{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a40c3060",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6ca0451b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_titanic = fetch_openml(\"titanic\", version = 1, as_frame = True)[\"data\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "108b699a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 13)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f68c698",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pclass', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket', 'fare',\n",
       "       'cabin', 'embarked', 'boat', 'body', 'home.dest'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "678f1623",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass                                             name     sex      age  \\\n",
       "0     1.0                    Allen, Miss. Elisabeth Walton  female  29.0000   \n",
       "1     1.0                   Allison, Master. Hudson Trevor    male   0.9167   \n",
       "2     1.0                     Allison, Miss. Helen Loraine  female   2.0000   \n",
       "3     1.0             Allison, Mr. Hudson Joshua Creighton    male  30.0000   \n",
       "4     1.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female  25.0000   \n",
       "\n",
       "   sibsp  parch  ticket      fare    cabin embarked  boat   body  \\\n",
       "0    0.0    0.0   24160  211.3375       B5        S     2    NaN   \n",
       "1    1.0    2.0  113781  151.5500  C22 C26        S    11    NaN   \n",
       "2    1.0    2.0  113781  151.5500  C22 C26        S  None    NaN   \n",
       "3    1.0    2.0  113781  151.5500  C22 C26        S  None  135.0   \n",
       "4    1.0    2.0  113781  151.5500  C22 C26        S  None    NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "1  Montreal, PQ / Chesterville, ON  \n",
       "2  Montreal, PQ / Chesterville, ON  \n",
       "3  Montreal, PQ / Chesterville, ON  \n",
       "4  Montreal, PQ / Chesterville, ON  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2f31b7e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Zabour, Miss. Hileni</td>\n",
       "      <td>female</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2665</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>None</td>\n",
       "      <td>C</td>\n",
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       "      <td>328.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Zabour, Miss. Thamine</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2665</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>None</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Zakarian, Mr. Mapriededer</td>\n",
       "      <td>male</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2656</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>None</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>304.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Zakarian, Mr. Ortin</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2670</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>None</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Zimmerman, Mr. Leo</td>\n",
       "      <td>male</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>315082</td>\n",
       "      <td>7.8750</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pclass                       name     sex   age  sibsp  parch  ticket  \\\n",
       "1304     3.0       Zabour, Miss. Hileni  female  14.5    1.0    0.0    2665   \n",
       "1305     3.0      Zabour, Miss. Thamine  female   NaN    1.0    0.0    2665   \n",
       "1306     3.0  Zakarian, Mr. Mapriededer    male  26.5    0.0    0.0    2656   \n",
       "1307     3.0        Zakarian, Mr. Ortin    male  27.0    0.0    0.0    2670   \n",
       "1308     3.0         Zimmerman, Mr. Leo    male  29.0    0.0    0.0  315082   \n",
       "\n",
       "         fare cabin embarked  boat   body home.dest  \n",
       "1304  14.4542  None        C  None  328.0      None  \n",
       "1305  14.4542  None        C  None    NaN      None  \n",
       "1306   7.2250  None        C  None  304.0      None  \n",
       "1307   7.2250  None        C  None    NaN      None  \n",
       "1308   7.8750  None        S  None    NaN      None  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c523519b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 13 columns):\n",
      " #   Column     Non-Null Count  Dtype   \n",
      "---  ------     --------------  -----   \n",
      " 0   pclass     1309 non-null   float64 \n",
      " 1   name       1309 non-null   object  \n",
      " 2   sex        1309 non-null   category\n",
      " 3   age        1046 non-null   float64 \n",
      " 4   sibsp      1309 non-null   float64 \n",
      " 5   parch      1309 non-null   float64 \n",
      " 6   ticket     1309 non-null   object  \n",
      " 7   fare       1308 non-null   float64 \n",
      " 8   cabin      295 non-null    object  \n",
