Mean Median Imputation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cfb49d99",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a351ad72",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea67bab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('titanic_csv.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "44d13a34",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Family</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Fare  Family  Survived\n",
       "0  22.0   7.2500       1         0\n",
       "1  38.0  71.2833       1         1\n",
       "2  26.0   7.9250       0         1\n",
       "3  35.0  53.1000       1         1\n",
       "4  35.0   8.0500       0         0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d6a4b512",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Age       714 non-null    float64\n",
      " 1   Fare      846 non-null    float64\n",
      " 2   Family    891 non-null    int64  \n",
      " 3   Survived  891 non-null    int64  \n",
      "dtypes: float64(2), int64(2)\n",
      "memory usage: 28.0 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "af7e45fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age         177\n",
       "Fare         45\n",
       "Family        0\n",
       "Survived      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d8568eb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Survived'])\n",
    "y = df['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0e28e036",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8f60f183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((712, 3), (179, 3))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1fe575fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age       0.207865\n",
       "Fare      0.050562\n",
       "Family    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b9cd4a8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_age = X_train['Age'].mean()\n",
    "median_age = X_train['Age'].median()\n",
    "\n",
    "mean_fare = X_train['Fare'].mean()\n",
    "median_fare = X_train['Fare'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "77cd2483",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train['Age_median'] = X_train['Age'].fillna(median_age)\n",
    "X_train['Age_mean'] = X_train['Age'].fillna(mean_age)\n",
    "\n",
    "X_train['Fare_median'] = X_train['Fare'].fillna(median_fare)\n",
    "X_train['Fare_mean'] = X_train['Fare'].fillna(mean_fare)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7721c841",
   "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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Family</th>\n",
       "      <th>Age_median</th>\n",
       "      <th>Age_mean</th>\n",
       "      <th>Fare_median</th>\n",
       "      <th>Fare_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "      <td>28.75</td>\n",
       "      <td>29.785904</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>30.0</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>25.0</td>\n",
       "      <td>91.0792</td>\n",
       "      <td>1</td>\n",
       "      <td>25.00</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>91.0792</td>\n",
       "      <td>91.0792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>28.75</td>\n",
       "      <td>29.785904</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>23.0</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>23.00</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>263.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age      Fare  Family  Age_median   Age_mean  Fare_median  Fare_mean\n",
       "738   NaN    7.8958       0       28.75  29.785904       7.8958     7.8958\n",
       "799  30.0   24.1500       2       30.00  30.000000      24.1500    24.1500\n",
       "484  25.0   91.0792       1       25.00  25.000000      91.0792    91.0792\n",
       "45    NaN    8.0500       0       28.75  29.785904       8.0500     8.0500\n",
       "88   23.0  263.0000       5       23.00  23.000000     263.0000   263.0000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b51ea79a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Age variable variance:  204.3495133904614\n",
      "Age Variance after median imputation:  161.9895663346054\n",
      "Age Variance after mean imputation:  161.81262452718673\n",
      "Original Fare variable variance:  2448.197913706318\n",
      "Fare Variance after median imputation:  2340.0910219753637\n",
      "Fare Variance after mean imputation:  2324.2385256705547\n"
     ]
    }
   ],
   "source": [
    "print('Original Age variable variance: ', X_train['Age'].var())\n",
    "print('Age Variance after median imputation: ', X_train['Age_median'].var())\n",
    "print('Age Variance after mean imputation: ', X_train['Age_mean'].var())\n",
    "\n",
    "print('Original Fare variable variance: ', X_train['Fare'].var())\n",
    "print('Fare Variance after median imputation: ', X_train['Fare_median'].var())\n",
    "print('Fare Variance after mean imputation: ', X_train['Fare_mean'].var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bb499200",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 16,
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# original variable distribution\n",
    "X_train['Age'].plot(kind='kde', ax=ax)\n",
    "\n",
    "# variable imputed with the median\n",
