwAABJtPYUeSoqKi9NBDD+mhhx7yZz0AAAB+5VPY2bx58yUfnzVrlk/FAAAA+JtPYWf+/Ple2y6XS83NzYqIiFB0dDRhBwAAhAyfXo1VX1/v9dXU1KRjx47p1ltv5QZlAAAQUnz+bKwvS01N1fLlyzut+gAAAAST38KOJIWFhemjjz7y55QAAABXxad7dl544QWvbbfbrZqaGq1Zs0ZjxozxS2EAAAD+4FPYueeee7y2LRaL+vXrpzvuuEMrV670R10AAAB+4VPYaW9v93cdAAAAAeHXe3YAAABCjU8rO/n5+YbHFhcX+3IKAAAAv/Ap7Lz55pt644039Pnnn+vrX/+6JOndd99VWFiYbr75Zs84i8XinyoBAAB85FPYmTJlimJjY7Vp0yb17t1b0hdvNPj9739ft912mxYsWODXIgEAAHzl0z07K1euVFFRkSfoSFLv3r21bNkyXo0FAABCik9hp6GhQR9//HGn/XV1dWpsbLzqogAAAPzFp7Bz77336vvf/77+9Kc/6dSpUzp16pT+9Kc/KTc3V9OmTfN3jQAAAD7z6Z6d3/zmNyooKNADDzwgl8v1xUTh4crNzdVTTz3l1wIBAACuhk9hJzo6Ws8884yeeuop/fOf/5Tb7dbgwYMVExPj7/oAAACuylW9qWBNTY1qamo0ZMgQxcTEyO12+6suAAAAv/Ap7Jw5c0ZZWVkaMmSI7rzzTtXU1EiSHn74YV52DgAAQopPYefHP/6xrFarTp48qejoaM/++++/Xy+99JLfigMAALhaPt2zU1ZWpt27d2vAgAFe+1NTU3XixAm/FAYAAOAPPq3snDt3zmtFp8Onn34qm8121UUBAAD4i09h5/bbb9fmzZs92xaLRe3t7Xrqqac0fvx4vxUHAABwtXx6Guupp57SuHHjdODAAbW2tmrhwoU6cuSI/vWvf+lvf/ubv2sEAADwmU8rO8OGDdPbb7+tb37zm8rOzta5c+c0bdo0vfnmm7r++usNz7N27VoNHz5ccXFxiouL06hRo7Rr1y7P4263Ww6HQ0lJSYqKitK4ceN05MgRrzmcTqfmzZunvn37KiYmRlOnTtWpU6d8uSwAAGBCVxx2XC6Xxo8fr4aGBi1ZskQvvviidu7cqWXLlikxMfGK5howYICWL1+uAwcO6MCBA7rjjjt09913ewLNihUrVFxcrDVr1qiqqkp2u13Z2dlen7+Vl5en7du3q7S0VPv27VNTU5MmT56stra2K700AABgQlccdqxWqw4fPiyLxXLVJ58yZYruvPNODRkyREOGDNEvf/lL9ezZU6+//rrcbrdWr16txYsXa9q0aUpLS9OmTZvU3NysrVu3SpLOnj2r9evXa+XKlZowYYJGjBihLVu26NChQ9qzZ89V1wcAALo/n+7ZmTVrltavX6/ly5f7rZC2tjb98Y9/1Llz5zRq1CgdP35ctbW1ysnJ8Yyx2WwaO3asKisrNWfOHFVXV8vlcnmNSUpKUlpamiorKzVx4sQLnsvpdMrpdHq2GxoaJH2xatXxWV/+0DGXrUf3e2dpf/bhSs8ZjHN3N/TKOHplHL0yjl4ZF8heGZ3Tp7DT2tqq3/3udyovL1dmZmanz8QqLi42PNehQ4c0atQonT9/Xj179tT27ds1bNgwVVZWSpISEhK8xickJHjey6e2tlYRERHq3bt3pzG1tbUXPWdRUZGWLFnSaX9ZWdkFX1J/tZZmtvt9zkDbuXNn0M5dXl4etHN3N/TKOHplHL0yjl4ZF4heNTc3Gxp3RWHn/fff13XXXafDhw/r5ptvliS9++67XmOu9Omtr3/96zp48KA+++wz/fnPf9bs2bNVUVFx0fncbvdlz3G5MYsWLVJ+fr5nu6GhQcnJycrJyVFcXNwV1X8pLpdL5eXleuJADznbr/5pv6502HHhVbFA6uhXdna2rFZrl5+/O6FXxtEr4+iVcfTKuED2quOZmcu5orCTmpqqmpoavfzyy5K++HiIX/3qV51WX65ERESEBg8eLEnKzMxUVVWVnn76af3kJz+R9MXqzb/f+FxXV+c5n91uV2trq+rr671Wd+rq6jR69OiLntNms13wzQ+tVmtAfmmd7RY527pX2AnmH2+gfg5mRK+Mo1fG0Svj6JVxgeiV0fmu6AblL3+q+a5du3Tu3LkrmcLQOZxOp1JSUmS3272WvVpbW1VRUeEJMhkZGbJarV5jampqdPjw4UuGHQAAcO3w6Z6dDl8OP1fq8ccf16RJk5ScnKzGxkaVlpbqlVde0UsvvSSLxaK8vDwVFhYqNTVVqampKiwsVHR0tGbMmCFJ6tWrl3Jzc7VgwQLFx8erT58+KigoUHp6uiZMmHBVtQEAAHO4orBjsVg63QtzNS9B//jjj/W9731PNTU16tWrl4YPH66XXnpJ2dnZkqSFCxeqpaVFc+fOVX19vUaOHKmysjLFxsZ65li1apXCw8M1ffp0tbS0KCsrSxs3blRYWJjPdQEAAPO4orDjdrv14IMPeu53OX/+vB555JFOr8batm2bofnWr19/ycctFoscDoccDsdFx0RGRqqkpEQlJSWGzgkAAK4tVxR2Zs+e7bX9wAMP+LUYAAAAf7uisLNhw4ZA1QEAABAQPn0QKAAAQHdB2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKYW1LBTVFSkW265RbGxserfv7/uueceHTt2zGuM2+2Ww+FQUlKSoqKiNG7cOB05csRrjNPp1Lx589S3b1/FxMRo6tSpOnXqVFdeCgAACFFBDTsVFRV69NFH9frrr6u8vFyff/65cnJydO7cOc+YFStWqLi4WGvWrFFVVZXsdruys7PV2NjoGZOXl6ft27ertLRU+/btU1NTkyZPnqy2trZgXBYAAAgh4cE8+UsvveS1vWHDBvXv31/V1dW6/fbb5Xa7tXr1ai1evFjTpk2TJG3atEkJCQnaunWr5syZo7Nnz2r9+vV67rnnNGHCBEnSli1blJycrD179mjixIldfl0AACB0BDXsfNnZs2clSX369JEkHT9+XLW1tcrJyfGMsdlsGjt2rCorKzVnzhxVV1fL5XJ5jUlKSlJaWpoqKysvGHacTqecTqdnu6GhQZLkcrnkcrn8dj0dc9l6uP02Z1fxZx+u9JzBOHd3Q6+Mo1fG0Svj6JVxgeyV0TlDJuy43W7l5+fr1ltvVVpamiSptrZWkpSQkOA1NiEhQSdOnPCMiYiIUO/evTuN6Tj+y4qKirRkyZJO+8vKyhQdHX3V1/JlSzPb/T5noO3cuTNo5y4vLw/aubsbemUcvTKOXhlHr4wLRK+am5sNjQuZsPPYY4/p7bff1r59+zo9ZrFYvLbdbnenfV92qTGLFi1Sfn6+Z7uhoUHJycnKyclRXFycD9VfmMvlUnl5uZ440EPO9kvXG2oOO7r+6b+OfmVnZ8tqtXb5+bsTemUcvTKOXhlHr4wLZK86npm5nJAIO/PmzdMLL7ygV199VQMGDPDst9vtkr5YvUlMTPTsr6ur86z22O12tba2qr6+3mt1p66uTqNHj77g+Ww2m2w2W6f9Vqs1IL+0znaLnG3dK+wE8483UD8HM6JXxtEr4+iVcfTKuED0yuh8QX01ltvt1mOPPaZt27Zp7969SklJ8Xo8JSVFdrvda+mrtbVVFRUVniCTkZEhq9XqNaampkaHDx++aNgBAADXjqCu7Dz66KPaunWr/vKXvyg2NtZzj02vXr0UFRUli8WivLw8FRYWKjU1VampqSosLFR0dLRmzJjhGZubm6sFCxYoPj5effr0UUFBgdLT0z2vzgIAANeuoIadtWvXSpLGjRvntX/Dhg168MEHJUkLFy5US0uL5s6dq/r6eo0cOVJlZWWKjY31jF+1apXCw8M1ffp0tbS0KCsrSxs3blRYWFhXXQoAAAhRQQ07bvflX5JtsVjkcDjkcDguOiYyMlIlJSUqKSnxY3UAAMAM+GwsAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgauHBLgCh6bqf/m+Xn9MW5taKb0ppjt1ytlmu+PgPlt8VgKoAAN0dKzsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUghp2Xn31VU2ZMkVJSUmyWCzasWOH1+Nut1sOh0NJSUmKiorSuHHjdOTIEa8xTqdT8+bNU9++fRUTE6OpU6fq1KlTXXgVAAAglAU17Jw7d0433XST1qxZc8HHV6xYoeLiYq1Zs0ZVVVWy2+3Kzs5WY2OjZ0xeXp62b9+u0tJS7du3T01NTZo8ebLa2tq66jIAAEAIC+r77EyaNEmTJk264GNut1urV6/W4sWLNW3aNEnSpk2blJCQoK1bt2rOnDk6e/as1q9fr+eee04TJkyQJG3ZskXJycnas2ePJk6c2GXXAgAAQlPIvqng8ePHVVtbq5ycHM8+m82msWPHqrKyUnPmzFF1dbVcLpfXmKSkJKWlpamysvKiYcfpdMrpdHq2GxoaJEkul0sul8tv19Axl62H229zmllHn3ztlz9/dqGu41qvpWv2Fb0yjl4ZR6+MC2SvjM4ZsmGntrZWkpSQkOC1PyEhQSdOnPCMiYiIUO/evTuN6Tj+QoqKirRkyZJO+8vKyhQdHX21pXeyNLPd73Oama/92rlzp58rCX3l5eXBLqHboFfG0Svj6JVxgehVc3OzoXEhG3Y6WCzeHxvgdrs77fuyy41ZtGiR8vPzPdsNDQ1KTk5WTk6O4uLirq7gf+NyuVReXq4nDvSQs/3KP/7gWmPr4dbSzHaf+3XYce08bdnxu5WdnS2r1RrsckIavTKOXhlHr4wLZK86npm5nJANO3a7XdIXqzeJiYme/XV1dZ7VHrvdrtbWVtXX13ut7tTV1Wn06NEXndtms8lms3Xab7VaA/JL62y3+PRZT9cqX/t1Lf4HJ1C/s2ZEr4yjV8bRK+MC0Suj84Xs++ykpKTIbrd7LXu1traqoqLCE2QyMjJktVq9xtTU1Ojw4cOXDDsAAODaEdSVnaamJv3jH//wbB8/flwHDx5Unz59NHDgQOXl5amwsFCpqalKTU1VYWGhoqOjNWPGDElSr169lJubqwULFig+Pl59+vRRQUGB0tPTPa/OAgAA17aghp0DBw5o/Pjxnu2O+2hmz56tjRs3auHChWppadHcuXNVX1+vkSNHqqysTLGxsZ5jVq1apfDwcE2fPl0tLS3KysrSxo0bFRYW1uXXAwAAQk9Qw864cePkdl/8ZcYWi0UOh0MOh+OiYyIjI1VSUqKSkpIAVAgAALq7kL1nBwAAwB8IOwAAwNQIOwAAwNRC9n12gGvBdT/9X5+Os4W5teKbUppjd5e/h9MHy+/q0vMBwNViZQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJiaacLOM888o5SUFEVGRiojI0OvvfZasEsCAAAhwBRh5/nnn1deXp4WL16sN998U7fddpsmTZqkkydPBrs0AAAQZOHBLsAfiouLlZubq4cffliStHr1au3evVtr165VUVFRkKsDEArSHLvlbLMEuwzDPlh+V7BLuCZc99P/9ek4W5hbK74ZnN8rfjeuXLcPO62traqurtZPf/pTr/05OTmqrKwMUlUAcHV8/Uf4alztP+D8I4xQ1e3Dzqeffqq2tjYlJCR47U9ISFBtbe0Fj3E6nXI6nZ7ts2fPSpL+9a9/yeVy+a02l8ul5uZmhbt6qK29+/wfZbCEt7vV3Nzuc7/OnDkTgKoCK/zzc74dd5W9uhrdrc/8HRrH3+AVHBfEv8HBBX/o0vNdLVsPt/5zRLvOnDkjq9Xq17kbGxslSW63+5Ljun3Y6WCxeP+yud3uTvs6FBUVacmSJZ32p6SkBKQ2GDfjKo7tu9JvZXQLV9Orq3Gt9flaw9+gccH6G+yOAt2rxsZG9erV66KPd/uw07dvX4WFhXVaxamrq+u02tNh0aJFys/P92y3t7frX//6l+Lj4y8akHzR0NCg5ORkffjhh4qLi/PbvGZFv4yjV8bRK+PolXH0yrhA9srtdquxsVFJSUmXHNftw05ERIQyMjJUXl6ue++917O/vLxcd9999wWPsdlsstlsXvu+8pWvBKzGuLg4/hiuAP0yjl4ZR6+Mo1fG0SvjAtWrS63odOj2YUeS8vPz9b3vfU+ZmZkaNWqU1q1bp5MnT+qRRx4JdmkAACDITBF27r//fp05c0a/+MUvVFNTo7S0NO3cuVODBg0KdmkAACDITBF2JGnu3LmaO3dusMvwYrPZ9POf/7zTU2a4MPplHL0yjl4ZR6+Mo1fGhUKvLO7LvV4LAACgGzPFx0UAAABcDGEHAACYGmEHAACYGmEHAACYGmEngJ555hmlpKQoMjJSGRkZeu2114JdUkh69dVXNWXKFCUlJclisWjHjh3BLikkFRUV6ZZbblFsbKz69++ve+65R8eOHQt2WSFr7dq1Gj58uOeNzEaNGqVdu3YFu6yQV1RUJIvFory8vGCXEpIcDocsFovXl91uD3ZZIev06dN64IEHFB8fr+joaH3jG99QdXV1l9dB2AmQ559/Xnl5eVq8