{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mjwen/chee6397/blob/main/lab/lab3-linear_models.ipynb)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "61b1446e452885a9"
  },
  {
   "cell_type": "markdown",
   "id": "db39781fdd445afb",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Lab 3: Data Processing & Linear Models\n",
    "\n",
    "This notebook is intended to get you familiar with the below topics:\n",
    "\n",
    "- Load, explore and split data\n",
    "- Using the scikit-learn library to train models (linear regression for example)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9c096dfa393039f",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Load the real estate valuation dataset\n",
    "\n",
    "This data set consists of the market historical data set of real estate valuation collected from Sindian Dist., Taipei City.\n",
    "\n",
    "More on the dataset: https://archive.ics.uci.edu/dataset/477/real+estate+valuation+data+set\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66079dee",
   "metadata": {},
   "source": [
    "### Load the dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad7946539566fe1b",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.183268Z",
     "start_time": "2023-09-10T22:49:24.937649Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"./data/real_estate_valuation_dataset.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3061f14f",
   "metadata": {},
   "source": [
    "### Explore the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1cdf1516f81b952b",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.193065Z",
     "start_time": "2023-09-10T22:49:25.185064Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "   No  X1 transaction date  X2 house age  \\\n0   1          2012.916667          32.0   \n1   2          2012.916667          19.5   \n2   3          2013.583333          13.3   \n3   4          2013.500000          13.3   \n4   5          2012.833333           5.0   \n\n   X3 distance to the nearest MRT station  X4 number of convenience stores  \\\n0                                84.87882                               10   \n1                               306.59470                                9   \n2                               561.98450                                5   \n3                               561.98450                                5   \n4                               390.56840                                5   \n\n   X5 latitude  X6 longitude  Y house price of unit area  \n0     24.98298     121.54024                        37.9  \n1     24.98034     121.53951                        42.2  \n2     24.98746     121.54391                        47.3  \n3     24.98746     121.54391                        54.8  \n4     24.97937     121.54245                        43.1  ",