      " 9   embarked   1307 non-null   category\n",
      " 10  boat       486 non-null    object  \n",
      " 11  body       121 non-null    float64 \n",
      " 12  home.dest  745 non-null    object  \n",
      "dtypes: category(2), float64(6), object(5)\n",
      "memory usage: 115.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ad1c6271",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>body</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1046.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1308.000000</td>\n",
       "      <td>121.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.294882</td>\n",
       "      <td>29.881135</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>33.295479</td>\n",
       "      <td>160.809917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.837836</td>\n",
       "      <td>14.413500</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>51.758668</td>\n",
       "      <td>97.696922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.166700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>155.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.275000</td>\n",
       "      <td>256.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>328.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pclass          age        sibsp        parch         fare  \\\n",
       "count  1309.000000  1046.000000  1309.000000  1309.000000  1308.000000   \n",
       "mean      2.294882    29.881135     0.498854     0.385027    33.295479   \n",
       "std       0.837836    14.413500     1.041658     0.865560    51.758668   \n",
       "min       1.000000     0.166700     0.000000     0.000000     0.000000   \n",
       "25%       2.000000    21.000000     0.000000     0.000000     7.895800   \n",
       "50%       3.000000    28.000000     0.000000     0.000000    14.454200   \n",
       "75%       3.000000    39.000000     1.000000     0.000000    31.275000   \n",
       "max       3.000000    80.000000     8.000000     9.000000   512.329200   \n",
       "\n",
       "             body  \n",
       "count  121.000000  \n",
       "mean   160.809917  \n",
       "std     97.696922  \n",
       "min      1.000000  \n",
       "25%     72.000000  \n",
       "50%    155.000000  \n",
       "75%    256.000000  \n",
       "max    328.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "61b8168d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "sns.heatmap(df_titanic.corr(), cmap = \"BuPu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a74e552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      843\n",
       "female    466\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic[\"sex\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e4b9b64c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2e9c47314f0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "plt.pie(df_titanic[\"sex\"].value_counts(), explode = [0, 0.05], labels = [\"Female\", \"Male\"], colors = [\"#FF82AB\", \"#8E388E\"])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f0e4d736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35.6"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round((sum(df_titanic[\"sex\"] == \"female\") / (df_titanic.shape[0]) * 100), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9187c02b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64.4"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round((sum(df_titanic[\"sex\"] == \"male\") / (df_titanic.shape[0]) * 100), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ccbfa540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(df_titanic[\"age\"].mean(), 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b68deced",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass          0\n",
       "name            0\n",
       "sex             0\n",
       "age           263\n",
       "sibsp           0\n",
       "parch           0\n",
       "ticket          0\n",
       "fare            1\n",
       "cabin        1014\n",
       "embarked        2\n",
       "boat          823\n",
       "body         1188\n",
       "home.dest     564\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "10093c65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Percentage of Missing Values'}, ylabel='Percentage'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "missing_values = pd.DataFrame(df_titanic.isnull().sum()/len(df_titanic) * 100)\n",
    "missing_values.plot(kind = \"bar\",\n",
    "                    title = \"Percentage of Missing Values\",\n",
    "                    ylabel = \"Percentage\",\n",
    "                    color = \"#00CD66\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7ee19288",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datatypes of Missing Values:\n",
      "age : nan\n",
      "fare : nan\n",
      "cabin : None\n",
      "embarked : nan\n",
      "boat : None\n",
      "body : nan\n",
      "home.dest : None\n"
     ]
    }
   ],
   "source": [