    "X_train['Age_median'].plot(kind='kde', ax=ax, color='red')\n",
    "\n",
    "# variable imputed with the mean\n",
    "X_train['Age_mean'].plot(kind='kde', ax=ax, color='green')\n",
    "\n",
    "# add legends\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(lines, labels, loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "173f2146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x22c9d619d00>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# original variable distribution\n",
    "X_train['Fare'].plot(kind='kde', ax=ax)\n",
    "\n",
    "# variable imputed with the median\n",
    "X_train['Fare_median'].plot(kind='kde', ax=ax, color='red')\n",
    "\n",
    "# variable imputed with the mean\n",
    "X_train['Fare_mean'].plot(kind='kde', ax=ax, color='green')\n",
    "\n",
    "# add legends\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(lines, labels, loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e2e86917",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Family</th>\n",
       "      <th>Age_median</th>\n",
       "      <th>Age_mean</th>\n",
       "      <th>Fare_median</th>\n",
       "      <th>Fare_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>204.349513</td>\n",
       "      <td>70.719262</td>\n",
       "      <td>-6.498901</td>\n",
       "      <td>204.349513</td>\n",
       "      <td>204.349513</td>\n",
       "      <td>64.858859</td>\n",
       "      <td>66.665205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>70.719262</td>\n",
       "      <td>2448.197914</td>\n",
       "      <td>17.258917</td>\n",
       "      <td>57.957599</td>\n",
       "      <td>55.603719</td>\n",
       "      <td>2448.197914</td>\n",
       "      <td>2448.197914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>-6.498901</td>\n",
       "      <td>17.258917</td>\n",
       "      <td>2.735252</td>\n",
       "      <td>-5.112563</td>\n",
       "      <td>-5.146106</td>\n",
       "      <td>16.476305</td>\n",
       "      <td>16.385048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_median</th>\n",
       "      <td>204.349513</td>\n",
       "      <td>57.957599</td>\n",
       "      <td>-5.112563</td>\n",
       "      <td>161.989566</td>\n",
       "      <td>161.812625</td>\n",
       "      <td>53.553455</td>\n",
       "      <td>55.023037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_mean</th>\n",
       "      <td>204.349513</td>\n",
       "      <td>55.603719</td>\n",
       "      <td>-5.146106</td>\n",
       "      <td>161.812625</td>\n",
       "      <td>161.812625</td>\n",
       "      <td>51.358000</td>\n",
       "      <td>52.788341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare_median</th>\n",
       "      <td>64.858859</td>\n",
       "      <td>2448.197914</td>\n",
       "      <td>16.476305</td>\n",
       "      <td>53.553455</td>\n",
       "      <td>51.358000</td>\n",
       "      <td>2340.091022</td>\n",
       "      <td>2324.238526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare_mean</th>\n",
       "      <td>66.665205</td>\n",
       "      <td>2448.197914</td>\n",
       "      <td>16.385048</td>\n",
       "      <td>55.023037</td>\n",
       "      <td>52.788341</td>\n",
       "      <td>2324.238526</td>\n",
       "      <td>2324.238526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Age         Fare     Family  Age_median    Age_mean  \\\n",
       "Age          204.349513    70.719262  -6.498901  204.349513  204.349513   \n",
       "Fare          70.719262  2448.197914  17.258917   57.957599   55.603719   \n",
       "Family        -6.498901    17.258917   2.735252   -5.112563   -5.146106   \n",
       "Age_median   204.349513    57.957599  -5.112563  161.989566  161.812625   \n",
       "Age_mean     204.349513    55.603719  -5.146106  161.812625  161.812625   \n",
       "Fare_median   64.858859  2448.197914  16.476305   53.553455   51.358000   \n",
       "Fare_mean     66.665205  2448.197914  16.385048   55.023037   52.788341   \n",
       "\n",
       "             Fare_median    Fare_mean  \n",
       "Age            64.858859    66.665205  \n",
       "Fare         2448.197914  2448.197914  \n",
       "Family         16.476305    16.385048  \n",
       "Age_median     53.553455    55.023037  \n",
       "Age_mean       51.358000    52.788341  \n",
       "Fare_median  2340.091022  2324.238526  \n",
       "Fare_mean    2324.238526  2324.238526  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1aaecd85",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Family</th>\n",
       "      <th>Age_median</th>\n",
       "      <th>Age_mean</th>\n",
       "      <th>Fare_median</th>\n",
       "      <th>Fare_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
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       "      <td>-0.299113</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.087356</td>\n",
       "      <td>0.090156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.092644</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.208268</td>\n",
       "      <td>0.091757</td>\n",
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       "      <td>0.205942</td>\n",
       "      <td>0.205499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_median</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.091757</td>\n",