eLHefPNN3XbbbZo0aZJOnjwZ7NJCzrlz53TTTTdpzZo1wS4lpFVUVOjRRx/V66+/rvLycn3++efKycnRuXO+fZCh2Q0YMEDLly/XgQMHdODAAd1xxx26++67deTIkWCXFrKqqqq0bt06DR8+PNilhLQbb7xRNTU1nq9Dhw4Fu6SQVF9frzFjxshqtWrXrl165513tHLlyoB+YsHF8NLzABk5cqRuvvlmrV271rNv6NChuueee1RUVBTEykKbxWLR9u3bdc899wS7lJD3ySefqH///qqoqNDtt98e7HK6hT59+uipp55Sbm5usEsJOU1NTbr55pv1zDPPaNmyZfrGN76h1atXB7uskONwOLRjxw4dPHgw2KWEvJ/+9Kf629/+FhLParCyEwCtra2qrq5WTk6O1/6cnBxVVlYGqSqYzdmzZyV98Q84Lq2trU2lpaU6d+6cRo0aFexyQtKjjz6qu+66SxMmTAh2KSHvvffeU1JSklJSUvSd73xH77//frBLCkkvvPCCMjMzdd9996l///4aMWKEnn322aDUQtgJgE8//VRtbW2dPnU9ISGh06ezA75wu93Kz8/XrbfeqrS0tGCXE7IOHTqknj17ymaz6ZFHHtH27ds1bNiwYJcVckpLS/XGG2+w6mzAyJEjtXnzZu3evVvPPvusamtrNXr0aJ05cybYpYWc999/X2vXrlVqaqp2796tRx55RD/60Y+0efPmLq/FNB8XEYosFovXttvt7rQP8MVjjz2mt99+W/v27Qt2KSHt61//ug4ePKjPPvtMf/7znzV79mxVVFQQeP7Nhx9+qPnz56usrEyRkZHBLifkTZo0yfN9enq6Ro0apeuvv16bNm1Sfn5+ECsLPe3t7crMzFRhYaEkacSIETpy5IjWrl2rWbNmdWktrOwEQN++fRUWFtZpFaeurq7Tag9wpebNm6cXXnhBL7/8sgYMGBDsckJaRESEBg8erMzMTBUVFemmm27S008/HeyyQkp1dbXq6uqUkZGh8PBwhYeHq6KiQr/61a8UHh6utra2YJcY0mJiYpSenq733nsv2KWEnMTExE7/YzF06NCgvFCHsBMAERERysjIUHl5udf+8vJyjR49OkhVobtzu9167LHHtG3bNu3du1cpKSnBLqnbcbvdcjqdwS4jpGRlZenQoUM6ePCg5yszM1MzZ87UwYMHFRYWFuwSQ5rT6dTRo0eVmJgY7FJCzpgxYzq9Pca7776rQYMGdXktPI0VIPn5+fre976nzMxMjRo1SuvWrdPJkyf1yCOPBLu0kNPU1KR//OMfnu3jx4/r4MGD6tOnjwYOHBjEykLLo48+qq1bt+ovf/mLYmNjPSuHvXr1UlRUVJCrCz2PP/64Jk2apOTkZDU2Nqq0tFSvvPKKXnrppWCXFlJiY2M73fcVExOj+Ph47ge7gIKCAk2ZMkUDBw5UXV2dli1bpoaGBs2ePTvYpYWcH//4xxo9erQKCws1ffp07d+/X+vWrdO6deu6vhg3AubXv/61e9CgQe6IiAj3zTff7K6oqAh2SSHp5Zdfdkvq9DV79uxglxZSLtQjSe4NGzYEu7SQ9NBDD3n+/vr16+fOyspyl5WVBbusbmHs2LHu+fPnB7uMkHT//fe7ExMT3Var1Z2UlOSeNm2a+8iRI8EuK2T9z//8jzstLc1ts9ncN9xwg3vdunVBqYP32QEAAKbGPTsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsA8P/74IMPZLFYdPDgwWCXAsCPCDsAQtaDDz4oi8Uii8Uiq9Wqr33tayooKNC5c+eCXRqAboTPxgIQ0r797W9rw4YNcrlceu211/Twww/r3LlzWrt27RXN43a71dbWpvBw/rMHXGtY2QEQ0mw2m+x2u5KTkzVjxgzNnDlTO3bs0JYtW5SZmanY2FjZ7XbNmDFDdXV1nuNeeeUVWSwW7d69W5mZmbLZbHrttdfU3t6uJ598UoMHD5bNZtPAgQP1y1/+0uuc77//vsaPH6/o6GjddNNN+r//+7+uvmwAfkTYAdCtREVFyeVyqbW1VUuXLtVbb72lHTt26Pjx43rwwQc7jV+4cKGKiop09OhRDR8+XIsWLdKTTz6pJ554Qu+88462bt2qhIQEr2MWL16sgoICHTx4UEOGDNF3v/tdff755110hQD8jQ8CBRCyHnzwQX322WfasWOHJGn//v268847lZWVpeeff95rbFVVlb75zW+qsbFRPXv21CuvvKLx48drx44duvvuuyVJjY2N6tevn9asWaOHH3640/k++OADpaSk6He/+51yc3MlSe+8845uvPFGHT16VDfccENgLxhAQLCyAyCkvfjii+rZs6ciIyM1atQo3X777SopKdGbb76pu+++W4MGDVJsbKzGjRsnSTp58qTX8ZmZmZ7vjx49KqfTqaysrEuec/jw4Z7vExMTJcnrKTIA3QthB0BIGz9+vA4ePKhjx47p/Pnz2rZtm2JiYpSTk6OePXtqy5Ytqqqq0vbt2yVJra2tXsfHxMR4vo+KijJ0TqvV6vneYrFIktrb26/2UgAECWEHQEiLiYnR4MGDNWjQIE8I+fvf/65PP/1Uy5cv12233aYbbrjB0MpLamqqoqKi9Ne//jXQZQMIIbwGE0C3M3DgQEVERKikpESPPPKIDh8+rKVLl172uMjISP3kJz/RwoULFRERoTFjxuiTTz7RkSNHPPfoADAfVnYAdDv9+vXTxo0b9cc//lHDhg3T8uXL9V//9V+Gjn3iiSe0YMEC/exnP9PQoUN1//33cz8OYHK8GgsAAJgaKzsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDU/j9VycUUmjh9uQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Parch\"].hist()\n",
    "plt.xlabel(\"Parch\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Parch\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "83f12aa7-e21c-4827-a967-de6e032db276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGkhDTtut1s2m83v4XQ6ffsNw5Db7ZbL5VJ0dLSmTJmihoYGv2N4PB4tWbJEycnJio2N1axZs9TU1HS+LwUAAISpkK/sXHXVVWpubvY99u3b59u3Zs0aFRcXa8OGDdq1a5ecTqeys7PV0dHhm5OXl6etW7eqsrJSO3fu1JEjR5Sbm6uenp5QXA4AAAgzkSEvIDLSbzWnj2EYWr9+vVasWKHZs2dLksrLy5WSkqLNmzdr0aJFOnz4sDZt2qRnnnlG06dPlyRVVFQoNTVV27dv14wZM87rtQAAgPAT8rBz4MABuVwuORwOTZgwQYWFhfrSl76kgwcPqqWlRTk5Ob65DodDkydPVm1trRYtWqS6ujp5vV6/OS6XSxkZGaqtrT1l2PF4PPJ4PL7t9vZ2SZLX65XX6w3atfUdyzHECNoxz5dg9sEsfTUOhloHI/prLvprLvprvnDo8UDPHdKwM2HCBD399NMaM2aMPvroI61atUqTJk1SQ0ODWlpaJEkpKSl+z0lJSVFjY6MkqaWlRVFRURo2bFi/OX3PP5mioiKtXLmy33hVVZViYmLO9bL6eWR8b9CPabZt27aFuoQBq66uDnUJlkZ/zUV/zUV/zRfKHnd2dg5oXkjDzsyZM33/nZmZqYkTJ+qyyy5TeXm5rr/+ekmSzWbze45hGP3GPu9Mc5YvX678/Hzfdnt7u1JTU5WTk6P4+PhALuWkvF6vqqur9ZM3hsjTe/qaw029O/xfAuzrb3Z2tux2e6jLsRz6ay76ay76a75w6HHfKzNnEvKXsf5TbGysMjMzdeDAAd1+++2STqzejBgxwjentbXVt9rjdDrV3d2ttrY2v9Wd1tZWTZo06ZTncTgccjgc/cbtdrspPzBPr02ensEVdgbTHwezfm44gf6ai/6ai/6aL5Q9Huh5Q/5urP/k8Xj097//XSNGjFBaWpqcTqff8lh3d7dqamp8QSYrK0t2u91vTnNzs+rr608bdgAAwBdHSFd2li5dqttuu02XXHKJWltbtWrVKrW3t2v+/Pmy2WzKy8tTYWGh0tPTlZ6ersLCQsXExGju3LmSpISEBC1cuFAFBQVKSkpSYmKili5dqszMTN+7swAAwBdbSMNOU1OTvvOd7+jjjz/WRRddpOuvv16vvfaaRo0aJUlatmyZurq6tHjxYrW1tWnChAmqqqpSXFyc7xjr1q1TZGSk5syZo66uLk2bNk1lZWWKiIgI1WUBAIAwEtKwU1lZedr9NptNbrdbbrf7lHOGDh2qkpISlZSUBLk6AABgBWF1zw4AAECwEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClBRR2Dh48GOw6AAAATBFQ2Bk9erSmTp2qiooKHTt2LNg1AQAABE1AYefNN9/UuHHjVFBQIKfTqUWLFun1118Pdm0AAADnLKCwk5GRoeLiYn3wwQcqLS1VS0uLbrzxRl111VUqLi7Wv/71r2DXCQAAEJBzukE5MjJSd9xxh5599lk9+uijevfdd7V06VKNHDlS8+bNU3Nzc7DqBAAACMg5hZ033nhDixcv1ogRI1RcXKylS5fq3Xff1csvv6wPPvhAX//614NVJwAAQEAiA3lScXGxSktLtX//ft1yyy16+umndcstt2jIkBPZKS0tTb/4xS90xRVXBLVYAACAsxVQ2Nm4caPuueceLViwQE6n86RzLrnkEm3atOmcigMAADhXAYWdAwcOnHFOVFSU5s+fH8jhAQAAgiage3ZKS0v13HPP9Rt/7rnnVF5efs5FAQAABEtAYWf16tVKTk7uNz58+HAVFhaec1EAAADBElDYaWxsVFpaWr/xUaNG6dChQ+dcFAAAQLAEFHaGDx+ut956q9/4m2++qaSkpHMuCgAAIFgCCjvf/va39f3vf1+vvPKKenp61NPTo5dfflkPPvigvv3tbwe7RgAAgIAF9G6sVatWqbGxUdOmTVNk5IlD9Pb2at68edyzAwAAwkpAYScqKkq/+c1v9Mgjj+jNN99UdHS0MjMzNWrUqGDXBwAAcE4CCjt9xowZozFjxgSrFgAAgKALKOz09PSorKxMf/7zn9Xa2qre3l6//S+//HJQigMAADhXAYWdBx98UGVlZbr11luVkZEhm80W7LoAAACCIqCwU1lZqWeffVa33HJLsOsBAAAIqoDeeh4VFaXRo0cHuxYAAICgCyjsFBQU6PHHH5dhGEErpKioSDabTXl5eb4xwzDkdrvlcrkUHR2tKVOmqKGhwe95Ho9HS5YsUXJysmJjYzVr1iw1NTUFrS4AADC4BfQy1s6dO/XKK6/oxRdf1FVXXSW73e63f8uWLWd1vF27dumpp57S2LFj/cbXrFmj4uJilZWVacyYMVq1apWys7O1f/9+xcXFSZLy8vL0+9//XpWVlUpKSlJBQYFyc3NVV1eniIiIQC4PAABYSEArOxdeeKHuuOMOTZ48WcnJyUpISPB7nI0jR47ozjvv1C9/+UsNGzbMN24YhtavX68VK1Zo9uzZysjIUHl5uTo7O7V582ZJ0uHDh7Vp0yatXbtW06dP17hx41RRUaF9+/Zp+/btgVwaAACwmIBWdkpLS4NWwP33369bb71V06dP16pVq3zjBw8eVEtLi3JycnxjDodDkydPVm1trRYtWqS6ujp5vV6/OS6XSxkZGaqtrdWMGTNOek6PxyOPx+Pbbm9vlyR5vV55vd6gXVvfsRxDgvdy3/kSzD6Ypa/GwVDrYER/zUV/zUV/zRcOPR7ouQP+UMHjx49rx44devfddzV37lzFxcXpww8/VHx8vC644IIBHaOyslK7d+/Wrl27+u1raWmRJKWkpPiNp6SkqLGx0TcnKirKb0Wob07f80+mqKhIK1eu7DdeVVWlmJiYAdV+Nh4Z33vmSWFm27ZtoS5hwKqrq0NdgqXRX3PRX3PRX/OFssednZ0DmhdQ2GlsbNTNN9+sQ4cOyePxKDs7W3FxcVqzZo2OHTumJ5988ozHeP/99/Xggw+qqqpKQ4cOPeW8z3+Gj2EYZ/xcnzPNWb58ufLz833b7e3tSk1NVU5OjuLj489Y+0B5vV5VV1frJ28Mkad3cH0WUb375Kti4aSvv9nZ2f3uG8O5o7/mor/mor/mC4ce970ycyYBf6jg+PHj9eabbyopKck3fscdd+jee+8d0DHq6urU2tqqrKws31hPT49effVVbdiwQfv375d0YvVmxIgRvjmtra2+1R6n06nu7m61tbX5re60trZq0qRJpzy3w+GQw+HoN2632035gXl6bfL0DK6wM5j+OJj1c8MJ9Ndc9Ndc9Nd8oezxQM8b0A3KO3fu1I9//GNFRUX5jY8aNUoffPDBgI4xbdo07du3T3v37vU9xo8frzvvvFN79+7Vl770JTmdTr/lse7ubtXU1PiCTFZWlux