    "#Imputing Missing Values:\n",
    "print(\"Datatypes of Missing Values:\")\n",
    "for col in df_titanic.columns[df_titanic.isnull().any()]:\n",
    "    print(col, \":\", df_titanic[col][df_titanic[col].isnull()].values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "aee16690",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': {'missing_values': nan, 'strategy': 'mean'},\n",
       " 'fare': {'missing_values': nan, 'strategy': 'mean'},\n",
       " 'cabin': {'missing_values': None, 'strategy': 'most_frequent'},\n",
       " 'embarked': {'missing_values': nan, 'strategy': 'most_frequent'},\n",
       " 'boat': {'missing_values': None, 'strategy': 'most_frequent'},\n",
       " 'body': {'missing_values': nan, 'strategy': 'mean'},\n",
       " 'home.dest': {'missing_values': None, 'strategy': 'most_frequent'}}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_parameters(df_titanic):\n",
    "    parameters = {}\n",
    "    for col in df_titanic.columns[df_titanic.isnull().any()]:\n",
    "        if  df_titanic[col].dtype == \"float64\" or df_titanic[col].dtype == \"int64\" or df_titanic[col].dtype == \"int32\":\n",
    "            strategy = \"mean\"\n",
    "        else:\n",
    "            strategy = \"most_frequent\"\n",
    "        missing_values = df_titanic[col][df_titanic[col].isnull()].values[0]\n",
    "        parameters[col] = {\"missing_values\" : missing_values, \n",
    "                           \"strategy\" : strategy}\n",
    "    return parameters\n",
    "get_parameters(df_titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "70ce99ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = get_parameters(df_titanic)\n",
    "for col, param in parameters.items():\n",
    "    missing_values = param[\"missing_values\"]\n",
    "    strategy = param[\"strategy\"]\n",
    "    IMP = SimpleImputer(missing_values = missing_values, strategy = strategy)\n",
    "    df_titanic[col] = IMP.fit_transform(df_titanic[[col]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2312e508",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass       0\n",
       "name         0\n",
       "sex          0\n",
       "age          0\n",
       "sibsp        0\n",
       "parch        0\n",
       "ticket       0\n",
       "fare         0\n",
       "cabin        0\n",
       "embarked     0\n",
       "boat         0\n",
       "body         0\n",
       "home.dest    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fa570c0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Null Values (after) : 0\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of Null Values (after) : {df_titanic.age.isnull().sum()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0e24e195",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pclass', 'age', 'sibsp', 'parch', 'fare', 'body'], dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Normalizing the Data - MinMaxScaler:\n",
    "n_cols = df_titanic.select_dtypes(include = [\"int64\", \"float64\", \"float32\"]).columns\n",
    "n_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b5d7be2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in n_cols:\n",
    "    fill_value = df_titanic[col].mean()\n",
    "    df_titanic[col].fillna(fill_value, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6956e3bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>body</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.361169</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.412503</td>\n",
       "      <td>0.488715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009395</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.295806</td>\n",
       "      <td>0.488715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022964</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.295806</td>\n",
       "      <td>0.488715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.373695</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.295806</td>\n",
       "      <td>0.409786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.311064</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.295806</td>\n",
       "      <td>0.488715</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.179540</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028213</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.372206</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028213</td>\n",
       "      <td>0.488715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.329854</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014102</td>\n",
       "      <td>0.926606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.336117</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014102</td>\n",
       "      <td>0.488715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.361169</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015371</td>\n",
       "      <td>0.488715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pclass       age  sibsp     parch      fare      body\n",
