Imputing Numerical Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6117a87f",
   "metadata": {},
   "source": [
    "## Arbitary Value Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "03b80818",
   "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": "678e04a4",
   "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": "f36dd5b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('titanic_csv.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef8dc11e",
   "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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "495a16e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age         0.198653\n",
       "Fare        0.050505\n",
       "Family      0.000000\n",
       "Survived    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8773540e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Survived'])\n",
    "y = df['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ec6c6c21",
   "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": "9e5c9cfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train['Age_99'] = X_train['Age'].fillna(99)\n",
    "X_train['Age_minus1'] = X_train['Age'].fillna(-1)\n",
    "\n",
    "X_train['Fare_999'] = X_train['Fare'].fillna(999)\n",
    "X_train['Fare_minus1'] = X_train['Fare'].fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "28dcb424",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Age variable variance:  204.3495133904614\n",
      "Age Variance after 99 wala imputation:  951.7275570187172\n",
      "Age Variance after -1 wala imputation:  318.0896202624484\n",
      "Original Fare variable variance:  2448.197913706318\n",
      "Fare Variance after 999 wala imputation:  47219.20265217623\n",
      "Fare Variance after -1 wala imputation:  2378.5676784883503\n"
     ]
    }
   ],
   "source": [
    "print('Original Age variable variance: ', X_train['Age'].var())\n",
    "print('Age Variance after 99 wala imputation: ', X_train['Age_99'].var())\n",
    "print('Age Variance after -1 wala imputation: ', X_train['Age_minus1'].var())\n",
    "\n",
    "print('Original Fare variable variance: ', X_train['Fare'].var())\n",
    "print('Fare Variance after 999 wala imputation: ', X_train['Fare_999'].var())\n",
    "print('Fare Variance after -1 wala imputation: ', X_train['Fare_minus1'].var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1dde33b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c5fe7626a0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QEyKMnlX6PkfXZ+v1OKwpJiaGpKQkAL744ou67YcOHWLIkCE8+uijnHPOOTbvGWbLxLEZ6C8isSLiBswHVpxVZgVwg/n5FcBapZQSEX9gJbBQKVU3a5iIuIhIsPm5KzAD2G3Dn0FzcCcKyoiycjMVQFSAJ36errpnVTewaNGiBrWLuXPnkpmZWbcex4IFCxqsx/Hoo48ybNgwEhMTbdIjypKnnnqKBx54gFGjRp2xzOyrr77K4MGDGTp0KK6urlx88cUATJw4kXnz5vHf//6XqKgoVq9ebZU4xMLtAasRkUuAVwFn4D2l1PMi8mdgi1JqhYh4AB8Bw4GTwHyl1GER+X/AY8DBeqe7CCgFNgKu5nP+ADyklKppKo5Ro0apLVt0J6yuRinFkKe/54qRUTw9y/IiOu1xzdu/UVpRzfJ7G29b1tpv3759DBo0yN5hWHT2ehw///xzl51a3dLvQUSSlFKjzi5r03scSqlVwKqztj1Z73k5MM/Ccc8BzzVy2pHWjFHrvIrKqimpqLbZan0JEb588OtRqmpMVpnOROt89Hocljn0zXFNa0qauUeVLZqqwLjPUVlt4lBOCXFhvja5hubY9HoclunEoXVatWM4rN0Vt1btCPK96UU6cdiYUgoRsXcY7dKZ1+No7S0LXf/WOq2WjhpXSrHhyAZe2/Qa61LXtfiPJDbYG3cXJz0Q0MY8PDzIy8tr9ZuXZh1KKfLy8lo1aFDXOLROKy2/DE9XZwK93RotU15dzoJlC/hi3+mui5f2v5RPLv8EP4+mV/hzcXYiLsyHvRk6cdhSVFQUaWlp5OTk2DuUbsvDw6NVgwZ14tA6rRMFp4gM8GyyiePeVffyxb4veOGCF7hh2A0s2bOER9Y8wpwlc/huwXe4OTeedADiI3z5dndml2hKcVSurq7ExsbaOwytFXRTldZppReUE9FEM9Wqg6t4d9u7/GnCn1g4YSHhPuE8OPZB3pv1HuuOrOO5jY113DstPsKPglNVZBSWN1tW07oLnTi0TiuzqJxwX8vtsjWmGh5Z8wj9A/vz1OSnzth33bDruHbItbz404vszdlr8fha8eGnb5BrmmbQiUPrlKpqTOSWVNDTz3Li+DblW/bm7OXZKc9abI76+7S/4+3mzWP/fazJ68SF+SCi1+bQtPp04tA6pZziCpSCsEZqHK///jqRPpFcPuhyi/tDvEN4aOxDrEheQVJ6UqPX8XZ3ITbIm70Zes4qTaulE4fWKWUWGfccwvzcG+w7nH+Y1YdWc8fIO3B1bnzW3AfGPkCARwAv/vxik9caFOGre1ZpWj06cWidUpb5ZnVPCzWOz/d8DsD1w65v8hy+7r7cMvwWvtz3JenF6Y2Wiw/35fjJMgrLqtoRsaZ1HTpxaJ1SXY3DUuLY+zmjI0fT2793s+e5c9SdmJSJt5LearRMvHkE+X5d69A0QCcOrZPKLCrHzdmpweC/1PxUkjKSmBffYO5Mi/oG9mV6v+m8vfVtakyWJ1mum3pEJw5NA3Ti0DqprMJyQn3dGwzK+zblWwBmD5zd4nPdPPxm0ovTWX9kvcX9oT4eBPdw111yNc1MJw6tU8osKrfYTLXm8Bpi/GPoF9ivxee6tP+l+Lj58MmuTxotE69vkGtaHZ04tE4pq6jhGI5qUzXrUtdxYeyFrZoexNPVk8sHXc4X+76gvNryCPH4cF8OZBVTWW1qV9ya1hXoxKF1OkopMgsb1jiS0pMorCjkwj4Xtvqc1w65lqKKIlYeWGlxf3yEL1U1ipTskjbFrGldiU4cWqdTVF5NWVVNg8Txw+EfADg/9vxWn3NK7BSCvYJZtn+Zxf11U4/o5ipN04lD63wya8dwnNVUtfHYRoaEDiHEO6TV53RxcmHGgBmsOriKqpqG4zVig73xcNVrc2ga6MShdUKWxnCYlIlNaZs4N+rcNp939sDZFJQX8OOxHxvsc3YS4sJ89dQjmoZOHFonVDtqvH7i2J+7n8KKQs6NbnvimNpnKh4uHizfv9zi/oQIX/amF+mV6rRuTycOrd2qaqr4+69/562ktzAp2/c6qq1xhPqenqfq1+O/ArSrxuHt5s2FfS5kxYEVFpNDfIQvReXVnCgoa/M1NK0r0IlDa7fnNj7HQ98/xB3f3MGbm9+0+fUyi8oJ8HLFw9W5btuvab8S4BHAgKAB7Tr3rAGzOFJwhF3Zuxrs02tzaJpBJw6tXcqqyvjn7/9kTtwczut9Hi/89ALVpmqbXjOrsLzB5Ia/pf3G2KixTY/fUApSUuDHHyHd8qSGl/S/BIDVKasb7IsL88VJr82habZNHCIyXUSSRSRFRBZa2O8uIkvM+zeJSIx5+1QRSRKRXeav59c7ZqR5e4qIvCZ6IWi7+ubAN+SX53Pv6Hu5d/S9nCg+wU/HfrLpNTOLygmr16OqtLKUvTl7GRM5pvGDfv8dRo+G/v1h0iSIjIRLLjESST2RvpEkhCTw/eHvG5zC082Z2GBv3SVX6/ZsljhExBl4A7gYiAeuFpH4s4rdAuQrpfoBfwdeMm/PBWYqpYYANwAf1TvmTeA2oL/5Md1WP4PWvDWH1+Dn7sek3pOY3m86bs5ufJ38tU2vmXXWdCO7snehUAwPH275gGXLYMIEyMqCf/wDVq+GZ56Bn3+GUaNg/fozil/U9yJ+PPojp6pONThVfISfbqrSuj1b1jhGAylKqcNKqUpgMXD2zHOzgQ/Mz5cCF4iIKKW2KaVq2xL2AJ7m2kk44KuU+k0Zdy8/BC6z4c+gNeOHwz8wOWYyLk4u9HDrwaTek1hzeI3NrldZbSK3pPKMpqptGdsASAxLbHjAunUwfz6MHAk7dsD998NFF8GTT8LOnUbNY9Ys2L697pCL+l5ERU0FPx5t2C03PtyXEwVlFJ7Sa3No3ZctE0ckcLze92nmbRbLKKWqgUIg6Kwyc4GtSqkKc/m0Zs4JgIjcLiJbRGRLTk5Om38IrXHZpdmkFqQysdfEum0ToiewO3s3RRW2+VSeXVy78t/pxLE9czsBHgFE+0afWTgvD669Fvr2hVWrICDgzP29e8P334Ofn5E88vMBmNR7Em7Obnx/qGFzlZ5iXdMc/Oa4iCRgNF/d0dpjlVJvKaVGKaVGhYS0fiSx1rztmdsBGBE+om7buOhxKBS/n/jdJtfMsjD4b3vWdhLDEhveGH/gAcjNhU8/bZg0akVGGk1ZGRlwzz0AeLl6MbHXRIv3OQbpqUc0zaaJ4wRQ/yNglHmbxTIi4gL4AXnm76OAL4HrlVKH6pWPauacWgepTRzDwobVbRsdORqAzSc22+SamYUVwOklY6tN1ezM2tmwmerXX+GTT+DRR2F4I/c+ap1zDjzxBCxaBN99BxjNVbuzdzdYUjbEx51QH702h9a92TJxbAb6i0isiLgB84EVZ5VZgXHzG+AKYK1SSomIP7ASWKiU+rm2sFIqAygSkbHm3lTXA5aH+Wo2ty1zG738ehHoGVi3zc/Dj95+vS2Og7CGuulGzE1VB/IOUF5dfmbiUAoefhjCwozE0RKPPgr9+sEf/gBVVVzU9yIAi81V8RG+7EnXU49o3ZfNEof5nsW9wGpgH/CZUmqPiPxZRGaZi70LBIlICvAQUNtl916gH/CkiGw3P0LN++4G3gFSgEPAt7b6GbSm7cjcYfGG9NCeQ9mZtdMm18wqKsfNxYkAL1fgdK3njDjWr4dffoGnnoIePVp2Ynd3eOUV2L8f3nyToT2HEuwVzNrUtQ2Kxof7kpJdQkW15aVmNa2rc7HlyZVSq4BVZ217st7zcqDB4tBKqeeA5xo55xZgsHUj1Vqr2lRNyskUi0u0Du05lFUHV1FRXYG7i7uFo9sus7CcnvWWjN2euR03ZzcGBQ86Xeill6BnT7jxxtadfMYMmDIFXngBp9tuY3LMZNYfWY9S6oz7J/ERvlSbFAezShgc6WeFn0rTOheHvjmuOa5jhceoMlVZXKJ1SOgQalQN+3L3Wf26Zy8Zuz1zOwkhCbg6GzUQduwwxmk88AB4NFxatkkiRi0lMxPeeYfJvSdzvOg4h/MPn1FMr82hdXc6cWhtknLSGHHdP6h/g32DQ40K4Z7sPVa/blbRmdON7M3ZW3c9AP75T/DygrvuatsFzjvPGFn+4otMiRwPwPoj688oEhPkjZebs75BrnVbOnFobXIw7yCAxRpH38C+CMLBkwetes2zl4wtLC/kRPEJ4kPMExIUF8PixcaAP3//tl/o8cchPZ1BP+wg1DuUdUfWnbHbyUkYFO6raxxat6UTh9YmKSdT8HL1IrxHeIN9Hi4e9PbvbfXEUVhWRUW1qa5HVW1TWF3iWLQISkvhttvad6GpUyE+HnntNSb3nsy6I+saTLMeH+7LPr02h9ZN6cShtUlKfgr9Avs1OhvtgKABHMg7YNVr1nbFrW2q2puzF6iXON5+GwYPhjFNTHbYEiLG1CRbtzKlJpr04vS6prla8RG+FFdUk5av1+bQuh+dOLQ2OZh30GIzVa0BgUbisOYn8tq1xmtrHHtz9uLu7E6sf6zRjXbLFrjlFuONv70WLICAAKasMMajnN1cVXuDXI/n0LojnTi0VjMpE4fzD9M3oG+jZfoH9aeooojs0myrXffs6Ub25uwlLjgOZydnWLLESBhXXmmdi3l7wy23MOCzHwjzbHifY2CYj16bQ+u2dOLQWi33VC5Vpip6+fVqtEztSnzWbK6qnW6kdsnYvTl7jWYqpYyb4uedBxERVrset92G1JiYUhFeN56jloerMwN6+rAjTdc4tO6nRYlDRJaJyKUiohONVjd/k6Ub47VskjiKygnydsPdxZmSyhKOFh41EsfOnUZT1fz5VrsWAAMGwKRJTP4lncySTJLzks/YPbxXANuP5WMy6RvkWvfS0kTwL+Aa4KCIvCgiA20Yk+bgahNHhE/jn+57+fXCWZwbDJ5rj/pjOPbn7gfMN8YXLwZnZ5g712rXqnPLLUz53ZiW/+zxHMN7+VNUXs3h3FLrX1fTHFiLEodS6gel1LXACOAI8IOI/CIiN4mIqy0D1BxPSxKHi5ML0X7RHCk8YrXrZhaWn3FjHCA+eBB8/jlceCEEB1vtWnWuuIJ+1T5EVns1uM8xopc/AFuP5Vv/uprmwFrc9CQiQcCNwK3ANuAfGInEdsu9aQ6pNnGE9Qhrslysfyyp+alWu279GsfenL24OrnSN6sSDh2COXOsdp0zeHkh1y5g8v5y1h1ee8Z9jj7BPfD1cGHbsQLbXFvTHFRL73F8CfwIeGGsBT5LKbVEKXUf0MLpR7WuIr04nWCv4GYnMIz1jyW1wDqJo6K6hrzSyjN6VA0IGoDrN+bJkWfMsMp1LLrhBqYcMpFTlltX0wFjBHlirwC26RqH1s20tMbxtlIqXin1gnlNDETEHUApNcpm0WkOKaMko8lmqlox/jFklmRSVtX+QXLZRUaPqjC/s3pUrVgBo0YZK/nZyujRTFG9gYbjOYZH+3Mgq5iSimrbXV/THExLE4elKc5/tWYgWueRXpzeosQRGxALwJGCI+2+5ukFnDwpry4ntSCVQZ7R8NtvxnrhtiRC7Owb6F0A6/adsUoAI3oHYFKw83iBbWPQNAfSZOIQkTARGQl4ishwERlhfkzGaLbSuqH04vQmu+LWivU3Eoc1mqvqRo37epByMgWTMhGXWmKM4bB14gDk2gVMSYX1RzZgUqa67YlR/oC+Qa51L80t5DQN44Z4FPBKve3FwJ9sFJPmwGpMNWSWZLaqxmGNG+T1R42vOWJ0xR34SzL06gVDh7b7/M3q358pTn14n8PsytpVt866n5crfUO82apvkGvdSJM1DqXUB0qpKcCNSqkp9R6zlFLLOihGzYFkl2ZjUqYWJY6wHmG4O7tbp6mqsBwPVyd8PV1IzjUG4g1YtcmobVhjbqoWmDLpBgDW/bbojO3nxASy5chJavRAQK2baK6paoH5aYyIPHT2owPi0xxMS8Zw1HISJ2L8Y6zTVGVe+U9E2J+3nyjXYHoUlcPMme0+d0tFX3MnfU/C2q1fnLF9TJ9Aisqr2afX59C6ieZujnubv/YAfCw8tG4moyQDaFniAKO5yhqJI6vo9OC/5Nxk4k55GkvDTprU7nO3WGgoU6qj2Fh9iJrqqrrNY2KDANiUerLjYtE0O2quqerf5q/PWHp0TIiaI2lNjQOsNwgww7zyn1KK/bn7GZhabExq2Np1xdtpypBZFLortq1+v25bhL8nvQK9+O1wXofGomn20tIBgP8rIr4i4ioi/xWRnHrNWFo3kl6cjiD09O7ZovKx/rHkl+dTWN72WWSVUmQXVdDTz4PMkkyKK4uJSymAadPafM62mnLZHwBYt+69M7aPiQ3k99STesJDrVto6TiOi5RSRcAMjLmq+gGP2CoozXGlF6cT4h2Cq