2u9+c5uZm1dfXnzbsAACAL46AVnZ6e3vV09PTb7ypqcn3lvAziYuLU0ZGht9YbGyskpKSfON5eXkqLCxUenq60tPTVVhYqJiYGM2dO1eSlJCQoIULF6qgoEBJSUlKTEzU0qVLlZmZqenTpwdyaQAAwGICCjvZ2dlav369nnrqKUkn7qs5cuSIHn744aB+hcSyZcvU1dWlxYsXq62tTRMmTFBVVZVfoFq3bp0iIyM1Z84cdXV1adq0aSorK+MzdgAAgKQAw866des0depUXXnllTp27Jjmzp2rAwcOKDk5Wb/+9a8DLmbHjh1+2zabTW63W263+5TPGTp0qEpKSlRSUhLweQEAgHUFFHZcLpf27t2rX//619q9e7d6e3u1cOFC3XnnnYqOjg52jQAAAAEL+HN2oqOjdc899+iee+4JZj0AAABBFVDYefrpp0+7f968eQEVAwAAEGwBf87Of/J6vers7FRUVJRiYmIIOwAAIGwE9Dk7bW1tfo8jR45o//79uvHGG8/pBmUAAIBgCyjsnEx6erpWr17db9UHAAAglIIWdiQpIiJCH374YTAPCQAAcE4CumfnhRde8Ns2DEPNzc3asGGDbrjhhqAUBgAAEAwBhZ3bb7/db9tms+miiy7S1772Na1duzYYdQEAAARFwN+NBQAAMBgE9Z4dAACAcBPQyk5+fv6A5xYXFwdyCgAAgKAIKOzs2bNHu3fv1vHjx3X55ZdLkv7xj38oIiJC1157rW+ezWYLTpUAAAABCijs3HbbbYqLi1N5ebmGDRsm6cQHDS5YsEBf/epXVVBQENQiAQAAAhXQPTtr165VUVGRL+hI0rBhw7Rq1SrejQUAAMJKQGGnvb1dH330Ub/x1tZWdXR0nHNRAAAAwRJQ2Lnjjju0YMEC/fa3v1VTU5Oampr029/+VgsXLtTs2bODXSMAAEDAArpn58knn9TSpUt11113yev1njhQZKQWLlyoxx57LKgFAgAAnIuAwk5MTIx+/vOf67HHHtO7774rwzA0evRoxcbGBrs+AACAc3JOHyrY3Nys5uZmjRkzRrGxsTIMI1h1AQAABEVAYeeTTz7RtGnTNGbMGN1yyy1qbm6WJN1777287RwAAISVgMLOD37wA9ntdh06dEgxMTG+8W9961t66aWXglYcAADAuQronp2qqir96U9/0siRI/3G09PT1djYGJTCAAAAgiGglZ2jR4/6rej0+fjjj+VwOM65KAAAgGAJKOzcdNNNevrpp33bNptNvb29euyxxzR16tSgFQcAAHCuAnoZ67HHHtOUKVP0xhtvqLu7W8uWLVNDQ4M+/fRT/fWvfw12jQAAAAELaGXnyiuv1FtvvaXrrrtO2dnZOnr0qGbPnq09e/bosssuC3aNAAAAATvrlR2v16ucnBz94he/0MqVK82oCQAAIGjOemXHbrervr5eNpvNjHoAAACCKqCXsebNm6dNmzYFuxYAAICgC+gG5e7ubv3qV79SdXW1xo8f3+87sYqLi4NSHAAAwLk6q7Dz3nvv6dJLL1V9fb2uvfZaSdI//vEPvzm8vAUAAMLJWYWd9PR0NTc365VXXpF04ushnnjiCaWkpJhSHAAAwLk6q3t2Pv+t5i+++KKOHj0a1IIAAACCKaAblPt8PvwAAACEm7MKOzabrd89OdyjAwAAwtlZ3bNjGIbuvvtu35d9Hjt2TPfdd1+/d2Nt2bIleBUCAACcg7Na2Zk/f76GDx+uhIQEJSQk6K677pLL5fJt9z0GauPGjRo7dqzi4+MVHx+viRMn6sUXX/TtNwxDbrdbLpdL0dHRmjJlihoaGvyO4fF4tGTJEiUnJys2NlazZs1SU1PT2VwWAACwsLNa2SktLQ3qyUeOHKnVq1dr9OjRkqTy8nJ9/etf1549e3TVVVdpzZo1Ki4uVllZmcaMGaNVq1YpOztb+/fvV1xcnCQpLy9Pv//971VZWamkpCQVFBQoNzdXdXV1ioiICGq9AABg8DmnG5TP1W233aZbbrlFY8aM0ZgxY/Q///M/uuCCC/Taa6/JMAytX79eK1as0OzZs5WRkaHy8nJ1dnZq8+bNkqTDhw9r06ZNWrt2raZPn65x48apoqJC+/bt0/bt20N5aQAAIEwE9AnKZujp6dFzzz2no0ePauLEiTp48KBaWlqUk5Pjm+NwODR58mTV1tZq0aJFqqur830xaR+Xy6WMjAzV1tZqxowZJz2Xx+ORx+Pxbbe3t0s68SWnXq83aNfUdyzHkMH3rrVg9sEsfTUOhloHI/prLvprLvprvnDo8UDPHfKws2/fPk2cOFHHjh3TBRdcoK1bt+rKK69UbW2tJPX7wMKUlBQ1NjZKklpaWhQVFaVhw4b1m9PS0nLKcxYVFZ30G9urqqoUExNzrpfUzyPje4N+TLNt27Yt1CUMWHV1dahLsDT6ay76ay76a75Q9rizs3NA80Iedi6//HLt3btXn332mZ5//nnNnz9fNTU1vv2ff2u7YRhnfLv7meYsX75c+fn5vu329nalpqYqJydH8fHxAV5Jf16vV9XV1frJG0Pk6R1cb9Gvd598VSyc9PU3Oztbdrs91OVYDv01F/01F/01Xzj0uO+VmTMJediJiory3aA8fvx47dq1S48//rh++MMfSjqxejNixAjf/NbWVt9qj9PpVHd3t9ra2vxWd1pbWzVp0qRTntPhcPjePv+f7Ha7KT8wT69Nnp7BFXYG0x8Hs35uOIH+mov+mov+mi+UPR7oeUN6g/LJGIYhj8ejtLQ0OZ1Ov+Wx7u5u1dTU+IJMVlaW7Ha735zm5mbV19efNuwAAIAvjpCu7Dz00EOaOXOmUlNT1dHRocrKSu3YsUMvvfSSbDab8vLyVFhYqPT0dKWnp6uwsFAxMTGaO3euJCkhIUELFy5UQUGBkpKSlJiYqKVLlyozM1PTp08P5aUBAIAwEdKw89FHH+m73/2umpublZCQoLFjx+qll15Sdna2JGnZsmXq6urS4sWL1dbWpgkTJqiqqsr3GTuStG7dOkVGRmrOnDnq6urStGnTVFZWxmfsAAAASSEOO5s2bTrtfpvNJrfbLbfbfco5Q4cOVUlJiUpKSoJcHQAAsIKwu2cHAAAgmAg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0kIadoqKivSVr3xFcXFxGj58uG6//Xbt37/fb45hGHK73XK5XIqOjtaUKVPU0NDgN8fj8WjJkiVKTk5WbGysZs2apaampvN5KQAAIEyFNOzU1NTo/vvv12uvvabq6modP35cOTk5Onr0qG/OmjVrVFxcrA0bNmjXrl1yOp3Kzs5WR0eHb05eXp62bt2qyspK7dy5U0eOHFFubq56enpCcVkAACCMRIby5C+99JLfdmlpqYYPH666ujrddNNNMgxD69ev14oVKzR79mxJUnl5uVJSUrR582YtWrRIhw8f1qZNm/TMM89o+vTpkqSKigqlpqZq+/btmjFjxnm/LgAAED7C6p6dw4cPS5ISExMlSQcPHlRLS4tycnJ8cxwOhyZPnqza2lpJUl1dnbxer98cl8uljIwM3xwAAPDFFdKVnf9kGIby8/N14403KiMjQ5LU0tIiSUpJSfGbm5KSosbGRt+cqKgoDRs2rN+cvud/nsfjkcfj8W23t7dLkrxer7xeb3Au6P8fT5IcQ4ygHfN8CWYfzNJX42CodTCiv+aiv+aiv+YLhx4P9NxhE3YeeOABvfXWW9q5c2e/fTabzW/bMIx+Y593ujlFRUVauXJlv/GqqirFxMScRdUD88j43qAf02zbtm0LdQkDVl1dHeoSLI3+mov+mov+mi+UPe7s7BzQvLAIO0uWLNELL7ygV199VSNHjvSNO51OSSdWb0aMGOEbb21t9a32OJ1OdXd3q62tzW91p7W1VZMmTTrp+ZYvX678/Hzfdnt7u1JTU5WTk6P4+PigXZfX61V1dbV+8sYQeXpPH87CTb07/O916utvdna27HZ7qMuxHPprLvprLvprvnDocd8rM2cS0rBjGIaWLFmirVu3aseOHUpLS/Pbn5aWJqfTqerqao0bN06S1N3drZqaGj366KOSpKysLNntdlVXV2vOnDmSpObmZtXX12vNmjUnPa/D4ZDD4eg3brfbTfmBeXpt8vQMrrAzmP44mPVzwwn011z011z013yh7PFAzxvSsHP//fdr8+bN+r//+z/FxcX57rFJSEhQdHS0bDab8vLyVFhYqPT0dKWnp6uwsFAxMTGaO3eub+7ChQtVUFCgpKQkJSYmaunSpcrMzPS9OwsAAHxxhTTsbNy4UZI0ZcoUv/HS0lLdfffdkqRly5apq6tLixcvVltbmyZMmKCqqirFxcX55q9bt06RkZGaM2eOurq6NG3aNJWVlSkiIuJ8XQoAAAhTIX8Z60xsNpvcbrfcbvcp5wwdOlQlJSUqKSkJYnUAAMAKwupzdgAAAIKNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACwtpGHn1Vdf1W233SaXyyWbzabf/e53fvsNw5Db7ZbL5VJ0dLSmTJmihoYGvzkej0dLlixRcnKyYmNjNWvWLDU1NZ3HqwAAAOEspGHn6NGjuvrqq7Vhw4aT7l+zZo2Ki4u1YcMG7dq1S06nU9nZ2ero6PDNycvL09atW1VZWamdO3fqyJEjys3NVU9Pz/m6DAAAEMYiQ3nymTNnaubMmSfdZxiG1q9frxUrVmj27NmSpPLycqWkpGjz5s1atGiRDh8+rE2bNumZZ57R9OnTJUkVFRVKTU3V9u3bNWPGjPN2LVZz6Y/+GOoSzsgRYWjNdVKG+0/y9Nj0z9W3hrokAEAYCtt7dg4ePKiWlhbl5OT4xhwOhyZPnqza2lpJUl1dnbxer98cl8uljIwM3xwAAPDFFtKVndNpaWmRJKWkpPiNp6SkqLGx0TcnKipKw4YN6zen7/kn4/F45PF4fNvt7e2SJK/XK6/XG5T6+44nSY4hRtCOiX/r62vf/w3mzw7/7id9NQf9NRf9NV849Hig5w7bsNPHZrP5bRuG0W/s8840p6ioSCtXruw3XlVVpZiYmMAKPY1HxvcG/Zj4t77+btu2LcSVWFN1dXWoS7A0+msu+mu+UPa4s7NzQPPCNuw4nU5JJ1ZvRowY4RtvbW31rfY4nU51d3erra3Nb3WntbVVkyZNOuWxly9frvz8fN92e3u7UlNTlZOTo/j4+KBdg9frVXV1tX7yxhB5ek8f0HD2HEMMPTK+19ffejf3aAVT3+9vdna27HZ7qMuxHPprLvprvnDocd8rM2cStmEnLS1NTqdT1dXVGjdunCSpu7tbNTU1evTRRyVJWVlZstvtqq6u1pw5cyRJzc3Nqq+v15o1a055bIfDIYfD0W/cbreb8gPz9Nrk6SHsmKWvv/xBM4dZ/y5wAv01F/01Xyh7PNDzhjTsHDlyRO+8845v++DBg9q7d68SExN1ySWXKC8vT4WFhUpPT1d6eroKCwsVExOjuXPnSpISEhK0cOFCFRQUKCkpSYmJiVq6dKkyMzN9784CAABfbCENO2+88YamTp3q2+57aWn+/PkqKyvTsmXL1NXVpcWLF6utrU0TJkxQVVWV4uLifM9Zt26dIiMjNWfOHHV1dWnatGkqKytTRETEeb8eAAAQfkIadqZMmSLDOPU7lWw2m9xut9xu9ynnDB06VCUlJSopKTGhQgAAMNiF7efsAAAABANhBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWFpkqAsAguXSH/0x1CWctX+uvjXUJQCA5bGyAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI0PFQRwVvjwRgCDjWVWdn7+858rLS1NQ4cOVVZWlv7yl7+EuiQAABAGLBF2fvOb3ygvL08rVqzQnj179NWvflUzZ87UoUOHQl0aAAAIMUu8jFVcXKyFCxfq3nvvlSStX79ef/rTn7Rx40YVFRWFuDoAoRaOL705IgytuU7KcP9Jnh5bv/289AYEz6APO93d3aqrq9OPfvQjv/GcnBzV1taGqCoAAMwRLuH9TIH9P4U6vA/6sPPxxx+rp6dHKSkpfuMpKSlqaWk56XM8Ho88Ho9v+/Dhw5KkTz/9VF6vN2i1eb1edXZ2KtI7RD29p/9FwNmL7DXU2dk7qPs7eumzoS7hlBxDDP14XK+uWbFFnv/o76D/oxEmzvT7+8knn4SgKuvo+/v7ySefyG63h7qcoIo8fjTUJUg6u7/BZv0+d3R0SJIMwzjtPMv83bLZ/BttGEa/sT5FRUVauXJlv/G0tDRTaoN55oa6AIujv+Y6XX+T1563MoCADfRvhNm/zx0dHUpISDjl/kEfdpKTkxUREdFvFae1tbXfak+f5cuXKz8/37fd29urTz/9VElJSacMSIFob29Xamqq3n//fcXHxwftuDiB/pqL/pqL/pqL/povHHpsGIY6OjrkcrlOO2/Qh52oqChlZWWpurpad9xxh2+8urpaX//610/6HIfDIYfD4Td24YUXmlZjfHw8/9hMRH/NRX/NRX/NRX/NF+oen25