       "0        0.0  0.361169  0.000  0.000000  0.412503  0.488715\n",
       "1        0.0  0.009395  0.125  0.222222  0.295806  0.488715\n",
       "2        0.0  0.022964  0.125  0.222222  0.295806  0.488715\n",
       "3        0.0  0.373695  0.125  0.222222  0.295806  0.409786\n",
       "4        0.0  0.311064  0.125  0.222222  0.295806  0.488715\n",
       "...      ...       ...    ...       ...       ...       ...\n",
       "1304     1.0  0.179540  0.125  0.000000  0.028213  1.000000\n",
       "1305     1.0  0.372206  0.125  0.000000  0.028213  0.488715\n",
       "1306     1.0  0.329854  0.000  0.000000  0.014102  0.926606\n",
       "1307     1.0  0.336117  0.000  0.000000  0.014102  0.488715\n",
       "1308     1.0  0.361169  0.000  0.000000  0.015371  0.488715\n",
       "\n",
       "[1309 rows x 6 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minmax = MinMaxScaler()\n",
    "df_titanic[n_cols] = minmax.fit_transform(df_titanic[n_cols])\n",
    "df_titanic[n_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "28746928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>body</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.309000e+03</td>\n",
       "      <td>1.309000e+03</td>\n",
       "      <td>1.309000e+03</td>\n",
       "      <td>1.309000e+03</td>\n",
       "      <td>1.309000e+03</td>\n",
       "      <td>1.309000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.000329e-15</td>\n",
       "      <td>1.832313e-16</td>\n",
       "      <td>-1.028801e-15</td>\n",
       "      <td>3.833620e-17</td>\n",
       "      <td>8.410215e-16</td>\n",
       "      <td>-2.912731e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000382e+00</td>\n",
       "      <td>1.000382e+00</td>\n",
       "      <td>1.000382e+00</td>\n",
       "      <td>1.000382e+00</td>\n",
       "      <td>1.000382e+00</td>\n",
       "      <td>1.000382e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.546098e+00</td>\n",
       "      <td>-2.307330e+00</td>\n",
       "      <td>-4.790868e-01</td>\n",
       "      <td>-4.449995e-01</td>\n",
       "      <td>-6.437751e-01</td>\n",
       "      <td>-5.402590e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-3.520907e-01</td>\n",
       "      <td>-6.119712e-01</td>\n",
       "      <td>-4.790868e-01</td>\n",
       "      <td>-4.449995e-01</td>\n",
       "      <td>-4.911082e-01</td>\n",
       "      <td>3.201135e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.419164e-01</td>\n",
       "      <td>1.302752e-16</td>\n",
       "      <td>-4.790868e-01</td>\n",
       "      <td>-4.449995e-01</td>\n",
       "      <td>-3.643001e-01</td>\n",
       "      <td>3.201135e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.419164e-01</td>\n",
       "      <td>3.974806e-01</td>\n",
       "      <td>4.812878e-01</td>\n",
       "      <td>-4.449995e-01</td>\n",
       "      <td>-3.906640e-02</td>\n",
       "      <td>3.201135e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.419164e-01</td>\n",
       "      <td>3.891737e+00</td>\n",
       "      <td>7.203909e+00</td>\n",
       "      <td>9.956864e+00</td>\n",
       "      <td>9.262219e+00</td>\n",
       "      <td>5.652087e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             pclass           age         sibsp         parch          fare  \\\n",
       "count  1.309000e+03  1.309000e+03  1.309000e+03  1.309000e+03  1.309000e+03   \n",
       "mean   5.000329e-15  1.832313e-16 -1.028801e-15  3.833620e-17  8.410215e-16   \n",
       "std    1.000382e+00  1.000382e+00  1.000382e+00  1.000382e+00  1.000382e+00   \n",
       "min   -1.546098e+00 -2.307330e+00 -4.790868e-01 -4.449995e-01 -6.437751e-01   \n",
       "25%   -3.520907e-01 -6.119712e-01 -4.790868e-01 -4.449995e-01 -4.911082e-01   \n",
       "50%    8.419164e-01  1.302752e-16 -4.790868e-01 -4.449995e-01 -3.643001e-01   \n",
       "75%    8.419164e-01  3.974806e-01  4.812878e-01 -4.449995e-01 -3.906640e-02   \n",
       "max    8.419164e-01  3.891737e+00  7.203909e+00  9.956864e+00  9.262219e+00   \n",
       "\n",
       "               body  \n",
       "count  1.309000e+03  \n",
       "mean  -2.912731e-17  \n",
       "std    1.000382e+00  \n",
       "min   -5.402590e+00  \n",
       "25%    3.201135e-17  \n",
       "50%    3.201135e-17  \n",
       "75%    3.201135e-17  \n",
       "max    5.652087e+00  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Normalizing the Data - StandardScaler:\n",
    "stsc = StandardScaler()\n",
    "df_titanic[n_cols] = stsc.fit_transform(df_titanic[n_cols])\n",
    "df_titanic[n_cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "201e4ce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#so:\n",
    "#the mean(5.000329e-15) is equal to zero.\n",
    "#the standard deviation(std) of scaled columns are also equal to zero."
   ]
  }
 ],
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