      "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>No</th>\n      <th>X1 transaction date</th>\n      <th>X2 house age</th>\n      <th>X3 distance to the nearest MRT station</th>\n      <th>X4 number of convenience stores</th>\n      <th>X5 latitude</th>\n      <th>X6 longitude</th>\n      <th>Y house price of unit area</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2012.916667</td>\n      <td>32.0</td>\n      <td>84.87882</td>\n      <td>10</td>\n      <td>24.98298</td>\n      <td>121.54024</td>\n      <td>37.9</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2012.916667</td>\n      <td>19.5</td>\n      <td>306.59470</td>\n      <td>9</td>\n      <td>24.98034</td>\n      <td>121.53951</td>\n      <td>42.2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2013.583333</td>\n      <td>13.3</td>\n      <td>561.98450</td>\n      <td>5</td>\n      <td>24.98746</td>\n      <td>121.54391</td>\n      <td>47.3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2013.500000</td>\n      <td>13.3</td>\n      <td>561.98450</td>\n      <td>5</td>\n      <td>24.98746</td>\n      <td>121.54391</td>\n      <td>54.8</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2012.833333</td>\n      <td>5.0</td>\n      <td>390.56840</td>\n      <td>5</td>\n      <td>24.97937</td>\n      <td>121.54245</td>\n      <td>43.1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5755be27",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.200693Z",
     "start_time": "2023-09-10T22:49:25.192707Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "414"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "129808bc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.201044Z",
     "start_time": "2023-09-10T22:49:25.197029Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0      37.9\n1      42.2\n2      47.3\n3      54.8\n4      43.1\n       ... \n409    15.4\n410    50.0\n411    40.6\n412    52.5\n413    63.9\nName: Y house price of unit area, Length: 414, dtype: float64"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Y house price of unit area\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e07a921a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.233779Z",
     "start_time": "2023-09-10T22:49:25.202100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "               No  X1 transaction date  X2 house age  \\\ncount  414.000000           414.000000    414.000000   \nmean   207.500000          2013.148953     17.712560   \nstd    119.655756             0.281995     11.392485   \nmin      1.000000          2012.666667      0.000000   \n25%    104.250000          2012.916667      9.025000   \n50%    207.500000          2013.166667     16.100000   \n75%    310.750000          2013.416667     28.150000   \nmax    414.000000          2013.583333     43.800000   \n\n       X3 distance to the nearest MRT station  \\\ncount                              414.000000   \nmean                              1083.885689   \nstd                               1262.109595   \nmin                                 23.382840   \n25%                                289.324800   \n50%                                492.231300   \n75%                               1454.279000   \nmax                               6488.021000   \n\n       X4 number of convenience stores  X5 latitude  X6 longitude  \\\ncount                       414.000000   414.000000    414.000000   \nmean                          4.094203    24.969030    121.533361   \nstd                           2.945562     0.012410      0.015347   \nmin                           0.000000    24.932070    121.473530   \n25%                           1.000000    24.963000    121.528085   \n50%                           