Fp8+gDzuSlJ+fr+9+97saP368Jk6cqKeeekqHDh3SfffdF+rSAABAiFki7HzrW9/SJ598op/+9Kdqbm5WRkaGtm3bplGjRoW6NAAAEGKWCDuStHjxYi1evDjUZfhxOBx6+OGH+71khuCgv+aiv+aiv+aiv+YbTD22GWd6vxYAAMAgZomviwAAADgVwg4AALA0wg4AALA0wg4AALA0wo6Jfv7znystLU1Dhw5VVlaW/vKXv4S6pEHh1Vdf1W233SaXyyWbzabf/e53fvsNw5Db7ZbL5VJ0dLSmTJmihoYGvzkej0dLlixRcnKyYmNjNWvWLDU1NZ3HqwhPRUVF+spXvqK4uDgNHz5ct99+u/bv3+83h/4GbuPGjRo7dqzvQ9YmTpyoF1980bef3gZXUVGRbDab8vLyfGP0OHBut1s2m83v4XQ6ffsHdW8NmKKystKw2+3GL3/5S+Ptt982HnzwQSM2NtZobGwMdWlhb9u2bcaKFSuM559/3pBkbN261W//6tWrjbi4OOP555839u3bZ3zrW98yRowYYbS3t/vm3HfffcbFF19sVFdXG7t37zamTp1qXH311cbx48fP89WElxkzZhilpaVGfX29sXfvXuPWW281LrnkEuPIkSO+OfQ3cC+88ILxxz/+0di/f7+xf/9+46GHHjLsdrtRX19vGAa9DabXX3/duPTSS42xY8caDz74oG+cHgfu4YcfNq666iqjubnZ92htbfXtH8y9JeyY5LrrrjPuu+8+v7ErrrjC+NGPfhSiiganz4ed3t5ew+l0GqtXr/aNHTt2zEhISDCefPJJwzAM47PPPjPsdrtRWVnpm/PBBx8YQ4YMMV566aXzVvtg0NraakgyampqDMOgv2YYNmyY8atf/YreBlFHR4eRnp5uVFdXG5MnT/aFHXp8bh5++GHj6quvPum+wd5bXsYyQXd3t+rq6pSTk+M3npOTo9ra2hBVZQ0HDx5US0uLX28dDocmT57s621dXZ28Xq/fHJfLpYyMDPr/OYcPH5YkJSYmSqK/wdTT06PKykodPXpUEydOpLdBdP/99+vWW2/V9OnT/cbp8bk7cOCAXC6X0tLS9O1vf1vvvfeepMHfW8t8gnI4+fjjj9XT09PvW9dTUlL6fTs7zk5f/07W28bGRt+cqKgoDRs2rN8c+v9vhmEoPz9fN954ozIyMiTR32DYt2+fJk6cqGPHjumCCy7Q1q1bdeWVV/r+2NPbc1NZWandu3dr165d/fbx+3tuJkyYoKefflpjxozRRx99pFWrVmnSpElqaGgY9L0l7JjIZrP5bRuG0W8MgQmkt/Tf3wMPPKC33npLO3fu7LeP/gbu8ssv1969e/XZZ5/p+eef1/z581VTU+PbT28D9/777+vBBx9UVVWVhg4desp59DgwM2fO9P13ZmamJk6cqMsuu0zl5eW6/vrrJQ3e3vIylgmSk5MVERHRL8m2trb2S8U4O33vDDhdb51Op7q7u9XW1nbKOV90S5Ys0QsvvKBXXnlFI0eO9I3T33MXFRWl0aNHa/z48SoqKtLVV1+txx9/nN4GQV1dnVpbW5WVlaXIyEhFRkaqpqZGTzzxhCIjI309osfBERsbq8zMTB04cGDQ//4SdkwQFRWlrKwsVVdX+41XV1dr0qRJIarKGtLS0uR0Ov16293drZqaGl9vs7KyZLfb/eY0Nzervr7+C99/wzD0wAMPaMuWLXr55ZeVlpbmt5/+Bp9hGPJ4PPQ2CKZNm6Z9+/Zp7969vsf48eN15513au/evfrSl75Ej4PI4/Ho73//u0aMGDH4f39DcVf0F0HfW883bdpkvP3220ZeXp4RGxtr/POf/wx1aWGvo6PD2LNnj7Fnzx5DklFcXGzs2bPH97b91atXGwkJCcaWLVuMffv2Gd/5zndO+vbHkSNHGtu3bzd2795tfO1rXwuLtz+G2ve+9z0jISHB2LFjh9/bSzs7O31z6G/gli9fbrz66qvGwYMHjbfeest46KGHjCFDhhhVVVWGYdBbM/znu7EMgx6fi4KCAmPHjh3Ge++9Z7z22mtGbm6uERcX5/vfrcHcW8KOiX72s58Zo0aNMqKiooxrr73W9/ZenN4rr7xiSOr3mD9/vmEYJ94C+fDDDxtOp9NwOBzGTTfdZOzbt8/vGF1dXcYDDzxgJCYmGtHR0UZubq5x6NChEFxNeDlZXyUZpaWlvjn0N3D33HOP79/8RRddZEybNs0XdAyD3prh82GHHgeu73Nz7Ha74XK5jNmzZxsNDQ2+/YO5tzbDMIzQrCkBAACYj3t2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AAwqd999t2w2W7/HO++8E+rSAISpyFAXAABn6+abb1Zpaanf2EUXXXRWx+jp6ZHNZtOQIfz/fIDV8a8cwKDjcDjkdDr9Ho8//rgyMzMVGxur1NRULV68WEeOHPE9p6ysTBdeeKH+8Ic/6Morr5TD4VBjY6O6u7u1bNkyXXzxxYqNjdWECRO0Y8eO0F0cgKAj7ACwhCFDhuiJJ55QfX29ysvL9fLLL2vZsmV+czo7O1VUVKRf/epXamho0PDhw7VgwQL99a9/VWVlpd566y1985vf1M0336wDBw6E6EoABBtfBApgULn77rtVUVGhoUOH+sZmzpyp5557zm/ec889p+9973v6+OOPJZ1Y2VmwYIH27t2rq6++WpL07rvvKj09XU1NTXK5XL7nTp8+Xdddd50KCwvPwxUBMBv37AAYdKZOnaqNGzf6tmNjY/XKK6+osLBQb7/9ttrb23X8+HEdO3ZMR48eVWxsrCQpKipKY8eO9T1v9+7dMgxDY8aM8Tu+x+NRUlLS+bkYAKYj7AAYdGJjYzV69GjfdmNjo2655Rbdd999euSRR5SYmKidO3dq4cKF8nq9vnnR0dGy2Wy+7d7eXkVERKiurk4RERF+57jgggvMvxAA5wVhB8Cg98Ybb+j48eNau3at791Vzz777BmfN27cOPX09Ki1tVVf/epXzS4TQIhwgzKAQe+yyy7T8ePHVVJSovfee0/PPPOMnnzyyTM+b8yYMbrzzjs1b948bdmyRQcPHtSuXbv06KOPatu2beehcgDnA2EHwKB3zTXXqLi4WI8++qgyMjL0v//7vyoqKhrQc0tLSzVv3jwVFBTo8ssv16xZs/S3v/1NqampJlcN4Hzh3VgAAMDSWNkBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACW9v8A3Tw8of1EePAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Fare\"].hist()\n",
    "plt.xlabel(\"Fare\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Fare\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "fd4d889a-6348-4b67-a53f-9bf974bfe78a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Pclass\"].value_counts().plot(\n",
    "    kind=\"bar\",\n",
    "    xlabel=\"Pclass\",\n",
    "    ylabel=\"Frequency\",\n",
    "    title=\"Pclass\"\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcf08b8e-7d15-4f89-80c7-a14a1cbf9a0e",
   "metadata": {},
   "source": [
    "Its time to understand categorical variables. Starting with gender, we can see that there were more male than female passengers. Quite as many passengers embarked from Southampton than Cherbough and Queenstown combined and if we group by the rate of survival, we can determine more detailed information such as the proportion of each gender that survived from each location and their ages. \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4ed392f2-b391-4d9a-8819-a0e7dd0dda4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Distribution by Gender'}, xlabel='Sex', ylabel='Frequency'>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Sex\"].value_counts().plot(kind=\"bar\",\n",
    "                                           xlabel=\"Sex\",\n",
    "                                            ylabel=\"Frequency\",\n",
    "                                            title=\"Distribution by Gender\"\n",
    "                                            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "5cc5a84d-bc44-421e-8d25-9d6d54753475",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Embarked\"].value_counts().plot(\n",
    "    kind=\"bar\",\n",
    "    xlabel=\"Embarked\",\n",
    "    ylabel=\"Frequency\",\n",
    "    title=\"Embarked\"\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "e102ae31-4d1d-4251-81d3-9ec4eb9f6c9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Survived\"].value_counts().plot(\n",
    "    kind=\"bar\",\n",
    "    xlabel=\"Survive\",\n",
    "    ylabel=\"Frequency\",\n",
    "    title=\"Survival Distribution\"\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "beefd65b-9458-48d8-b34f-69f576b7b56b",
   "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></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">C</th>\n",
       "      <th>female</th>\n",
       "      <td>64</td>\n",
       "      <td>5487.3958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>29</td>\n",
       "      <td>4584.9004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Q</th>\n",
       "      <th>female</th>\n",
       "      <td>27</td>\n",
       "      <td>454.8585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>3</td>\n",
       "      <td>567.3958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">S</th>\n",
       "      <th>female</th>\n",
       "      <td>140</td>\n",
       "      <td>7864.4085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>77</td>\n",
       "      <td>9574.9903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Survived       Fare\n",
       "Embarked Sex                        \n",
       "C        female        64  5487.3958\n",
       "         male          29  4584.9004\n",
       "Q        female        27   454.8585\n",