       "      <td>-0.242883</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999454</td>\n",
       "      <td>0.086982</td>\n",
       "      <td>0.089673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_mean</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.088069</td>\n",
       "      <td>-0.244610</td>\n",
       "      <td>0.999454</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.083461</td>\n",
       "      <td>0.086078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare_median</th>\n",
       "      <td>0.087356</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.205942</td>\n",
       "      <td>0.086982</td>\n",
       "      <td>0.083461</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare_mean</th>\n",
       "      <td>0.090156</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.205499</td>\n",
       "      <td>0.089673</td>\n",
       "      <td>0.086078</td>\n",
       "      <td>0.996607</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Age      Fare    Family  Age_median  Age_mean  Fare_median  \\\n",
       "Age          1.000000  0.092644 -0.299113    1.000000  1.000000     0.087356   \n",
       "Fare         0.092644  1.000000  0.208268    0.091757  0.088069     1.000000   \n",
       "Family      -0.299113  0.208268  1.000000   -0.242883 -0.244610     0.205942   \n",
       "Age_median   1.000000  0.091757 -0.242883    1.000000  0.999454     0.086982   \n",
       "Age_mean     1.000000  0.088069 -0.244610    0.999454  1.000000     0.083461   \n",
       "Fare_median  0.087356  1.000000  0.205942    0.086982  0.083461     1.000000   \n",
       "Fare_mean    0.090156  1.000000  0.205499    0.089673  0.086078     0.996607   \n",
       "\n",
       "             Fare_mean  \n",
       "Age           0.090156  \n",
       "Fare          1.000000  \n",
       "Family        0.205499  \n",
       "Age_median    0.089673  \n",
       "Age_mean      0.086078  \n",
       "Fare_median   0.996607  \n",
       "Fare_mean     1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "54ca532f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train[['Age', 'Age_median', 'Age_mean']].boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b0f55967",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train[['Fare', 'Fare_median', 'Fare_mean']].boxplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3800ad54",
   "metadata": {},
   "source": [
    "## Using Sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "644d00cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "17eb24a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer1 = SimpleImputer(strategy='median')\n",
    "imputer2 = SimpleImputer(strategy='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "508d1a84",
   "metadata": {},
   "outputs": [],
   "source": [
    "trf = ColumnTransformer([('imputer1',imputer1,['Age']),\n",
    "                       ('imputer2',imputer2,['Fare'])],\n",
    "                        remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c9588f72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;imputer1&#x27;, SimpleImputer(strategy=&#x27;median&#x27;),\n",
       "                                 [&#x27;Age&#x27;]),\n",
       "                                (&#x27;imputer2&#x27;, SimpleImputer(), [&#x27;Fare&#x27;])])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;imputer1&#x27;, SimpleImputer(strategy=&#x27;median&#x27;),\n",
       "                                 [&#x27;Age&#x27;]),\n",
       "                                (&#x27;imputer2&#x27;, SimpleImputer(), [&#x27;Fare&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">imputer1</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">imputer2</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Fare&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Family&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ColumnTransformer(remainder='passthrough',\n",
       "                  transformers=[('imputer1', SimpleImputer(strategy='median'),\n",
       "                                 ['Age']),\n",
       "                                ('imputer2', SimpleImputer(), ['Fare'])])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trf.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9d649a29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([28.75])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trf.named_transformers_['imputer1'].statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "569885c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([32.61759689])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trf.named_transformers_['imputer2'].statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1d26817b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = trf.transform(X_train)\n",
    "X_test = trf.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6e666e4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 40.    ,  27.7208,   0.    ],\n",
       "       [  4.    ,  16.7   ,   2.    ],\n",
       "       [ 47.    ,   9.    ,   0.    ],\n",
       "       ...,\n",
       "       [ 71.    ,  49.5042,   0.    ],\n",
       "       [ 28.75  , 221.7792,   0.    ],\n",
       "       [ 28.75  ,  25.925 ,   0.    ]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  }
 ],
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