4.000000    24.971100    121.538630   \n75%                           6.000000    24.977455    121.543305   \nmax                          10.000000    25.014590    121.566270   \n\n       Y house price of unit area  \ncount                  414.000000  \nmean                    37.980193  \nstd                     13.606488  \nmin                      7.600000  \n25%                     27.700000  \n50%                     38.450000  \n75%                     46.600000  \nmax                    117.500000  ",
      "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>No</th>\n      <th>X1 transaction date</th>\n      <th>X2 house age</th>\n      <th>X3 distance to the nearest MRT station</th>\n      <th>X4 number of convenience stores</th>\n      <th>X5 latitude</th>\n      <th>X6 longitude</th>\n      <th>Y house price of unit area</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>414.000000</td>\n      <td>414.000000</td>\n      <td>414.000000</td>\n      <td>414.000000</td>\n      <td>414.000000</td>\n      <td>414.000000</td>\n      <td>414.000000</td>\n      <td>414.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>207.500000</td>\n      <td>2013.148953</td>\n      <td>17.712560</td>\n      <td>1083.885689</td>\n      <td>4.094203</td>\n      <td>24.969030</td>\n      <td>121.533361</td>\n      <td>37.980193</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>119.655756</td>\n      <td>0.281995</td>\n      <td>11.392485</td>\n      <td>1262.109595</td>\n      <td>2.945562</td>\n      <td>0.012410</td>\n      <td>0.015347</td>\n      <td>13.606488</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>2012.666667</td>\n      <td>0.000000</td>\n      <td>23.382840</td>\n      <td>0.000000</td>\n      <td>24.932070</td>\n      <td>121.473530</td>\n      <td>7.600000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>104.250000</td>\n      <td>2012.916667</td>\n      <td>9.025000</td>\n      <td>289.324800</td>\n      <td>1.000000</td>\n      <td>24.963000</td>\n      <td>121.528085</td>\n      <td>27.700000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>207.500000</td>\n      <td>2013.166667</td>\n      <td>16.100000</td>\n      <td>492.231300</td>\n      <td>4.000000</td>\n      <td>24.971100</td>\n      <td>121.538630</td>\n      <td>38.450000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>310.750000</td>\n      <td>2013.416667</td>\n      <td>28.150000</td>\n      <td>1454.279000</td>\n      <td>6.000000</td>\n      <td>24.977455</td>\n      <td>121.543305</td>\n      <td>46.600000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>414.000000</td>\n      <td>2013.583333</td>\n      <td>43.800000</td>\n      <td>6488.021000</td>\n      <td>10.000000</td>\n      <td>25.014590</td>\n      <td>121.566270</td>\n      <td>117.500000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4a41740d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.454208Z",
     "start_time": "2023-09-10T22:49:25.213248Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def plot_histogram(data):\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.hist(data, bins=20)\n",
    "    ax.set_xlabel(\"Value\")\n",
    "    ax.set_ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "86b88c1d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.529364Z",