       "         male           3   567.3958\n",
       "S        female       140  7864.4085\n",
       "         male          77  9574.9903"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"Embarked\", \"Sex\"])[[\"Survived\", \"Fare\"]].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "d6fcb157-3b01-43e4-a2be-bdf6358ae410",
   "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></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>female</th>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>female</th>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>female</th>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>347</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Survived  Fare\n",
       "Pclass Sex                   \n",
       "1      female        94    94\n",
       "       male         122   122\n",
       "2      female        76    76\n",
       "       male         108   108\n",
       "3      female       144   144\n",
       "       male         347   347"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"Pclass\", \"Sex\"])[[\"Survived\", \"Fare\"]].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9d25df8-8b24-4e3e-8b84-16e325d86f15",
   "metadata": {},
   "source": [
    "Diving in further, we can use a pivot table to explore the relationship between the likelihood of survival for different ages, family type and fare.\n",
    "\n",
    "Shows that younger were more likely to survive than older folks. Also, those who paid more for the cruise were more likely to survive, that is, first class tickets\n",
    "                                                                                                                                     \n",
    "Those with parents were also likely to survive however, if you had a sibling, data shows low chance of survival\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "91df65c2-ad1d-4bed-85d9-569b4b1aec73",
   "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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>SibSp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.626179</td>\n",
       "      <td>22.117887</td>\n",
       "      <td>0.329690</td>\n",
       "      <td>0.553734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.343690</td>\n",
       "      <td>48.395408</td>\n",
       "      <td>0.464912</td>\n",
       "      <td>0.473684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age       Fare     Parch     SibSp\n",
       "Survived                                          \n",
       "0         30.626179  22.117887  0.329690  0.553734\n",
       "1         28.343690  48.395408  0.464912  0.473684"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, index= \"Survived\", values = [\"Age\", \"SibSp\", \"Parch\", \"Fare\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aaf536f-4aed-4ca8-ab54-062f12b5017a",
   "metadata": {},
   "source": [
    "Probing further, it is clear more of first-class ticket holders survived than second and third-class ticket holders. \n",
    "\n",
    "More males did not survive than females and more females survived than males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "991a95ce-f2ba-4be5-a9ba-a5c3592d2c86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>136</td>\n",
       "      <td>87</td>\n",
       "      <td>119</td>\n",
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       "Pclass      1   2    3\n",
       "Survived              \n",
       "0          80  97  372\n",
       "1         136  87  119"
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    "\n",
    "pd.pivot_table(df, index = \"Survived\", columns = \"Pclass\", values = \"Ticket\", aggfunc = \"count\")"
   ]
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  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "93b8c65a-1930-4ae2-851e-4952708c25b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Sex</th>\n",
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       "      <th>male</th>\n",
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       "Sex       female  male\n",
       "Survived              \n",
       "0             81   468\n",
       "1            233   109"
      ]
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   "source": [
    "\n",
    "pd.pivot_table(df, index = \"Survived\", columns = \"Sex\", values = \"Ticket\", aggfunc = \"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "326a5b2f-6178-40d6-a202-120444482cc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <td>1</td>\n",
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       "      <td>female</td>\n",
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       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0           0       3    male  22.0      1      0   7.2500        S\n",
       "1           1       1  female  38.0      1      0  71.2833        C\n",
       "2           1       3  female  26.0      0      0   7.9250        S\n",
       "3           1       1  female  35.0      1      0  53.1000        S\n",
       "4           0       3    male  35.0      0      0   8.0500        S\n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...\n",
       "886         0       2    male  27.0      0      0  13.0000        S\n",
       "887         1       1  female  19.0      0      0  30.0000        S\n",
       "888         0       3  female   NaN      1      2  23.4500        S\n",
       "889         1       1    male  26.0      0      0  30.0000        C\n",
       "890         0       3    male  32.0      0      0   7.7500        Q\n",
       "\n",
       "[891 rows x 8 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdd07b8f-04ec-4cdb-888d-89bd17e14cea",
   "metadata": {},
   "source": [
    "### Correlation Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e0261d0-4ff3-48ff-af5b-1cf5c87e898f",
   "metadata": {},
   "source": [
    "A correlation matrix shows the correlation coefficients of our variables and help us summarize the datasets and visualize the patterns or relationships.\n",
    "\n",
    "Interestingly, the correlation matrix shows the family size (SibSp and Parch) have some multicollinearity. This might imply either could be a candidate of omission in our analysis. However, since the correlation is less than 0.5, changes in one variable is unlikely to affect the changes in the other to the extent it affects the target variable. \n",
    "\n",
    "The second heatmap shows survival is positively correlated with fare and negatively correlated with passenger class. The passengers in 1st class were more likely to survive than those in second and third class. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "6fbb3996-4987-45d4-9901-8bcf42138b0f",