3PLpiir61nVjuaqk6WVVNaYCPP1qJvccGAucNFFbT5nW4WH9SOuwod1eUlQdbq5amyfIArLqtifWdzhMWlaR2tp4qjttnsp8LlSqkUfH0Vkuogki0iKiCy0sN9dRJaY928SkRjz9iARWSciJSLy+lnHrDefc7v5EdrCn0GzgpYO/qsV4x8DtK9Lbu3gv3A/j7qpzeNcekJ8fJvP2R5TIsbzY3gVVWtW120b0ycQgE2purlK6/pamji+EZH9wEjgvyISApQ3dYCIOANvABcD8cDVInL2X/otQL5Sqh/wd+Al8/Zy4Ang4UZOf61SKtH8sN4Sc1qzWps4agcBtqdLbla9tcb35+zDqwoix1/cYd1wzzZl4nWUuEPSl2/UbYsK8CIqwJNfD+nEoXV9LZ1WfSEwDhillKoCSoHZzRw2GkhRSh1WSlUCiy0cMxv4wPx8KXCBiIhSqlQp9RPNJCet42WUZBDRo+WJI9AzEB83n3Y1VdWu/Bfm50Hy4d8ZmAtO06a3+XztNbnfVADWHV4LZafn4RrfN5hfD+dRXWNq7FBN6xJas6JfHHCViFwPXAE018AcCRyv932aeZvFMkqpaqAQCGpBLP8xN1M9IWL5Y6eI3C4iW0RkS05OTgtOqTWn2lRNVklWq2ocItLuLrmZhWU4CYT0cDd6VOVirL9hJyHeIQz2imFdZCWsXFm3fdKAEIrLq9mu563SuriW9qr6CHgZmACcY37Ya1bca5VSQ4CJ5sd1lgoppd5SSo1SSo0KCQnp0AC7qqySLBSqVYkD2t8lN7OonOAe7lSZKjiqCohzj4Cglny+sJ0pCTP4qRdULP64btuEfsE4CWw4oD+oaF1bS2sco4DxSqm7lVL3mR/3N3PMCSC63vdR5m0Wy4iIC+AHNNlIrJQ6Yf5aDHyK0SSmdYC6tcZ9mp/gsL7adTnqL4LUGplFFYT7eZBydCtKYGAf+//Kp/a9iDJX+HnXKig0+or4ebmSGO3PRp04tC6upYljN9D0cm8NbQb6i0isiLgB84EVZ5VZAdxgfn4FsFY18e4iIi4iEmx+7orRPXh3K+PS2qi1g/9qxfjHcKrqFLmnctt03axCY+W//T9+CUDcWBsu2tRCU2Kn4CourO5VBV9+Wbd90oAQdp4o5GRppR2j0zTbamniCAb2ishqEVlR+2jqAPM9i3uB1cA+4DOl1B4R+bOI1M6D/S4QJCIpwENAXZddETmCMSPvjSKSZu6R5Q6sFpGdwHaMGsvbLfwZtHZqa+Jo71iO9MIyoyvuno0A9J88t03nsaYebj0Y32sC38W7waLTkx5OGhCCUvBTStuSpKZ1Bs1Nq17r6bacXCm1Clh11rYn6z0vB+Y1cmxMI6cd2ZZYtPbLKMnASZwI9W7d0Jm6dTnyUxkd2bpmpqLyKorLq4n092Bj9l56uXni7e3fqnPYyvR+01l4dD0Zv60hPCsLevZkWJQ/fp6ubEjOYdaw1iVYTessWtoddwPGiHFX8/PNwFYbxqU5oPTidHp698TFqaWfNwztqXGkFxjdXfsXZZLsUcrAHr1bfQ5bmdbPmPLk+1gFn38OgLOTMHlgCOuSs/U061qX1dJeVbdhjLP4t3lTJPCVjWLSHFRrB//V6uHWg2Cv4Db1rKpNHH23/sz+YIjrc06rz2ErQ3sOpad3T74b5XdGc9XU+J6cLK0k6aheFVDrmlp6j+MeYDxQBKCUOgjoqT66mbYmDjjds6q1TuQbiaNm6xpK3GFgnzFtur4tOIkT0/pNY010FTW//gJHjgBw3oAQXJ2F7/dk2jdATbORliaOCvPob6Cu66yuh3czLV1r3JLYgNg2TTtyoqAcb2o4fPBnAOKC49p0fVuZ1ncaeZwiKQJYvBgAHw9XxvUNZs2+rDZ3QdY0R9bSxLFBRP4EeIrIVOBz4GvbhaU5msqaSnJO5bS5xhHjF8PRwqOYVOum40gvKOP8wsMc8DZmnxkYPLBN17eVqX2mIgjfTolq0Fx1NO8UB7NL7BidptlGSxPHQiAH2AXcgdFT6v/ZKijN8WSVZAGt74pbKzYglsqayrouvS11oqCM84/uYH+I4O3qTaTP2bPW2FeIdwhjo8by9UCBnTthzx7ASByAbq7SuqSW9qoyYdwMv1spdYVS6u2mBuppXU9bx3DUqt8lt1XXLShj5IHNJPf1Y2DwQBqZmsyuZg2cRVLNcdL8pK7W0dPXg2HR/ny/N8vO0Wma9TWZOMTwtIjkAslAsnn1vyebOk7retqdONrQJbeqxkRFVjbRh/eyJ9hEfIh91t9ozuyBxqTPX88caCQO82eq6Qlh7Ewr5PjJU/YMT9Osrrkaxx8welOdo5QKVEoFAmOA8SLyB5tHpzmM9iaO3n69EaRVN8gzC8s5N3UHRW6KNIpICElo07VtLS44jn6B/Vg+2AUOH4bNmwGYMdToSPDNzgx7hqdpVtdc4rgOuFopVfcxUSl1GFgAXG/LwDTHkl6cjrM4E+LdtpmG3V3cifCJaFWN40RBGROPbGNnby8Ah00cIsLsgbNZW5lMUQ/Xuuaq6EAvhkX7s3JX6+7raJqjay5xuCqlGky6o5TKAVq26LTWJaSXpBPWIwwnac0SLmeK8Y9p1T2O9PxTTDiyjR3n9gcgIdQxEwcY9zmqTFWsnptodMutqQFgxpBwdp8o4khuqX0D1DQrau5doKkpPvX0n91IRnFGm5uparV2QafinXuIKsrhYHwQXq5edeuXO6Jx0eMI8gziq2HukJkJGzYAcKm5uWrlLt1cpXUdzSWOYSJSZOFRDAzpiAA1x9CeUeO1Yv1jSStKo6qmqkXlfTauA2CffyWDgge1q7Zjay5OLsweOJuvy3dQ7udd11wV4e/JyN4BfL1DN1dpXUeTf4lKKWellK+Fh49SSjdVdSPWSBx9A/piUqYW1zp6bfmZjNAo9pQcduhmqlpXDb6K4spivp0/EpYuhQpjrfQZQ8PZn1lMih4MqHURjvsRTnMYFdUV5JXltTtx1I76Ts5Nbr5wZSXxB5LYOeoc0ovTHfbGeH3nx55PsFcwSxKAggJYvRqAS4aEIwIrde8qrYvQiUNrVkaJ8YbX7sQRZE4cec0njlMbfsKrspxdY/sCjtujqj4XJxfmDprL18VbKO0ZeMZgwHNiAvlmZ7qeu0rrEnTi0JpVt9Z4Gyc4rBXgGUCIV0iLahylX6+iWpw4PtAXcOweVfVdlXAVp6pOsfLqEbBiBZQYzVMzh0VwMLuEfRnFdo5Q09pPJw6tWe0d/FffwOCB7M/b32w5t/+uYWtkHDlynB5uPejl16vd1+4Ik3pPIqxHGEv6V8KpU0byAC4dEo6Lk7B8xwk7R6hp7acTh9asjGLrNFWB0VzVbI0jIwO/vTtZ32cUhwt3kxiW6NA9qupzdnJmXvw8Vp7cREGfiLrmqkBvNyYNCOHr7emY9MqAWifXOf4aNbtKL07H1cmVIK+gdp8rLjiOnFM55Jc1sTret98CsHnwGHZn72R42PB2X7cjXT/seipqKvjsygT47jvIywNgdmIE6YXlbNErA2qdnE4cWrPSS9IJ9wm3yqf+Ft0gX7mSvIBQTg7wp7SqlBHhI9p93Y40MnwkCSEJvB+eBdXVdeuRXzioJ56uzny1XTdXaZ2bThxas6wxhqNWbZfc/bmN3OeorIQ1a9jQ9xw8vY8BdLoah4hwY+KN/Jq/k+RxA+DddwHwdnfhooSerNqVQWV16xa00jRHohOH1ixrJo4+AX3wcPFgV9YuywV++gmKi1nVawRVTodxc3Zz2OnUm3LtkGtxEic+nBUDW7bA9u2A0VxVcKqKHw/m2DU+TWsPmyYOEZkuIskikiIiCy3sdxeRJeb9m0Qkxrw9SETWiUiJiLx+1jEjRWSX+ZjXxBFX9uli2rPW+NlcnFwYEjqE7VnbLRdYuRKTmxs/9x5GbuV+hoQOwdW5801SEO4TzvR+0/nQZTc1Hm51tY6J/UMI8HLlq+16ChKt87JZ4hARZ+AN4GIgHrhaRM7+6HgLkK+U6gf8HXjJvL0ceAJ42MKp3wRuA/qbH9OtH71Wq6yqjILyAqvVOAASwxLZnrnd8mC4lSvJGj6WU25upBTsYmT4SKtdt6PdOOxG0krS+e+C8fDxx1BWhquzE5cMCWfN3kxKK6rtHaKmtYktaxyjgRSl1GGlVCWwGJh9VpnZwAfm50uBC0RElFKlSqmfMBJIHREJB3yVUr+Zl679ELjMhj9Dt2etUeP1JYYlcrLsJGlFaWfuOHQIkpPZMXQ8zq4nKKooYFz0OKtdt6PNGjiLYK9g3kqsMaYg+eILAC4bHkl5lYk1elnZlisrg+XL4cMPIbV1yw9r1mfLxBEJHK/3fZp5m8UySqlqoBBoqs9npPk8TZ0TABG5XUS2iMiWnBzdntxW1hz8VysxLBGA7Znbz9yxciUAa2JH0sP3EADje4232nU7mruLOzcl3sRXeT+TPiQG3nkHgJG9Aoj099S9q1pqzRqIiYHLLoMbboB+/eDhh+vWPNE6Xpe9Oa6UekspNUopNSokpG2r1mm2SRxDew5FELZlbjtzx1dfoQYNYkONHzWu+wnxCqFvQF+rXdcebh95OzWqhnev7Ges0XHgAE5OwsxhEfx4MJe8kgp7h+jYVq2Ciy+G0FD4/nvYvRtuuw3+9jcjiei5v+zClonjBBBd7/so8zaLZUTEBfAD8po5Z1Qz59SsyBaJo4dbDxJCE/jl+C+nN+bmwoYNlF46i9ySCnIrdzG+13g6e9+HfoH9uKjvRbzlsYdqV2d46y3A6F1VY1Ks0gs8Ne7IEbjqKhg6FH75BaZOhYQE+L//g2efhU8+gVdesXeU3ZItE8dmoL+IxIqIGzAfWHFWmRXADebnVwBrVRPThyqlMoAiERlr7k11PbDc+qFrtdKL03F3difAI8Cq550QPYFfjv9Cjcnc3PD112AysXvMBVRLNtllR5nUa5JVr2kvd468k7TSDFZdN9boXVVayqBwXwb29NG9qxqjFNxyi/H8yy/Bx+fM/Y8/bjRdPf64cW9M61A2Sxzmexb3AquBfcBnSqk9IvJnEZllLvYuECQiKcBDQF2XXRE5ArwC3CgiafV6ZN0NvAOkAIeAb231M2hwovgEET4RVv/kP7H3RIori9mZtdPYsGwZ9O7NL769KHfeCsD0fl2jw9zMgTOJ8IngzeHVxk3yTz4BYFZiBElH8zl+8pR9A3REn30Ga9fCX/8KvXs33C8Cb7wBrq5w//0dH183Z9N7HEqpVUqpAUqpvkqp583bnlRKrTA/L1dKzVNK9VNKjVZKHa53bIxSKlAp1UMpFaWU2mvevkUpNdh8znubqqFo7Xei6ASRvhb7H7TLxF4TAVibuhaKi40boHPmsDu9CGfPHUT7RhMXHGf169qDi5MLt424jdV5v3N43CB4/XVQilnDjOa/FXpZ2TNVVcETT8Dgwcb9jMZERMBTTxn3QTZu7Lj4tK57c1yzjhPFJ4j0sX7iiPaLZkjoEFYcWGFMalhRgZozh63HMsiv2cIl/S/p9Pc36rttxG04OznzxuVRsGsXbNxIdKAXI3sHsEI3V53po4/g4EF4/nlwdm667D33QM+exj0PrcPoxKE1Sill1DhskDgA5sTN4adjP5GzYhGEhHCo/1DSKjZSpcq4dsi1NrmmvUT6RjIvfh7vVG6iuGcA/POfAFyWGEFyVjH7MorsHKGDUMq44T1sGMyc2Xx5T0945BH44QfYtMn28WmAThxaEwrKCyirLrNJUxXA5YMux6RMfHr8W5g9m1+PFlLivIZIn16devxGYx4c+yBFlUX855YR8NVXcPw4lwwJx9lJWK5rHYYffoA9e+APfzDuY7TE7bcbN89ff735sppV6MShNepEsdHT2VY1jmFhwxjnOZBXh1dQNm8Oy3avocJ5Jw+Ova/TLNzUGqMjR3Nu1Lm8FpRCDSZ4/XWCergzsX8wX+/QCzwB8OqrRtPT/PktP8bHxxjT8dlnkJ1ts9C007reX6dmNSeKzInDRjUOgD/vCuZIAFyR/QbfpD2Gr2s4d426y2bXs7cHxz7IoeKjrLxhnDEeobCQyxIjOVFQphd4OnrUuN91553g7t66Y+++25iS3zw6X7MtnTi0Rtm6xkF+Phd8tpnny8fz3eHVKEy8eN4HeLt52+Z6DuDyQZcT7RvNq8MroKgI/v1vpsb3xMPVSU9B8oF52rqbbmr9sYMGwfnnw9tvg0mvdWJrOnFojaqtcYT7WGdK9QaWLYPKSv4091Xuj/uJ/qYPuWn0+ba5loNwcXLhvtH3sS5vCztmj4FXX8WbGqYlhPH1jnTKKrvp/EsmE7z/vvHmb2ncRkvcdJMx2vznn60ZmWaBThxao04UnyDIMwgPFw/bXOCTT6B/fwoHDeW7XQXMHBqFh2sz3S+7gFtH3IqXqxevTvODjAz4+GPmn9OL4vJqVnbXKUg2bDBmvW1LbaPWnDng7W3MoKvZlE4cWqNOFNtm8J9x8hOwfj1ccw2fbD5GWVUNN4yLsc21HEyAZwA3Jd7EJ7nrOHFuAvz1r4yN8adPiDefbjpq7/Ds4/33wc8PLr+87efw9oYrrjBukpeVWS00rSGdOLRG2XIMB59+CkqRNeNy3lx3iCkDQ0iI8LPNtRzQw+MexqRMvDI/BpKTkWXLuGZ0L7YeK2B/Zjcb01FWZjRbzptnjMtoj+uvN+4drTh7WjzNmnTi0BplzVHjpyqr2X2ikP2ZReQWl6PeeYey0WO5/scCFPDkzASrXKeziPGP4eohV/PvkvXkDesPTz/N5cPCcXN24tNNx+wdXsf69lsoKTFmwm2vyZMhKqpuPjDNNnTi0CyqrKkkuzS73U1V1TUmXvh2H4l/XsOMf/7E9Fd/5M67XkMOHOCJ4LGkF5bx7+tGEhvcdXtSNWbh+IWUVpXy+m3DYO9eAlctZ8bQcJYmpVFwqtLe4XWczz6DkBDjTb+9nJyMmsvq1VBY2P7zaRbpxKFZlFFs3KRtT43DZFLcv3gb/95wmBlDwnnz2hG8cc0IXjz5OxWe3oz4422sf3gy4/sFWyvsTiUhNIFZA2fxWulaSobGwTPPcPuE3pyqrOGjX7vJvY7SUmNK/blzwcXFOue88kpjTIdurrIZnTg0i44WGm9cvf3b2DUS+M8vR1i1K5PHLo7jlasSuXhIOJf29qLf+lW4X7+Aa86PJ6hHKwd6dTGPTXiMk2UnefuuMbB/P3HrVzF5YAjv/3KE8qpu0DV31So4dcp4s7eWMWMgOho+/9x659TOoBOHZtHRAnPi8Gtb4sgrqeCV75M5Py6U2yf1Ob3j00+Nm6G33mqNMDu9sVFjmRwzmZdPraE8cTA88wx3jetFXmklSzYft3d4trdkiTHFyCQrLtolcrq5qqDAeufV6ujEoVlUW+Po5derTce/uf4QZVU1/OmSuNPToytlTEQ3fDiMHGmtUDu9Jyc9SXpxOm/eMwYOHmT0f5cxOjaQf649SElFtb3Ds52SEli50uhC29z06a01b55urrIhnTg0i44WHCXUOxRP19Z3jywqr+KTTce4LDGSfqH1lvz8739h71544IGWz3zaDUyJncKFfS7kL/nLKZ4yDnn6af7fxEhySyp5e+Ph5k/QWX39NZSXW6c31dnGjIFevXRzlY3oxKFZdLTwaJubqZZvO2F5QN9rr0FoaOtmPu0mnj//eXJP5fLqrUMgJ4ehn77FpUPCeWvj4a67tOxnnxmr+I23wRT6urnKpnTi0Cw6Wni0zTfGP/39OAkRvgyNqjeg79Ah+OYbuOOO1s982g2MjhzNZXGX8fKxRZy89nL429/4f4k+OAn86ctddLkVkouKjPEb8+YZXWhtYd48Yxla3VxldTpxaA0opThWeKxNNY6UbGM1uytHRZ+59OvrrxvdLe/qulOmt9ezU56lpLKEZ2b5gslE+EvP8ujFcfx4MJfPtnSxG+UrVkBFhXV7U51t9GijuWrJEttdo5vSiUNrILs0m/Lq8jYljlW7MhGB6YPDTm8sKIB33zU+AYbbaKbdLmBw6GDuGHkHb+z/iF1/XAAffcSCquOc2yeIp1bs6VpTkXz2mTHCe+xY211DxEhMa9ZAfjdf68TKdOLQGmjPGI5VuzIY1TuAnr71ZtR9/XUoLjbWhtaa9Nz5z+Hv4c99fQ+goqNwuvce/jFvMD4ertz18VaKy6vsHWL7FRTAd98Zb+q2aqaqdeWVRnPV8uW2vU43oxOH1kBbx3Ck5payP7OYiwfXq1WUlBjLgV56KSQmWi/ILirQM5C/XPAXNqT9xOKnr4Bduwj94B1ev3o4x06e4uHPd3T+JWaXLzfezG3ZTFVr1CiIiTFqOJrV2DRxiMh0EUkWkRQRWWhhv7uILDHv3yQiMfX2PWbeniwi0+ptPyIiu0Rku4hssWX83dWRgiNA62sca/cb6z1Pje95euPbb0NeHjz+uLXC6/JuGX4L50Scw/15H5M9Ywo8+SRjPCp47OI4Vu/J4s0Nh+wdYvt89pmxWNPo0ba/Vv3mqpMnbX+9bsJmiUNEnIE3gIuBeOBqEYk/q9gtQL5Sqh/wd+Al87HxwHwgAZgO/Mt8vlpTlFKJSqlRtoq/OzuUf4ggzyD8PfxbddzGAzn0CfEmOtDL2FBRAS+/DFOmwLnnWj/QLsrZyZn/zP4PxRXF3HGZC6qyAh56iFsmxDJrWAQvf5/MuuRse4fZNvn58P33xpt5R43lufJKqK6Gr77qmOt1A7ascYwGUpRSh5VSlcBiYPZZZWYD5oWGWQpcIEZXnNnAYqVUhVIqFUgxn0/rAAdPHqRfYL9WHVNeVcOm1Dwm9Q85vfG99yA9Hf70JytH2PUlhCbw3PnP8VXaGj760wxYsgT56itemjuUuDBfHli0jSO5pfYOs/W+/NJ4E++IZqpaI0ZAnz66ucqKbJk4IoH6fQjTzNssllFKVQOFQFAzxyrgexFJEpHbbRB3t5dyMoX+Qf1bdczvqScprzJx3gBz4jh1Cp59FiZMgAsusEGUXd8fxv6Bib0mcrfLavZOjIO77sKzuIC3rhuJk5Nwx0dJlHa2KUk++8x4E+/IKWdqm6t++MFoNtXarTPeHJ+glBqB0QR2j4hYnB1NRG4XkS0isiUnJ6djI+zEyqvLOV54nH4BratxbDyQg5uzE2P6BBob/vlPYz3tF17Q04u0kbOTM4uvWIy3mzdzZ1dQXJwLDzxAdKAX/7x6OAezi3niq932DrPl8vKMN++ObKaqdeWVUFNj1Hi0drNl4jgBRNf7Psq8zWIZEXEB/IC8po5VStV+zQa+pJEmLKXUW0qpUUqpUSEhIZaKaBYczj+MQrW6xrHxYA7nxAbg5eZidLd86SW45BKjxqG1WYRPBEuuWMKB0qPc/D8DMX36CSxfzsT+Idx7fn+WbTvBmr1Z9g6zZZYtM968O7KZqlZiIvTrp5urrMSWiWMz0F9EYkXEDeNm99lj/1cAN5ifXwGsVcbcCiuA+eZeV7FAf+B3EfEWER8AEfEGLgI60Ucux5dyMgWgVfc4MgrLOJBVcvr+xssvGzdBn3/eFiF2O5NjJvPShS+xlL08dVUo3H47ZGZy75R+DAr35U9f7uocKwZ+/DEMHGifbtm1zVVr14JugWg3myUO8z2Le4HVwD7gM6XUHhH5s4jMMhd7FwgSkRTgIWCh+dg9wGfAXuA74B6lVA3QE/hJRHYAvwMrlVLf2epn6I5qE0f/wJbXOH48kAvAeQND4NgxeOUVYyJDPW7Dav547h+5ZfgtPBeXzYe98uGGG3BzgpfnDeVkaSUvf59s7xCblpoKGzfC9dfbr+lSN1dZjZXWarRMKbUKWHXWtifrPS8H5jVy7PPA82dtOwwMs36kWq3k3GQCPQMJ8Axo8TEbDubQ09edgT194Jo7jHU3XnzRhlF2PyLCvy79F6kFqdw6YwMx//meSa+8QsLDD3P9ub354JcjXD26FwkRfs2fzB4+/tj4umCB/WIYOtSo8Xz8sVFr09qsM94c12xoT84eEkISWly+xqT4OSWXCf1CkJ9+gsWL4X/+xxjgpVmVm7MbS+ctpU9QP+Zc50bK/y6EzZt58MIB+Hu58cyKvY45i65S8OGHxnieXm1bGMwqRIwaz48/GrM1a22mE4dWRynFnpw9DA4d3OJjdp8opOBUFZP6BhgLNEVFGYlDs4kAzwBWXrMS6dGDGdcKBQuuwK+smEemDeT3Iyf5ZmeGvUNs6NdfISXFeNO2t+uuMxLIhx/aO5JOTScOrU56cToF5QWtqnH8eNC40XjBryth2zb43/8Fb29bhagBfQP7smz+lxwKUFw95jg1117DlcMjiA/35cVv91NeVWPvEM/0/vvg6Qlz59o7EoiOhgsvhA8+AJPJ3tF0WjpxaHX25OwBaFWNY+OBXCb4VNPjycdh4kS9ul8HmdR7Em/O+D++66v4H9NqnJ9+iv83YxAnCsp496dUe4d3WmEhfPopXH01+Pg0X74j3HgjHD0KGzbYO5JOSycOrc7ubKNnc0Joy2ocxeVVbD2Wz5/WvAWlpfDWW3qwXwe6dcSt3D/6fl4ZB+99+xfG7djI1Pie/GtdCtnF5fYOz/DRR8b/DUdawOuyy8DXF/7zH3tH0mnpxKHV2ZO9h1DvUIK9gltU/rfDJ5lw8HfiN6wyZr+Ni7NxhNrZ/jbtb0yNuYA7Zwo/P3YtT0eWU1lj4m+rD9g7NOOm+L/+BeecY0xv7ii8vOCaa4zBgHoKkjbRiUOrk5SRRGJYYovLb9p5lL98/yam+HhY2GDWfK0DuDi5sOTKz4kJiGXuZZVwx+Xc39eNz5KOsye90L7BbdgA+/bB3XfbNw5L7rnHmL353XftHUmnpBOHBsCpqlPszt7NORHntPiYYa8+S1hxDk5vvQVubjaMTmtKgGcAy6/9hlO+nsyZepKbX7mXKCp47pt99u2e+7e/QVAQXHWV/WJozODBMHmyUSOqcbDOBJ2AThwaANszt1OjalqcOLI++ZyZm75hz9W3wfjxNo5Oa86gkEF8NPcTNofVcM+AA3z2/f+yNTndfvNY7doF33xjdNH29LRPDM25917jJvnKlfaOpNPRiUMDYPOJzQCcE9mCxJGdje99d7E3NBb/l/UIcUcxO242T5/3NB8MU3zuuokPv/0r/7tiJ5XVduh2+tJLRrfse+7p+Gu31OzZRvfcl1+2dySdjk4cGgCb0zcT4RNBhE9E0wVNJrj5ZpyLi/nHjU8SHd7yqUk023vivCeYPXA2D13sRFnZb/zx/Wf46MeUjg1i3z5YtAjuuAMCAzv22q3h4gKPPGKMJN+40d7RdCo6cWgA/HL8F8ZEjmm+4AsvwMqVPD/lJgZeoJeDdTRO4sSHcz5kQPBA5t3kTXzWL0Tcfzt5BR24WuBjjxm1jc7QYeLWW6FnT2PRMa3FdOLQOFJwhNSCVKbETGm64Jo18MQTHJs2mw+Gz2BqfFjHBKi1iq+7L1/N/4oqNxdmPhjOeckbOHb+JaiyMttf/OefYflyePRR6Azr4Hh6wsMPGwtM/fqrvaPpNHTi0FiXug6AKbFNJI7UVGP0b3w8z1/2ByL8PRkc6dtBEWqtNSBoAJ/O/ZTdkslljwwhcdtGsiacD0VFtrtoZaUx0C8yEh580HbXsbY774TQUKPZyhEniXRAOnFo/JD6AyFeIY3PUZWfb6zmV1PDyQ8X88OxUmYPj0T0KHGHdkn/S3j+/OdZ47aLWQ+dT9D2zZw6d7zRk8gW/vpXozfVv/7VueYr69EDnnvOqC0tXWrvaDoFnTi6uaqaKr49+C0X97/YciKoqIA5c+DwYVi+nK9OeVNjUlw+PLLjg9VabeGEhVw39Dq+8V3LRfddTM3hVGpGjrL+zeDff4c//xnmzYNZs5ov72huvhmGDDFqHSUl9o7G4enE0c1tPLqR/PJ85sTNabizogKuuMIYAfz++6iJE/liaxqDI33p39NBJqzTmiQivDf7PeYOmsv6gG8Yf89s0pw8URdcYHRDtcYMsTk5xv+T8HB48832n88enJ2NmtKxY8bNfa1JOnF0c0v3LsXTxZOL+l505o7KSmOpzW++Md4Mrr6apKP57Ekv4qpz7LgYj9ZqLk4ufDr3U2YOmMkun0+ZeMe5bBgw2vh0fcEF7Wu6KiqCGTMgOxu++MIYKd5ZTZgA998Pr79urE2uNUonjm7sVNUpPt39KVfEX4GXq9fpHYWFxpvBihXGp7A77wTgvZ9T8fN0Ze4I3UzV2bg5u/HFlV9wU+JNHHf5jCuu8uLRWfdQ/ftmSEiAv/wFyls5o25GhrG2xdatxoSBI0faJviO9Je/GJN1zp8Px4/bOxqHpRNHN7Zk9xKKKoq4dcStpzceO2Z88lq3zph22jwddnJmMd/tzuSaMb3wcrPpUvWajbg6u/LurHd5ZvIznFT/5c1zvmXMTY9xcNhYY3bjgQPhH/+A4uKmT6QUfPkljBgBe/caNY3OeF/DEi8v42crL4fLL9f3OxohDrlGsZWNGjVKbdmyxd5hOJQaUw3x/4rHw8WD7XdsN26Mf/kl3HKLMenbsmVGM4bZrR9sZlPqSTY+MoUAbz2hYWe3OmU1C5YtoLC8hB6V13BXUQJPbPocj99/M9aqmDnT6Ek3fDiEhRnrrBw7BuvXG2tsbNkCw4YZS7AOHWrvH8f6vv7a6BQycaIxl5WXV/PHdEEikqSUajgnvlKqyz9GjhyptDO9k/SO4mnU0j1LlcrJUermm5UCpUaOVOrAgTPKfrc7Q/V+9Bv1xrqDdopWs4W0wjQ189OZiqdRHk/1UzFP/E19+84yZbrxRqWCgoz/D5Ye8fFK/fvfSlVW2vtHsK1PPlFKRKlzz1UqK8ve0dgFsEVZeE/VNY5u6ETRCYa8OYTBwfGsL56L05+fNZon/vhHo0tlvSnS0wvKmPnPnwj392DZXeNxc9Gtm12JUoov9n3BvavuI6s0E8+acxgfcg9vzp1Fv+yjxriMvDyjFhoZaSzK1LevvcPuOEuXwvXXQ3Cw0XRbrxbeHTRW49CJo5vJL8tn6rvnkZyXzJZPvBl4MN/4Y3jtNYiPP6NsTnEF17z9G5mF5Sy7e5zugtuFlVaW8tqm13h+44uUVhfhaUpkdr9b+Pusmwnz657NNHWSkoxZEw4ehAUL4KmnoF8/e0fVIRpLHDb9+Cgi00UkWURSRKTBjGci4i4iS8z7N4lITL19j5m3J4vItJaeU7OgpgaSkkh64T7GPxXFzuxdLP6kkoH9xhoDwdasaZA0fjyYw6zXf+J4/ineun6UThpdnLebN49NfIzjDx3hsfFP4+yWzuLD9xH9916MeWMBHyZ9S2VNpb3DtI+RI2H7dmPSxi++MHpdzZpl1EYKCuwdnV3YrMYhIs7AAWAqkAZsBq5WSu2tV+ZuYKhS6k4RmQ/MUUpdJSLxwCJgNBAB/AAMMB/W5Dkt6bI1DqWM3h8lJcajuBiysiAtDZWWxslj+zmWup3NpQdZ1q+K7/tCSIULSzyvY/J1T0BsLAA1JkVeaQXHT5ax7Vg+X+/MYMfxAmKCvHj9mhEMjvSz8w+qdbSqmir+/ftiXv3lPxwu/gklVTjhRpT3UIaEDmdY2CBGRAyib1AkEb49CfQMxMWpG/S2y8w0ep59+CGkp4OTk9G7LDHR+PDVp48x71XPnkbzlqcnuLraO+o26/CmKhE5F3haKTXN/P1jAEqpF+qVWW0u86uIuACZQAiwsH7Z2nLmw5o8pyVtThyzZhnV09rXqPb2YGPPG9n3z0HF/GtQCUrAhKJGKeq/6kpAYX6IMn89cx91+0GhTu+vf3y9sqVuUOV8+hq+lT4Eqql4uy/ASXkZ5cyFT1XVUGM6HdHAnj5cPTqaq8f0wt2l3km0bul4QR5/Xf85aw9v5FDRFio4hpKKBuWccEdwwQlXnMQFwRU5o1FDzvjacIKbevul9ln75kML9/Pg3dn/ZlLvSe06TwM1NcbcVmvXGjMr7N4NubmWyzo7g4eHkUTc3IxkI3L6a1PPrWHbNnB3b9OhjSUOW35EiATqj6BJA85e8KGujFKqWkQKgSDz9t/OOrZ21Flz5wRARG4Hbgfo1auNI5379TN+4fV/ic09t7Av1DuVIW5HERGqaxSFp6qQ2j8JcxlRxve1fygigigx/w0Zz83f1h1rcnZFObtgcnbF5GJ8rXb3pNLDCzwD8PYIo4drCGGegwh0j8ZJ5Iz5qMR8bk83J3r6ehDu58nQKD96+nq07fXSuqRo/yBeu+xO4E5qTIpjJ0v49cgBdmUnk1mcTU5pDsWVBVTUnKLKVEmVqYpqUyU1qgqFeUqTsz6gqnr/Gs/UGXssHUPDEs0aEOhLD7cerTiihZydYdIk41ErJ8fospydbTxyc6GszGgVqP1aXn7mB02TqfHn1mKDyUi7bN1SKfUW8BYYNY42neSVV6wSy1Xmh6Z1ds5OQmywD7HBI4EuMFLcmkJCOscaJFZgy5vjJ4Doet9HmbdZLGNuqvID8po4tiXn1DRN02zIloljM9BfRGJFxA2YD6w4q8wK4Abz8yuAteZBJyuA+eZeV7FAf+D3Fp5T0zRNsyGbNVWZ71ncC6wGnIH3lFJ7ROTPGKMRVwDvAh+JSApwEiMRYC73GbAXqAbuUUrVAFg6p61+Bk3TNK0hPQBQ0zRNs8guAwA1TdO0rkcnDk3TNK1VdOLQNE3TWkUnDk3TNK1VusXNcRHJAVq7sHIw0MgcAnbnqLE5alygY2sLR40LHDc2R40L2hZbb6VUg1GN3SJxtIWIbLHUm8AROGpsjhoX6NjawlHjAseNzVHjAuvGppuqNE3TtFbRiUPTNE1rFZ04GveWvQNogqPG5qhxgY6tLRw1LnDc2Bw1LrBibPoeh6ZpmtYqusahaZqmtYpOHJqmaVqr6MRxFhF5WkROiMh28+OSevseE5EUEUkWkWkdHNdfRWS/iOwUkS9FxN+8PUZEyurF+38dGVe9+KabX5cUEVlojxjMcUSLyDoR2Ssie0TkAfP2Rn+vHRzfERHZZY5hi3lboIisEZGD5q8BdohrYL3XZruIFInIg/Z43UTkPRHJFpHd9bZZfI3E8Jr5/91OERlhh9gc4m+zkdhs836mlNKPeg+Mtc0ftrA9HtgBuAOxwCHAuQPjughwMT9/CXjJ/DwG2G3n18zZ/Hr0AdzMr1O8nWIJB0aYn/sAB8y/O4u/VzvEdwQIPmvb/wILzc8X1v5u7fz7zAR62+N1AyYBI+r/v27sNQIuAb7FWFV5LLDJDrE5xN9mI7HZ5P1M1zhabjawWClVoZRKBVKA0R11caXU90qpavO3v2GsfugoRgMpSqnDSqlKYDHG69XhlFIZSqmt5ufFwD5Or1fvqGYDH5iffwBcZr9QALgAOKSUau1sC1ahlNqIsT5PfY29RrOBD5XhN8BfRMI7MjZH+dts5HVrTLvez3TisOxec7XzvXrNBpHA8Xpl0rDfG9LNGJ+yasWKyDYR2SAiE+0QjyO9NnVEJAYYDmwyb7L0e+1oCvheRJJE5Hbztp5KqQzz80ygp31CqzMfWFTve0d43Rp7jRzt/56j/W2CDd7PumXiEJEfRGS3hcds4E2gL5AIZAB/c5C4ass8jrEq4ifmTRlAL6XUcOAh4FMR8e2omB2ViPQAvgAeVEoVYcff61kmKKVGABcD94jIpPo7ldGOYLc+8mIsyTwL+Ny8yVFetzr2fo0a46B/mzb5/dls6VhHppS6sCXlRORt4BvztyeA6Hq7o8zbOiwuEbkRmAFcYP7jQSlVAVSYnyeJyCFgANCRSx7a/LVpDRFxxUganyillgEopbLq7a//e+1QSqkT5q/ZIvIlRvNAloiEK6UyzM0s2faIzexiYGvt6+UorxuNv0YO8X/PUf82m/j9tet165Y1jqac1T46B6jtobACmC8i7iISC/QHfu/AuKYD/wPMUkqdqrc9RESczc/7mOM63FFxmW0G+otIrPkT63yM16vDiYhgrGW/Tyn1Sr3tjf1eOzI2bxHxqX2OcVN1N8ZrdYO52A3A8o6OrZ6rqddM5Qivm1ljr9EK4Hpz76qxQGG9Jq0O4ch/mzZ7P+uoO/6d5QF8BOwCdppf3PB6+x7H6H2QDFzcwXGlYLRJbjc//s+8fS6wx7xtKzDTTq/bJRg9mA4Bj9vx9zcBoxljZ73X6pKmfq8dGFsfjJ4sO8y/s8fN24OA/wIHgR+AQDu9dt5AHuBXb1uHv24YiSsDqMJoe7+lsdcIozfVG+b/d7uAUXaIzSH+NhuJzSbvZ3rKEU3TNK1VdFOVpmma1io6cWiapmmtohOHpmma1io6cWiapmmtohOHpmma1io6cWiapmmtohOHpmma1ir/Hw2PZqQirD71AAAAAElFTkSuQmCC",
      "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_99'].plot(kind='kde', ax=ax, color='red')\n",
    "\n",
    "# variable imputed with the mean\n",
    "X_train['Age_minus1'].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": 13,
   "id": "9bd09a57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c5fe8fe580>"
      ]
     },
     "execution_count": 13,
     "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_999'].plot(kind='kde', ax=ax, color='red')\n",
    "\n",
    "# variable imputed with the mean\n",
    "X_train['Fare_minus1'].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": 14,
   "id": "477edd44",
   "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>Family</th>\n",
       "      <th>Age_99</th>\n",
       "      <th>Age_minus1</th>\n",
       "      <th>Fare_999</th>\n",
       "      <th>Fare_minus1</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>162.793430</td>\n",
       "      <td>63.321188</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>-101.671097</td>\n",
       "      <td>125.558364</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>-7.387287</td>\n",
       "      <td>-4.149246</td>\n",
       "      <td>11.528625</td>\n",
       "      <td>16.553989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_99</th>\n",
       "      <td>204.349513</td>\n",
       "      <td>-101.671097</td>\n",
       "      <td>-7.387287</td>\n",
       "      <td>951.727557</td>\n",