     "start_time": "2023-09-10T22:49:25.453142Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram(df[\"X2 house age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c48e026c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.609994Z",
     "start_time": "2023-09-10T22:49:25.531417Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram(df[\"Y house price of unit area\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f4653a8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.610241Z",
     "start_time": "2023-09-10T22:49:25.605498Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_scatter(X, Y):\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.scatter(X, Y)\n",
    "    ax.set_xlabel(\"Horizonal\")\n",
    "    ax.set_ylabel(\"Vertical\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "05d3cfa9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.675755Z",
     "start_time": "2023-09-10T22:49:25.608604Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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ZFIFJERERkRWsToDN/vzWevUZERERyRUa4tBu2X0gnFNEREREBPYUERER1cN5T80TkyIiIqJaJG5kSubg8BkREdF/Sd3IlMzBpIiIiAiyNzIlczApIiIiQuM2MqXrE5MiIiIiyN7IlMzBpIiIiAiyNzIlczApIiIiguyNTMkcTIpIa9VuA5m5xXgv+yQyc4s5wZGILCN5I1MyB+sUkbZYK4SImtrI1AQsfqxfvWuPi9eeZoEbwoIbwurIUyuk7pvTc3+2+LF+vDgRkWVY0VoGbghL171gtUIcqKkVMjzFxYsUUR38MDeHtI1MyRxMikg7jakVwosW0RVShpyZuJGumBSRdlgrhKjx/A05e7an0GXIWUriRs0TV5+RdlgrhKhxpGxPwX3FSHdMikg7rBVC1DgStqeQkrhR88akiLTDWiFEjSNhyFlC4kakNKeob9++cDjUPoD27t17TQ0iAlgrhKgxJAw5S0jciJSSojFjxljcDKL6RqYmYHiKi6tUiILwDDkXll5qcHjKgZobCjuHnCUkbkRKSdHs2bOtbgdRg1grhCg4z5Dzk6sa7qk3YP+Qs4TEjYhzioiIyHKcK0gSNDopqq6uxm9+8xvccsstcLlciI2N9fkiIqKm5VnZ5Y+nCrzdK7s8cwVdTt8hMpezlTZ1lKh5a3TxxldeeQV//OMf8fzzz2PWrFl4+eWXkZ+fj3Xr1uHnP/+5FW0kIqIAJFWB51xB0lmjk6K//vWvWLZsGe677z688sorePTRR9GlSxd8+9vfRlZWFp577jkr2klERH5IW9nFuYKkq0YPnxUWFqJXr14AgBtuuAGlpaUAgPvvvx/vv/++ua0jIqKguLKLyByNToo6dOiAgoKaUuxdu3bFpk2bAAC7d+9GeHi4ua0jIqKgWAWeyByNToq++93v4sMPPwQA/PjHP8asWbPQrVs3TJw4ET/4wQ9MbyAREQXGlV1E5nAYhnFNyxE++eQTfPzxx+jatStGjx5tVruaVFlZGZxOJ0pLSxETE2N3c4iIrgp3oKfmxuzP72tOiq4HTIqI6HpR7Ta4souaDbM/vxu9+iwjIwPx8fH1hsr+/Oc/45tvvsH06dOvuVFERHR1uLKL6Oo1ek7RkiVL0L1793qP9+zZE2+++aYpjSIiIqLAqt0GMnOL8V72SWTmFttenPN60OieosLCQiQk1B+b/ta3vuVdlUZERETW4fwxazS6pygpKQkff/xxvcc//vhjJCYmmtIoImp+eNdLOtH5/bgxpwBPrdpbr4p5YeklPLVqLzbmsIPiajW6p+iHP/whpk2bhqqqKtx1110AgA8//BAvvfQSXnjhBdMbSETXv405BUhffxCFZVcu8q6YVkgfzbteano698J49rlrKEXzPPbKPw5ieIqLE+yvQqOTopdeeglnzpzB008/jcrKSgBAq1atMH36dMycOdP0BhLR9W1jTgGeXLW33uOFZZfw5Kq9eJMbhVIT8vTC1E06PL0wdm9cG2yfO0Cffe4kavTwmcPhwLx58/DNN98gKysL+/fvx5kzZ7gZLBE1WrXbwIw1nweMmbnmc62GLuj6pdoLY+f7sXZvqhlx5KvRPUUeN9xwAwYOHGhmW4iomck6Uoyz5VUBY0rKq5B1pBhDurZtolb5xxpA17dgvTAG7O+FOXO+wtQ48qWUFI0dOxYrVqxATEwMxo4dGzB2zZo1pjSMiK5/mbnFynF2J0U6zzMhcxSdU+tdUY2zQmxUmKlxVpN2I6GUFDmdTjgcNX9ETEyM999ERNdGdRjC3uEz3eeZkDna3qC2qblqnBXaRbcyNc5KEm8klJKi5cuXe/+9YsUKq9pCRM1MWue2WLQlVynOLsHmmTjA1T7XDQk5uupbzOa3otQbiUZPtL7rrrtw9uzZeo+XlZV5l+gTEakY1CUOrSNbBoxpHdkSg2xcRdOYeSY60Lm+ju5OX1Cbh6MaZ4XTinOFVOOsIGHCuj+Nnmi9detW71L82i5duoQdO3aY0igiah5CQxyYO7ZXg0vyPeaO7WVrD4yEeSYeEocrdCJhaEpCGyVMWPdHOSn67LPPvP8+ePAgCgsLvd9XV1dj48aNaN++vbmtIyKymYQPIUDucIVObkmORYKzFQpLLzXYy+EA4HLWTBa2i4Q2SrqRqEt5+KxPnz7o27cvHA4H7rrrLvTp08f71b9/f/zyl7+0pFbRyZMn8dhjjyEuLg6RkZHo06cP9uzZ4/25YRhIT09HYmIiIiIiMGzYMBw4cMD0dhCR+Tzd7P545uvY2c3u+RDy11flQE1vjJ0fQpKHK3QSGuLA7AdSANSfkuP5fvYDKbb2XEpoo5QbiYYoJ0V5eXnIzc2FYRj49NNPkZeX5/06efIkysrK8IMf/MDUxpWUlGDIkCFo2bIl/vnPf+LgwYN47bXX0Lp1a2/M/PnzsWDBAixatAi7d++Gy+XC8OHDce7cOVPbQkTmkzBfR8KHkITXUYqRqQl4/PZk1F1k7XAAj9+erEVvm+5tlHAj4Y9yUtSxY0e0b98eEydORGxsLDp27Oj9SkhIQGhoqOmNmzdvHpKSkrB8+XLccsst6NSpE+6++2506dIFQE0v0cKFC/Hyyy9j7NixSE1NxVtvvYXy8nKsXr3a9PYQkbmkdLOPTE3A4sf6weX0vbN1OVtpMSwl5XWUYGNOAZZuz0PdTjW3ASzdnqfFZqu6t7H2jYQ/dt9I+NOo1WctW7bEe++9Z1Vb6lm/fj0GDBiA733ve2jXrh369u2LZcuWeX+el5eHwsJCjBgxwvtYeHg47rjjDuzatcvv762oqEBZWZnPFxE1PUnd7CNTE7Bz+l14e+og/O6RPnh76iDsnH6X7QkRIOt11FmgYUgPu4chJbQRuNKbVTfvCdGkN8ufRi/JHzNmDNatW2dBU+o7cuQIFi9ejG7duuGDDz7Ak08+ieeeew5/+ctfAMA72Ts+Pt7n/8XHx/tMBK8rIyMDTqfT+5WUlGTdH0FEft2SHKu0JF+XbvbQEAfSusThwT7tkdYlTps7XcnDFTqRMAwpoY2A/94sQ5PeLH8avSS/a9euePXVV7Fr1y70798fUVFRPj9/7rnnTGuc2+3GgAEDMGfOHABA3759ceDAASxevBgTJ070xtWtsG0YRsCq2zNnzsTzzz/v/b6srIyJEV01aWXspeErGZxnuOKpVXvhgG9tQV3mPdWm6zkjYRhSQhslFzxtdFL0xz/+Ea1bt8aePXt8VoEBNcmJmUlRQkICUlJ8xyV79OiBd999FwDgcrkA1PQYJSRc6YorKiqq13tUW3h4OMLD7SvTTtcP1oW5Np/mnVHaEFbHeia68cx7qvt+dGn2ftT5nJEwDCmhjc2iTpFHXl6eFe1o0JAhQ/Dll1/6PHb48GF07NgRAJCcnAyXy4XNmzejb9++AIDKykps27YN8+bNa7J2UvPEujDXTsJdryQjUxMwPMWlZS8MoP85I6EGkIQ2Sj6vGz2nyKOyshJffvklLl++bGZ7fPzkJz9BVlYW5syZg6+//hqrV6/G0qVL8cwzzwCo6ZmaNm0a5syZg7Vr1yInJweTJ09GZGQkxo8fb1m7iIJ1DxvQY7Kj7tpGKW7AqRhH+s57klBLSUL5BQltlHxeNzopKi8vx5QpUxAZGYmePXvi2LFjAGrmEs2dO9fUxg0cOBBr167F22+/jdTUVLz66qtYuHAhvv/973tjXnrpJUybNg1PP/00BgwYgJMnT2LTpk2Ijo42tS1EtQXrHgb0mOyoPSGbW9K1kzJBWPfyC8CVNsbHaNpGwed1o4fPZs6cif3792Pr1q0YOXKk9/F77rkHs2fPxowZM0xt4P3334/777/f788dDgfS09ORnp5u6vNeK10nEpI5CsvUun1V45orCZtbkjkkDanoPgx5hW+vmmHo0TMt+bxudFK0bt06/O1vf8OgQYN8VnilpKQgNzfX1MZJpfNEQjLHGcWTWTWuuWp7g2I3u2Kc1Xizc/UkTBCuzTMMqSN/c7P+U1ahxdwsace6tkYnRd988w3atWtX7/ELFy4EXAbfXOg+kZDMERsVZmpcs6V6Y6vBDTBvdq5N/45tEOJAvbo1tYU4auLIPwnL3SVMBven0XOKBg4ciPfff9/7vScRWrZsGdLS0sxrmUASJhJKU3nZjT/tOIKfv5eDP+04gsrLbrubBED2nZBOTl9Q7GZXjLOK52an7pyYgv/e7OhaiE4ne46WBEyIgJqEac/RkqZpkFAS5mZJmAzuj3JPUXZ2Nvr06YO5c+fi3nvvxcGDB3H58mX87ne/w4EDB5CZmYlt27ZZ2VbtSavNoPtQQMaGg1i2w7ci6q82HMLU25Ixc1TgfXUsJ3gioU4kJJfBtlXwrDTUpRCdrue1pDlFOpPyOnomg6evP+gzt1K3ull1KSdF/fr1Q9++ffHDH/4QGzZswLJly9ClSxds2rQJ/fr1Q2ZmJnr16mVlW7Un5c0K6D8UkLHhIJZsr18Ty23A+7idiZHkiYQ68XSzB7qZsHt7isasNLT7Zkfn81pCAiyBvOXuek4G90d5+Ozjjz9Gv379MGPGDIwYMQLV1dX4/e9/j4MHD2LVqlXNPiEC5Jz0/oYCCjUZCqi87MayHYGLhC7bkWfrUJqUY6270BAHRvcO/GE9uneCrT0dhaUXTY2ziu7nNfdnM4mQXmrP+7GwzPfG0DMZ3O73oz/KSVFaWhqWLVuGwsJCLF68GCdOnMDw4cPRpUsX/OpXv8KJEyesbKcIEk56CfOeVmbmK809WJmZ3yTtaYjnWAdi97GWoNptYP3+wBfH9fsLbH0/nrlQaWqcFSSc15LnmehEQi+1hPejP42eaB0REYFJkyZh69atOHz4MB599FEsWbIEycnJGDVqlBVtFEPCSS9hkt7RM+WmxllBQg+HBBKKYMYqlgNQjbOChPMakFEYUXcSylhIeT82pNFL8mvr0qULZsyYgaSkJPzsZz/DBx98YFa7xPKc9LPfy8F/zl25c2wXHYZXHky1/aSXMO8pqU2kqXFWUO3heGlkDyZGAUgogumKURsCVY2zgoTz2mNkagLu6h6PlZn5OHqmHB1jIzEhrRPCWlz1rlPNi4AyFpLej3VddVK0bds2/PnPf8a7776L0NBQjBs3DlOmTDGzbWLtO1aCb877dqV/c74S+46V2J4USZgL092ltkWLapwVJE2+1ZmEIpgSJoNLOK89GpoM/sedeVpMBq9N11V8EspYSOjN8qdRSdHx48exYsUKrFixAnl5eRg8eDBef/11jBs3DlFRUVa1URTdV01JKKolYcxc8p2QTiQUwfQMiz+1ai8A3xtwXYbFJZzXgJzitlzFd40E9Gb5o9xfOXz4cCQnJ+ONN97AQw89hEOHDmHnzp34f//v/zEh+i8Jq6YkzHuSMLFV2p1QtdtAZm4x3ss+iczcYm0mOLqcEabGWUX3uTASzmspk2+5iu/aSejN8ke5pygiIgLvvvsu7r//foSGhlrZJrEas2pqym2dm6ZRDbhSVOuAz3LJ+JhwpI/uafsFXsLEVne12oVbNc5KOt/1Shia8tB9k1DPeV33WOtSLE9CcVsJW2jU7blsiN0JsIjeLD+Uk6L169db2Y7rgoRVU7783VPaq120WrKjGmeFT/KLleNuu/lbFrfGP92HKzwX+Cc1vsDXpvMmoYDeiZuEIWcJiRtQc5wfvz0ZS7fn1RvOffz2ZNsTYCnDuQ3hdH8TdYxVWw2lGmeVK0W1fE/+/5Tp0T0sYzxa/wpqUoYrqHmQ0HsgIXEDaq7hS+okREDNeb1ke57t13AJw7n+MCky0YS0TnAEOcYOR02cXSR8UEoYj1a9S7TzblJCrZBqt4EX/r4/YMwL/99+Jm6KNuYUYOi8j/Dosiz8+J1sPLosC0PnfWT7hyQgYy6MhMSt2m1gxprPA8bMWPO57eeMZzg3Psa3Rz8+Jtz2HupAmBSZKDTEgYiWgedbRbYMZfHGICRcmAZ2ilVKgAd2su8CL+Gud9fXp3GhsjpgzIWKauz6+nQTtUgu3ScIS+g96N+xDYI9fYijJs4uWbnFOFteFTDmbHkVsnLVhvitp+c0DX+YFJno07wzKA92ga+stjXhkPBBKeGOcs/REgTb19AwauLsIiG5XLNXbXsg1bjmSkIPMFC790DPVXx7jpYoLZax87zOPKJ2g6AaZxXtp2n4waTIRBISDgkflBLuKCUc61uSY9E6smXAmDaRLW1NLoP1EjU2rrmS0APsS8+d02Vs/it/PqMBPZL0hjApMpGEhENC9zCgf10YCcdahd2XpIGd1N5nqnHNlYQkHdB/53QJNdKuh/mMgG5J+hXXtPcZ+fLcmQca721t8515Y7qH7V56rPPyYglLTj/NO6M098DO5cWTBidjzoYvAiZnjv/GkX8SknTVIT47awBJqJE2qHMcosJCA/aeRoWHYlBn+67fEvY09Ic9RSarClKtuqravmrWgJw7Sg9PXZgH+7RHWpc4LRIigEN8ZglrEYLHbw+c8Dx+ezI3Cw3Ck6QHYvc8PAm9BxI2/wWAlkHOh5ah9p4vEvY09IdXGhNlHSlWWkmTdcS+VQES7iil4BCfOWaOSsETtyc3mFw+cXuyrXsFShEa4kBq+5iAMantY2xN0iX0HqjMw7O7t78xPcB2kbCnoT8cPjNRpuISyMzcYgzp2tbi1jRMwrCPJBziM8fMUSl4YUR3rMzMx9Ez5egYG4kJaZ206yHSdef0ystu/OtQUcCYfx0qQuVlt22vqeTeg9rsPtoSeoCl7GnYEL2uOOLpX4pZwrAPmUPasQ4NcSAl0Yn+HdsgJdGpTbs8dC6M+NaufKUSEW/tym+S9jREQu+BSi9Mic29MBJ6gCUM5/rDpMhEaZ3Ven9U46yi+7CPJDp/UAJyjvXGnAIMmev7Og6Zq8/rqHthxN2Ke/GpxllBQu+BhF4YCQlHaIgDo3sHvraM7p2g3Y0PwOEzUw3qEqe0+myQBhtK6jzsI4Xum6166H6sN+YUNLghbGHZJTy5ai/etPl1lLBzemRLtUu5apwVVFbn2l03S0IvjGf+WKBJ63bPH6t2G1i/P/CNwvr9BXhpZA9trkMe7CkyUWiII2g9lYGd2mj3JqDGk1JB2EPXVXwq+zjNtHkfJwmFEbsnRJsaZxe7zxYJddwqL7vxYZD5Yx/+d/6YXSSsNPSHSZGJJLxZPXQfrtCdhA9KCbKOBN/HqaS8ytYVmxKGVMouBX4NGxtnBQmrpiRs87EyM1+pjSsz85ukPQ2RcM74w6TIRBLerMCV4Yq6S189wxVMjIKTdtJXXnbjTzuO4Ofv5eBPO45okZgDjVuxaRcJQyohDrVLuWqcFSScMxLamF98wdQ4K0g4Z/zhnCIT5Sm+CVXjrKA6XGHn/AgJJJ30GRsOYtmOPJ+E/VcbDmHqbfbXAHIbasmZapwVJJQ2SOsSh0VbvlaKs4uEc6ZtlFqlatU4K6huE2fndnKeYchAnQR2D0P6w54iE+m/TZ+M4QoJPB+U/o6lA/avAAFqEqIl2/PqXZzcBrBkex4yNhy0p2H/1SZS7cNFNc4KntIG/q7vBuwvbTCoc5zS5r92bv0g4ZxxK2YSqnFWiG6l1pehGmcFCcOQ/jApMlHfJLWsVzXOChKGK2qrdhvIzC3Ge9knkZlbrNXEZU8NIH/s/qCsvOzGsh15AWOW7cizdShNQu0aANh3LPDFO9jPrRYa4sDcsb0CxmSM7WXr+1FCcql6M2jnTaPq68Mthq4OkyITxQepHdHYOGvoX2DSo2Yy+Id1JoN/qM2cp5GpCXj89uR6q1VCHDX7ddm9HF/CHLez5Wq7javGWUFCcgnUvB+f8PN+fEKD96MEJ89eNDXOChLq4UkYKvWHSZGZBOQbtyardZ+rxlnlymRw35L/hWUV2kwG35hTgKUNDE0ZBrB0e57tbTx6ptzUOCtI2JVcQnIJ6P9+9JSx8MdT78nO3uBgRREbG2cFTz28QOyuhydhqNQfJkUmOn1Bbc8e1TgrhDjUulRV46ygMhl8hs21ayTUKeoYG2lqnBUk7EouIbmU8H6UUMaiTaTaMK1qnBVCQxx4eECHgDEPD+igxVApIGOLodqYFJlIQpehhMQtKzf4ZPCz5VXIsnHek4QL/IS0TkqF6CakdWqS9jREwpYFSW3Utp1QjbOChPejhHkmJYrDtKpxVqh2G/jbv08EjPnbv0/YPv9SyhZDdXFJvokkLN2VkLhlHjmtHDekmz3j5hIu8GEtQjD1tmQs2e5/PszU25Jt3Ynec0f51H+3+ah93uhyR9ndFWNqnBUkvB8lLHc/pThXSDXOCo25abTr+uih+xZDDWFPkYkkdBlKuDOXUNxAQnIJADNHpQScfGt3nSLgyh1lu2jfD8P4mHAt7ijPKPYKqMZZQcL7UcJydwnXnsbcNOpA1y2G/GFSZDLduwwl7F6sWmDOzkJ0EvZI8pg5KgVfvPodzLqvByamdcSs+3rgi1e/o0VC5LHvWAm+Oe87ZFt0rsL2pe6AjITjluRYRIWFBoyJCg+19Wbnkzy14W7VOCsktFacaK0YZw39EzfJOHxmAZ27DCXsXuwpRBdsN207C9E1pjiZncmbR1iLEEy5rbPdzWiQp8BkXZ4CkwBsTeAk7O5e7TZQXlkdMKa8ohrVbsPG65D+H+ZlFxX3kFOMs4KE6uWSsafIItVuAwdPlWLP0RIcPFVq+6Q3Dwm7F6usrhhn8+oKCXM4JJBSAygYu8/ulZn5QdtgwN6yARJ6gP