   "metadata": {},
   "outputs": [
    {
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       "<table id=\"T_38703\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_38703_level0_col0\" class=\"col_heading level0 col0\" >Pclass</th>\n",
       "      <th id=\"T_38703_level0_col1\" class=\"col_heading level0 col1\" >Age</th>\n",
       "      <th id=\"T_38703_level0_col2\" class=\"col_heading level0 col2\" >SibSp</th>\n",
       "      <th id=\"T_38703_level0_col3\" class=\"col_heading level0 col3\" >Parch</th>\n",
       "      <th id=\"T_38703_level0_col4\" class=\"col_heading level0 col4\" >Fare</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th id=\"T_38703_level0_row0\" class=\"row_heading level0 row0\" >Pclass</th>\n",
       "      <td id=\"T_38703_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_38703_row0_col1\" class=\"data row0 col1\" >-0.369226</td>\n",
       "      <td id=\"T_38703_row0_col2\" class=\"data row0 col2\" >0.083081</td>\n",
       "      <td id=\"T_38703_row0_col3\" class=\"data row0 col3\" >0.018443</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_38703_level0_row1\" class=\"row_heading level0 row1\" >Age</th>\n",
       "      <td id=\"T_38703_row1_col0\" class=\"data row1 col0\" >-0.369226</td>\n",
       "      <td id=\"T_38703_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_38703_row1_col2\" class=\"data row1 col2\" >-0.308247</td>\n",
       "      <td id=\"T_38703_row1_col3\" class=\"data row1 col3\" >-0.189119</td>\n",
       "      <td id=\"T_38703_row1_col4\" class=\"data row1 col4\" >0.096067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_38703_level0_row2\" class=\"row_heading level0 row2\" >SibSp</th>\n",
       "      <td id=\"T_38703_row2_col0\" class=\"data row2 col0\" >0.083081</td>\n",
       "      <td id=\"T_38703_row2_col1\" class=\"data row2 col1\" >-0.308247</td>\n",
       "      <td id=\"T_38703_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_38703_row2_col3\" class=\"data row2 col3\" >0.414838</td>\n",
       "      <td id=\"T_38703_row2_col4\" class=\"data row2 col4\" >0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_38703_level0_row3\" class=\"row_heading level0 row3\" >Parch</th>\n",
       "      <td id=\"T_38703_row3_col0\" class=\"data row3 col0\" >0.018443</td>\n",
       "      <td id=\"T_38703_row3_col1\" class=\"data row3 col1\" >-0.189119</td>\n",
       "      <td id=\"T_38703_row3_col2\" class=\"data row3 col2\" >0.414838</td>\n",
       "      <td id=\"T_38703_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_38703_row3_col4\" class=\"data row3 col4\" >0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_38703_level0_row4\" class=\"row_heading level0 row4\" >Fare</th>\n",
       "      <td id=\"T_38703_row4_col0\" class=\"data row4 col0\" >-0.549500</td>\n",
       "      <td id=\"T_38703_row4_col1\" class=\"data row4 col1\" >0.096067</td>\n",
       "      <td id=\"T_38703_row4_col2\" class=\"data row4 col2\" >0.159651</td>\n",
       "      <td id=\"T_38703_row4_col3\" class=\"data row4 col3\" >0.216225</td>\n",
       "      <td id=\"T_38703_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1ff00e094c0>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = df.select_dtypes(\"number\").drop(columns=\"Survived\").corr()\n",
    "corr.style.background_gradient(axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "88e6c40c-d944-4b08-a7cb-129a4c86a283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "d98bbcbc-e1a0-4c0c-8268-a2406efc506a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_f2996_row0_col0, #T_f2996_row1_col1, #T_f2996_row2_col2, #T_f2996_row3_col3, #T_f2996_row4_col4, #T_f2996_row5_col5 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
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       "#T_f2996_row0_col1, #T_f2996_row1_col0 {\n",
       "  background-color: #eae6f1;\n",
       "  color: #000000;\n",
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       "#T_f2996_row0_col2, #T_f2996_row2_col0 {\n",
       "  background-color: #bdc8e1;\n",
       "  color: #000000;\n",
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       "  background-color: #b5c4df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f2996_row0_col4, #T_f2996_row1_col3, #T_f2996_row3_col1, #T_f2996_row4_col0 {\n",
       "  background-color: #99b8d8;\n",
       "  color: #000000;\n",
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       "  background-color: #69a5cc;\n",
       "  color: #f1f1f1;\n",
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       "  background-color: #eee8f3;\n",
       "  color: #000000;\n",
       "}\n",
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       "  background-color: #a9bfdc;\n",
       "  color: #000000;\n",
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       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f2996_row2_col3, #T_f2996_row3_col2 {\n",
       "  background-color: #e6e2ef;\n",
       "  color: #000000;\n",
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       "#T_f2996_row2_col4, #T_f2996_row4_col2 {\n",
       "  background-color: #d4d4e8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f2996_row2_col5, #T_f2996_row5_col2 {\n",
       "  background-color: #96b6d7;\n",
       "  color: #000000;\n",
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       "#T_f2996_row3_col4, #T_f2996_row4_col3 {\n",
       "  background-color: #3790c0;\n",
       "  color: #f1f1f1;\n",
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       "#T_f2996_row3_col5, #T_f2996_row5_col3 {\n",
       "  background-color: #84b0d3;\n",
       "  color: #f1f1f1;\n",
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       "  background-color: #76aad0;\n",
       "  color: #f1f1f1;\n",
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       "</style>\n",
       "<table id=\"T_f2996\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_f2996_level0_col0\" class=\"col_heading level0 col0\" >Survived</th>\n",
       "      <th id=\"T_f2996_level0_col1\" class=\"col_heading level0 col1\" >Pclass</th>\n",
       "      <th id=\"T_f2996_level0_col2\" class=\"col_heading level0 col2\" >Age</th>\n",
       "      <th id=\"T_f2996_level0_col3\" class=\"col_heading level0 col3\" >SibSp</th>\n",
       "      <th id=\"T_f2996_level0_col4\" class=\"col_heading level0 col4\" >Parch</th>\n",
       "      <th id=\"T_f2996_level0_col5\" class=\"col_heading level0 col5\" >Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f2996_level0_row0\" class=\"row_heading level0 row0\" >Survived</th>\n",