       "      <td>-189.535540</td>\n",
       "      <td>-159.931663</td>\n",
       "      <td>-94.317400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_minus1</th>\n",
       "      <td>204.349513</td>\n",
       "      <td>125.558364</td>\n",
       "      <td>-4.149246</td>\n",
       "      <td>-189.535540</td>\n",
       "      <td>318.089620</td>\n",
       "      <td>257.379887</td>\n",
       "      <td>114.394141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare_999</th>\n",
       "      <td>162.793430</td>\n",
       "      <td>2448.197914</td>\n",
       "      <td>11.528625</td>\n",
       "      <td>-159.931663</td>\n",
       "      <td>257.379887</td>\n",
       "      <td>47219.202652</td>\n",
       "      <td>762.474982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare_minus1</th>\n",
       "      <td>63.321188</td>\n",
       "      <td>2448.197914</td>\n",
       "      <td>16.553989</td>\n",
       "      <td>-94.317400</td>\n",
       "      <td>114.394141</td>\n",
       "      <td>762.474982</td>\n",
       "      <td>2378.567678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Age         Fare     Family      Age_99  Age_minus1  \\\n",
       "Age          204.349513    70.719262  -6.498901  204.349513  204.349513   \n",
       "Fare          70.719262  2448.197914  17.258917 -101.671097  125.558364   \n",
       "Family        -6.498901    17.258917   2.735252   -7.387287   -4.149246   \n",
       "Age_99       204.349513  -101.671097  -7.387287  951.727557 -189.535540   \n",
       "Age_minus1   204.349513   125.558364  -4.149246 -189.535540  318.089620   \n",
       "Fare_999     162.793430  2448.197914  11.528625 -159.931663  257.379887   \n",
       "Fare_minus1   63.321188  2448.197914  16.553989  -94.317400  114.394141   \n",
       "\n",
       "                 Fare_999  Fare_minus1  \n",
       "Age            162.793430    63.321188  \n",
       "Fare          2448.197914  2448.197914  \n",
       "Family          11.528625    16.553989  \n",
       "Age_99        -159.931663   -94.317400  \n",
       "Age_minus1     257.379887   114.394141  \n",
       "Fare_999     47219.202652   762.474982  \n",
       "Fare_minus1    762.474982  2378.567678  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bfa51fb6",
   "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>Family</th>\n",
       "      <th>Age_99</th>\n",
       "      <th>Age_minus1</th>\n",
       "      <th>Fare_999</th>\n",
       "      <th>Fare_minus1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.092644</td>\n",
       "      <td>-0.299113</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.051179</td>\n",
       "      <td>0.084585</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>-0.066273</td>\n",
       "      <td>0.142022</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>-0.299113</td>\n",
       "      <td>0.208268</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.144787</td>\n",
       "      <td>-0.140668</td>\n",
       "      <td>0.032079</td>\n",
       "      <td>0.205233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_99</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.066273</td>\n",
       "      <td>-0.144787</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344476</td>\n",
       "      <td>-0.023857</td>\n",
       "      <td>-0.062687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_minus1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.142022</td>\n",
       "      <td>-0.140668</td>\n",
       "      <td>-0.344476</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066411</td>\n",
       "      <td>0.131514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare_999</th>\n",
       "      <td>0.051179</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032079</td>\n",
       "      <td>-0.023857</td>\n",
       "      <td>0.066411</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.071946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare_minus1</th>\n",
       "      <td>0.084585</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.205233</td>\n",
       "      <td>-0.062687</td>\n",
       "      <td>0.131514</td>\n",
       "      <td>0.071946</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Age      Fare    Family    Age_99  Age_minus1  Fare_999  \\\n",
       "Age          1.000000  0.092644 -0.299113  1.000000    1.000000  0.051179   \n",