9Tpna+qsZZQUL1csmYFFkgY8NBdJ/1T7z6/iH8JfMoXn3/ELrP+qftWyoAqLcJ7LXGWUG1N8vORFPCkEptulYGl1ADSMLu7vnFauUAVOOsMKhzXNBJ/eEtQmz9ML9UFbi3rbFxVpBQvVwyDp+ZTPehgDPn1Zbaq8ZZoTG9WXbdVaoMqbS2eUjFY2NOAV75x0Gf1zTB2QqzH0ixfY6bhBpAMnoF9a8cW+02UFUduMevstpt6xBf2xvUbmJU46ziqV5ed6PnEEfNilK7z2vJ2FNkosrLbiwNMhSwlHtNBSXjQwioCnIcg30ANIWNOQV4atXeeklmQeklPKVBZfCkNmqFI1XjrCBhKXmfDq1NjbPCysz8oDu3Gzb3CrZvrVZrSjXOKrpXL5eMSZGJ3tqldtK/tSu/SdrTEAnDPhLamHWkGBeCTGy9UFFt68aRgaocAzV9BnZXOb6p3Q2mxllBwlJyl1PtQ1o1zgoSegWDzdVpbJwVJFQvl4xJkYl256vNKVCNs4T+C0BELHfPVKymrRpnBQmT6j9VPBdU46wgYSm5hMRNwrYzZy4o1qRSjLOChOrlkjEpMlFkkDohjY2zwmnFuUKqcVZozHJ3++g/h6OwVK3qrmqcFSTsSi7hTkK1R9LOnssJaZ2CvkIO2LvtzOcnS02Ns4KU6QVSMSky0f/0DbyMvLFxVpAwNCXhpE/rrFY+XzXOChLueiUUy5OwlFzC9hShIY6gN4SR4aG2rpqScGMbG6E4L1QxjnwxKTLR4G5tg5/0YaEYbON+NJ5VU4HYXYhOwknfT3HoTjXOCrE3qE38VY2zQptWiruSK8ZZYWAntXNBNc4KCYpzhVTjrPBp3hmleXh2DvtIOK+/+M85U+PIl6ikKCMjAw6HA9OmTfM+ZhgG0tPTkZiYiIiICAwbNgwHDhywpX2hIQ4M6Rr4bnFIV/v3fgm2+s3uQnkSTvrVnxw1Nc4Krhi13hXVOCucuahYIkIxzgqfKA45qcZZwRmpVl1FNc4KEnqA9R8oBY6XqE1EV40jX2KSot27d2Pp0qX49re/7fP4/PnzsWDBAixatAi7d++Gy+XC8OHDce5c039gVl5248NDRQFjPjxUZGvSkXWkOOh2ABcq7V01JeGkzyu+YGqcFSRMWC8sVUt2VOOs8O7eE6bGWaHs4mVT46wgYeh+77GzpsZZQUIZi9p0LRzrj4ik6Pz58/j+97+PZcuWoU2bKxdwwzCwcOFCvPzyyxg7dixSU1Px1ltvoby8HKtXr27ydkqozith1ZSEVSoS7iglTFhv30axLoxinBXKK9USCdU4Kzgcau801Tgr3JIciwRnK7/nhAM1RUXtHLqPaKH2+qjGWeGmbymWsVCMs9LGnAIMnfcRHl2WhR+/k41Hl2Vh6LyPtK6jJCIpeuaZZ3Dffffhnnvu8Xk8Ly8PhYWFGDFihPex8PBw3HHHHdi1a5ff31dRUYGysjKfLzMcOX3e1Dhr6L9qavytHU2Ns0LfJLXeFdU4K0gYrhjcRW1+nWqcFQbcqPYhrRpnhVsVEwnVOCuEhjgw+4Gaav51UwrP97MfSLF3onW4Wv0h1Tgr7D6mWPpFMc4q/grHFmpSONYf7ZOid955B3v37kVGRka9nxUWFgIA4uPjfR6Pj4/3/qwhGRkZcDqd3q+kpCRT2lpUptbFrxpnhVs7qa2QUY2zQvbxs6bGWSHeqdbFrxpnhbaKE6hV46wwqHMcwjXfD6tHYoypcVYIUewBUo2zysjUBDx+ezLqNsPhAB6/3f7tKSSULJHQTy25wKTWSdHx48fx4x//GKtWrUKrVv4/XOp2CRuGEbCbeObMmSgtLfV+HT9+3JT2tlOcsKoaZ4WQUMWLp2KcFST0cAjocBPRxmq3oTTx386L52nFkgWqcVY4fUHxw1wxzir+tqdwa7I9RVS42kR01TgrSCgRIbnApNZJ0Z49e1BUVIT+/fujRYsWaNGiBbZt24bf//73aNGihbeHqG6vUFFRUb3eo9rCw8MRExPj82WGzm2jTI2zgoQ7IQkTMiV8CBUpHkPVOCuszMwPmpMZsHcenoRNlCWcM8G2nQHs7z2QUGtOQokIETe2fmidFN199934/PPPkZ2d7f0aMGAAvv/97yM7OxudO3eGy+XC5s2bvf+nsrIS27Ztw+DBg5u8vRPSOtXrFq7L4bC3YquEzS1VainZvQO9hA8hCR/m+cVqKwhV46wgYRNlCZOYJfQeSKg1J6FEhITroz9aJ0XR0dFITU31+YqKikJcXBxSU1O9NYvmzJmDtWvXIicnB5MnT0ZkZCTGjx/f5O0NDXEgomWQE6qlvRVbBQxHK7G7ef07tlHassDO5e4SPszdhlp5CtU4K0jYbNUziTnQ5r92T2KW0HsQGuLAgnG9A8YsGNfb1tdRQokICUm6P1onRSpeeuklTJs2DU8//TQGDBiAkydPYtOmTYiOjm7ytnyad0apBpCdd0IShs8+zTuDs+VVAWNKyqtsfR13551RGvbZbWMbJXyYxyiu4lGNs0L/jm2UeoDtTIAlkNx7oJMTivXZVOOsIGGloT/ikqKtW7di4cKF3u8dDgfS09NRUFCAS5cuYdu2bUhNTbWlbRL2H4qNVOw9UIyzgoQ7yo9zvzE1zgoShiFDFC+KqnFW2J1/BsE2lzeMmji7eObr+OOA/fN1JBQTDfY6Ava/ju1bK9b2UoyzimelYUN0WGnoj7ikSGfZx9WK4KnGWeGLQrWaTKpxVpBwR3nqrFpCphpnlYtBei6D/dxq0YqreFTjrCCh4KmE+ToSiokGex0B+1/Hh/qrlZBRjbPKxpwCLNmeV69H3QCwRIOVhv4wKWpmjpeo9VKpxllBwni0hErMu74+jYogy90rLrux6+vTTdSi+g4pJt+qcdbQv7aBhN5VCW0sLFW77qnGWeFWxZpdqnFWqHYbeP7v+wPGvPD3/axTdL3rFKe21F41zgoSttCQMGl0ULLaBUc1zgprFCdaqsZZIdgcvMbGWSGts9pKI9U4K0joXZXQxtPnFWtSKcZZQbUnzc4et11fn1aaX2vnDZk/TIpMNCGtk9KYuZ1L8iWUDZBAQgXhC4qJhGqcFeIVC5mqxllhUJc4pblZg2wslidh/piEHuCz5WrJjmqcFST0uElYIecPkyIThbUIwdTbGp5Y5jH1tmSEBdnWwEoSygZImDQqoXjjwE5qE1ZV46zQN6m1qXFWCA1xYO7YXgFj5o7tZftKmmCVwauC/NxqElYkqd7D2LlbioQeNwmbKPvDpMhkM0el