       "      <td id=\"T_f2996_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_f2996_row0_col1\" class=\"data row0 col1\" >-0.338481</td>\n",
       "      <td id=\"T_f2996_row0_col2\" class=\"data row0 col2\" >-0.077221</td>\n",
       "      <td id=\"T_f2996_row0_col3\" class=\"data row0 col3\" >-0.035322</td>\n",
       "      <td id=\"T_f2996_row0_col4\" class=\"data row0 col4\" >0.081629</td>\n",
       "      <td id=\"T_f2996_row0_col5\" class=\"data row0 col5\" >0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f2996_level0_row1\" class=\"row_heading level0 row1\" >Pclass</th>\n",
       "      <td id=\"T_f2996_row1_col0\" class=\"data row1 col0\" >-0.338481</td>\n",
       "      <td id=\"T_f2996_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_f2996_row1_col2\" class=\"data row1 col2\" >-0.369226</td>\n",
       "      <td id=\"T_f2996_row1_col3\" class=\"data row1 col3\" >0.083081</td>\n",
       "      <td id=\"T_f2996_row1_col4\" class=\"data row1 col4\" >0.018443</td>\n",
       "      <td id=\"T_f2996_row1_col5\" class=\"data row1 col5\" >-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f2996_level0_row2\" class=\"row_heading level0 row2\" >Age</th>\n",
       "      <td id=\"T_f2996_row2_col0\" class=\"data row2 col0\" >-0.077221</td>\n",
       "      <td id=\"T_f2996_row2_col1\" class=\"data row2 col1\" >-0.369226</td>\n",
       "      <td id=\"T_f2996_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_f2996_row2_col3\" class=\"data row2 col3\" >-0.308247</td>\n",
       "      <td id=\"T_f2996_row2_col4\" class=\"data row2 col4\" >-0.189119</td>\n",
       "      <td id=\"T_f2996_row2_col5\" class=\"data row2 col5\" >0.096067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f2996_level0_row3\" class=\"row_heading level0 row3\" >SibSp</th>\n",
       "      <td id=\"T_f2996_row3_col0\" class=\"data row3 col0\" >-0.035322</td>\n",
       "      <td id=\"T_f2996_row3_col1\" class=\"data row3 col1\" >0.083081</td>\n",
       "      <td id=\"T_f2996_row3_col2\" class=\"data row3 col2\" >-0.308247</td>\n",
       "      <td id=\"T_f2996_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_f2996_row3_col4\" class=\"data row3 col4\" >0.414838</td>\n",
       "      <td id=\"T_f2996_row3_col5\" class=\"data row3 col5\" >0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f2996_level0_row4\" class=\"row_heading level0 row4\" >Parch</th>\n",
       "      <td id=\"T_f2996_row4_col0\" class=\"data row4 col0\" >0.081629</td>\n",
       "      <td id=\"T_f2996_row4_col1\" class=\"data row4 col1\" >0.018443</td>\n",
       "      <td id=\"T_f2996_row4_col2\" class=\"data row4 col2\" >-0.189119</td>\n",
       "      <td id=\"T_f2996_row4_col3\" class=\"data row4 col3\" >0.414838</td>\n",
       "      <td id=\"T_f2996_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_f2996_row4_col5\" class=\"data row4 col5\" >0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f2996_level0_row5\" class=\"row_heading level0 row5\" >Fare</th>\n",
       "      <td id=\"T_f2996_row5_col0\" class=\"data row5 col0\" >0.257307</td>\n",
       "      <td id=\"T_f2996_row5_col1\" class=\"data row5 col1\" >-0.549500</td>\n",
       "      <td id=\"T_f2996_row5_col2\" class=\"data row5 col2\" >0.096067</td>\n",
       "      <td id=\"T_f2996_row5_col3\" class=\"data row5 col3\" >0.159651</td>\n",
       "      <td id=\"T_f2996_row5_col4\" class=\"data row5 col4\" >0.216225</td>\n",
       "      <td id=\"T_f2996_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1ff030c95b0>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_survive = df.select_dtypes(\"number\").corr()\n",
    "corr_survive.style.background_gradient(axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "173b8c5b-ed68-4188-98eb-81209cbe00f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr_survive)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c627d7f0-2dd8-43fa-92de-50338d8b4b68",
   "metadata": {},
   "source": [
    "A boxplot shows the distribution of the survivers relative to the fare paid. There is considerable differences betweeen the fares paid by those who survived and thosed who did not\n",
    "with the latter being lower. It is understandable given the common relation between the value of the ticket one possess and comfort, and by extension death in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "46a6c476-29dc-4c65-9434-f97510ebb892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Survived', ylabel='Fare'>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Survived\", y=\"Fare\", data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73c3bb0e-d216-47c5-9df5-db75159ba3d0",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98d79648-70c5-45d8-ae57-241fd7d3b270",
   "metadata": {},
   "source": [
    "### Divide"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17708216-59a6-4c50-9a0e-08b2fcb5c927",
   "metadata": {},
   "source": [
    "The next step is to divide the data into target and features and training and testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "08e2d8c6-a972-424c-bad1-8ee320cd2e7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape (891, 7)\n",
      "Y shape (891,)\n"
     ]
    }
   ],
   "source": [
    "target = \"Survived\"\n",
    "X = df.drop(columns=target)\n",
    "y = df[target]\n",
    "print(\"X shape\", X.shape)\n",
    "print(\"Y shape\", y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc0e8611-2856-4ed0-bd22-a523a99b7a31",
   "metadata": {},
   "source": [
    "Training and Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "3684eb83-da96-4917-9259-6558642722a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train (712, 7)\n",
      "y_train (712,)\n",
      "X_test (179, 7)\n",
      "y_test (179,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")\n",
    "print(\"X_train\", X_train.shape)\n",
    "print(\"y_train\", y_train.shape)\n",
    "print(\"X_test\", X_test.shape)\n",
    "print(\"y_test\", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a3648a9-f107-488e-ac1f-9fbec9d0a2aa",
   "metadata": {},
   "source": [
    "Since we have missing data, we will use a transformer to impute the missing values before we put it into a predictor\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29afe5cd-8cb8-4698-bd15-888bc86678a1",
   "metadata": {},
   "source": [
    "### Transform and Encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "7c92fac1-b0bb-4025-883a-51b1ad929a89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 712 entries, 331 to 102\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Pclass    712 non-null    int64  \n",