       "Fare         0.092644  1.000000  0.208268 -0.066273    0.142022  1.000000   \n",
       "Family      -0.299113  0.208268  1.000000 -0.144787   -0.140668  0.032079   \n",
       "Age_99       1.000000 -0.066273 -0.144787  1.000000   -0.344476 -0.023857   \n",
       "Age_minus1   1.000000  0.142022 -0.140668 -0.344476    1.000000  0.066411   \n",
       "Fare_999     0.051179  1.000000  0.032079 -0.023857    0.066411  1.000000   \n",
       "Fare_minus1  0.084585  1.000000  0.205233 -0.062687    0.131514  0.071946   \n",
       "\n",
       "             Fare_minus1  \n",
       "Age             0.084585  \n",
       "Fare            1.000000  \n",
       "Family          0.205233  \n",
       "Age_99         -0.062687  \n",
       "Age_minus1      0.131514  \n",
       "Fare_999        0.071946  \n",
       "Fare_minus1     1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "004a5ba5",
   "metadata": {},
   "source": [
    "## Using Sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "de7c1a09",
   "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": 17,
   "id": "6dd64028",
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer1 = SimpleImputer(strategy='constant',fill_value=99)\n",
    "imputer2 = SimpleImputer(strategy='constant',fill_value=999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a6db3384",
   "metadata": {},
   "outputs": [],
   "source": [
    "trf = ColumnTransformer([\n",
    "    ('imputer1',imputer1,['Age']),\n",
    "    ('imputer2',imputer2,['Fare'])\n",
    "],remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1029314e",
   "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;,\n",
       "                                 SimpleImputer(fill_value=99,\n",
       "                                               strategy=&#x27;constant&#x27;),\n",
       "                                 [&#x27;Age&#x27;]),\n",
       "                                (&#x27;imputer2&#x27;,\n",
       "                                 SimpleImputer(fill_value=999,\n",
       "                                               strategy=&#x27;constant&#x27;),\n",
       "                                 [&#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;,\n",
       "                                 SimpleImputer(fill_value=99,\n",
       "                                               strategy=&#x27;constant&#x27;),\n",
       "                                 [&#x27;Age&#x27;]),\n",
       "                                (&#x27;imputer2&#x27;,\n",
       "                                 SimpleImputer(fill_value=999,\n",
       "                                               strategy=&#x27;constant&#x27;),\n",
       "                                 [&#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(fill_value=99, strategy=&#x27;constant&#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(fill_value=999, strategy=&#x27;constant&#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-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',\n",
       "                                 SimpleImputer(fill_value=99,\n",
       "                                               strategy='constant'),\n",
       "                                 ['Age']),\n",
       "                                ('imputer2',\n",
       "                                 SimpleImputer(fill_value=999,\n",
       "                                               strategy='constant'),\n",
       "                                 ['Fare'])])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trf.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f18e627b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([99.])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trf.named_transformers_['imputer1'].statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "24f6fce7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([999.])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trf.named_transformers_['imputer2'].statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e51edc12",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = trf.transform(X_train)\n",
    "X_test = trf.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fc893f1e",
   "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",
       "       [ 99.    , 221.7792,   0.    ],\n",
       "       [ 99.    ,  25.925 ,   0.    ]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  }
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