      " 1   Sex       712 non-null    object \n",
      " 2   Age       572 non-null    float64\n",
      " 3   SibSp     712 non-null    int64  \n",
      " 4   Parch     712 non-null    int64  \n",
      " 5   Fare      712 non-null    float64\n",
      " 6   Embarked  710 non-null    object \n",
      "dtypes: float64(2), int64(3), object(2)\n",
      "memory usage: 44.5+ KB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0218d290-1bf0-45b7-bd3e-4ad75a475f89",
   "metadata": {},
   "source": [
    "But first, we have to convert categorical variables in Age and Embarked columns to numeric values using OneHotEncoder\n",
    "\n",
    "NB: Tranformers are not usually separated from predictors but we have done so for the sake of learning. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "ac59d420-778d-45b4-b565-8bcfbb845539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneHotEncoder(cols=[&#x27;Sex&#x27;, &#x27;Embarked&#x27;], use_cat_names=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(cols=[&#x27;Sex&#x27;, &#x27;Embarked&#x27;], use_cat_names=True)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "OneHotEncoder(cols=['Sex', 'Embarked'], use_cat_names=True)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe= OneHotEncoder(use_cat_names=True)\n",
    "ohe.fit(X_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "fe6e6a7b-609c-4723-ba67-c3e1d92c8c9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "XT_train = ohe.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "f16aa3ae-c4a1-43f4-b784-e01519d4cb70",
   "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",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_nan</th>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.6500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>14.1083</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>120.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>77.2875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>712 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex_male  Sex_female   Age  SibSp  Parch      Fare  Embarked_S  \\\n",
       "331       1         1           0  45.5      0      0   28.5000           1   \n",
       "733       2         1           0  23.0      0      0   13.0000           1   \n",
       "382       3         1           0  32.0      0      0    7.9250           1   \n",
       "704       3         1           0  26.0      1      0    7.8542           1   \n",
       "813       3         0           1   6.0      4      2   31.2750           1   \n",
       "..      ...       ...         ...   ...    ...    ...       ...         ...   \n",
       "106       3         0           1  21.0      0      0    7.6500           1   \n",
       "270       1         1           0   NaN      0      0   31.0000           1   \n",
       "860       3         1           0  41.0      2      0   14.1083           1   \n",
       "435       1         0           1  14.0      1      2  120.0000           1   \n",
       "102       1         1           0  21.0      0      1   77.2875           1   \n",
       "\n",
       "     Embarked_C  Embarked_Q  Embarked_nan  \n",
       "331           0           0             0  \n",
       "733           0           0             0  \n",
       "382           0           0             0  \n",
       "704           0           0             0  \n",
       "813           0           0             0  \n",
       "..          ...         ...           ...  \n",
       "106           0           0             0  \n",
       "270           0           0             0  \n",
       "860           0           0             0  \n",
       "435           0           0             0  \n",
       "102           0           0             0  \n",
       "\n",
       "[712 rows x 11 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XT_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13db37c3-8411-43cc-b7f2-e6a2c8cf6263",
   "metadata": {},
   "source": [
    "### Impute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "449c59e6-fc5c-4905-aa4f-11af28cffd76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SimpleImputer()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SimpleImputer()"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer = SimpleImputer()\n",
    "imputer.fit(XT_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01c0c1dd-78d3-499b-bba8-380f6db5a250",
   "metadata": {},
   "source": [
    "As shown below, there are no null values in our data set, hence we can now start the model building and testing phase\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "fc23fdbf-fcf7-4adf-906b-825f6047dbb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 712 entries, 0 to 711\n",
      "Data columns (total 11 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Pclass        712 non-null    float64\n",
      " 1   Sex_male      712 non-null    float64\n",
      " 2   Sex_female    712 non-null    float64\n",
      " 3   Age           712 non-null    float64\n",
      " 4   SibSp         712 non-null    float64\n",
      " 5   Parch         712 non-null    float64\n",
      " 6   Fare          712 non-null    float64\n",
      " 7   Embarked_S    712 non-null    float64\n",
      " 8   Embarked_C    712 non-null    float64\n",
      " 9   Embarked_Q    712 non-null    float64\n",
      " 10  Embarked_nan  712 non-null    float64\n",
      "dtypes: float64(11)\n",
      "memory usage: 61.3 KB\n"
     ]
    }
   ],
   "source": [
    "XTI_train = imputer.transform(XT_train)\n",
    "pd.DataFrame(XTI_train, columns = XT_train.columns).info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b785668b-b8ee-4df7-a8c1-421bac772fe6",
   "metadata": {},
   "source": [
    "The below is a pipeline with imputer, encoder, and decision tree classifier all put together. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6324ec8f-82ba-452b-90d8-718baa4a111d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = make_pipeline(\n",
    "    SimpleImputer(), OneHotEncoder(), DecisionTreeClassifier(random_state=42)\n",
    ")\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "654169d4-e5d1-4074-bb8e-56a2a8ffbc49",
   "metadata": {},
   "source": [
    "In the next section, we will train the model, evaluate, and make predictions. See you!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5d8d9ba-dc69-44d2-98e2-792a8f8354d6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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