4Inbk+v1GIU4gCduT8bMUSn2NOy/JJQNkDBpVMKFadLgZKVaSpMGB07krSShbABQs5Lmzcf6wRXjW9TUFROONx/rZ/tKmqzcYqXzOsvGyeCA/nufSRgqldDjNlBx70zVuKZk35KO69jMUSl4YUR3rMzMx9Ez5egYG4kJaZ1s7SHykND1KqGNngtToOTN7gtTWIsQPH57MpZsz/Mb8/jt9vZcuoOtdW9knJVGpiZgeIoLn+adQdG5S2gXXXN87e4hAoDMI2pzMzKPnMYQG6sxe/Y+q3s0PXuf9b2xja2J0aAucYgMCw2YYEaFhdo6VOrpcXty1d4Gf67DnMtJgzthzj8PBSxl4XDUxOnG/k/p61RYixBMua0zfvFgKqbc1lmLhAiQ0cMhoY2hIQ6M7h344j26d4LtH5gzR6VgeEq7Bn82PKWd7T2XWXlqPReqcVYLDXEgrUscHuzTHmld4mw/vlfoX6pewt5nAIJeq1tqci3XWViLEDweZCrJ4zZPJfFHvxaRpW5JjkV4kDdieIsQTsgMotptYP3+wHU21u8vsP0CvzGnAP86WFTvcQeAfx0ssr1WyEnF0g+qcc0Vd043h0o1/bM2V9OXMOcS0H8qiT8cPmtmKi+7lWrXVF52IyLIxohWkdA93Jgib3Z9EAW6Mzdw5eI5PMVl22vpcqptPKwa11wN6hyHqLDQgCsJo8JDMcjG2jUShsUl1ClqTHJpZxIM6D2VxB8mRc3MnA2BS9jXjnt1TOAVN82ZhAu8hItnXJTaEKhqXHPWskUIECApahlq7weRhGHxMxfUltqrxllBwrWnNs9UEin0TdfIEvnFapsEqsZZQcL+QxIu8BIunm2j1XqAVOOaKwnDPhKGxWNvUHufqcZZoW2U4jmjGEe+mBRZpNptIDO3GO9ln0RmbrHt47seneLUKlWrxllBwv5DEi7wEi6eLsWijKpxzZWEBFhCpfp2ismOapwl9J9TLxqHzyywMacA6esPoLDsSuE+V0w40kf3tL0Ox89GpWBl1jGlOLsUlqlduFXjrOC5wD+1ai8c8N31SpdCdBKWu0sobSCBhJ5LEQQkHKfPKxaOVYwjX+wpMtnGnAI8uWqvT0IEAIVlFXhy1V7bV/tEhIX6XaLtMTylnW2TrAHgjOLJrBpnlZGpCVj8WL96W1C4nK2wWIOCfp8o9qSpxlnBk1w60HCVYwfsTy5r07UHuH/HNkpbDPXvaF/1cgmrpiQkHNISYF3PGX/YU2SiareBGWs+DxgzY83ntq72AYBlEwdi6l92Y3MDS7WHp7TDsokDbWjVFbFRYabGWc/3JDc0KDRYQ//d3YEryeUr/zjo02PkcrbC7AdSbE8uPTbmFNRrY4ImbdxztATBPmvcRk2cXZPqJUz8lzDkLKl3Vedzxh8mRSbKyi1WmuyYlVtsa1VZoCYxulhZjTkbDiK/uByd4iLxs1EptvYQeUjZ+mFjTgGeWrW3Xkrxn7IKPLVqr+29RWmd22LRllylOLvpXC0a8H+sC0svaXGsJcwpktBGCUPOnsKxgSrV61A4Vvdzxh8On5loV65aqX3VOKtFhIXi1TG9sHLKrXh1TC8tEiLgyp1QIHbfCQWrAQTYPxQgYXf32nStFi3hWEsYUpHQRgkV1iUUjpVwzvjDpMhEp86qFfRSjWuuJGyhIaE6r5Td3XUn4VjfkhyrlADbeSMhYd7TiTNq12bVOCtIWJ0r4Zzxh0mRiRLbqA3nqMY1VxLuhCQMBQD67+4ugZRjHYzdqW9j5j3ZxTACV/tvbJwVJKzOlXzOcE6RiQYlx+EPCnM4BiXrMVyhKwlbaLRVrFOiGmcl3efr6E7CsI9K8caS/xZvtOuckfBB6VBMHVXjrHD6nOIKOcU4K0g4Z/xhT5GJQhxqJ4pqXHMl4eIpZGEXmUDCsI+Ec0bCB6WheGlWjbNCSbnaFiOqcVaQUNzWH/YUmej0BcUMXjGuuZJw8SxSrFOiGmclKctiKy+7tdw4UsJydwnnjOeDsrD0UoP3Cg7UlGGw84NSxo2t/ndkEorb+mP/Fec6IuHCJIGEu4zTinfcqnFW8SyLrTsc6VkWa3cxUY+MDQfRfdY/8er7h/CXzKN49f1D6D7rn8hQ3MDYShJ6YSScMxK2+UhULPOhGmeF1hFq9dlU46yie3Fbf5gUmUjChUkCCRfPYPM3GhtnBSnLYjM2HMSS7Xn1emPcBrBke57tiZGEmx3POQM0XBkcsP+cAYB9xwJPog72c6vFRKgNnqjGWUHCtceXrsVtG8akyERSLkwSvLv3xDX93GoOxe5z1TgrSFgWW3nZjaU7/BehA4ClO/JQedm+1T5SbnY8d+Yup5535pWX3VgW5Fgvs/lYf1FQZmqcFU6VKpZ+UYyziqeXuu6WV57itrr0UtfFpMhkul+YJLhYWd3gFiS1bT5YhIuV1U3UovpU547YNccEkDHs89aufAS7cTSMmji7SLrZGZmagJ3T78LbUwfhd4/0wdtTB2Hn9Lu0uO6szMxXmpu1MjO/SdrTkPIqtYRMNc4Kia0Vh/gU46wgpZe6IZxobQEugb42cxSHS+ZsOIhXxwQuTmiVQZ3jEBUWigsBErOo8FAM6mxfUiRh2Gd3vlov1e78M5h6e2eLW+OflP3ZgCuVwXVz9Ey5qXFW6N+xNTYd/I9SnF2GdG2LN7YGL/0ypKt92/dI2OfOHyZFFtH1wiRBfrHaRVE1ziotW4QAAZKilqH2dsRKWO0T0VLtNVKNs5KUm51qt6FlGzvGRpoaZ4XurhhT46wwqHPN9j2B5gy1iWxp6w2ZhF5qf+y/0lynqt0GMnOL8V72SWTmFmvZTairTnFqF0XVOCuoFMs7+99ieXaRMOzTM8FpapzVdN2fzWNjTgGGzvsIjy7Lwo/fycajy7IwdN5HWszfmJDWSane04S0Tk3Snobszleb6K0aZ4XQEAcGdgpcE2tApza2vjcl9FL7w6TIAjpfmGrTNXH72agUU+OsIOVOaGRqAu5JaVevp8gAcE9KO9uHfdpGK1YGV4xrznQvvxDWIgRTb0sOGDP1tmSba1PpXwOo8rIbHx4KPOfyw0NFti9OCLYXXxub9+Lzh0mRyXS/MHnonLiFtQgJemFUibGSlDuhjA0H/U5a33ywyPbl7sUX1KruqsY1V1Imtva9MXAPR7CfWy2ts9o8HNU4K0iYsK5Cj1vw+pgUmSjYhcmAHhcm3RO3T/POBL3LqbzstnVoSsLWDxKWQEvYsqA2XXtXJZRf8Fwf/XHA/uvjoC5xQXs4Wke2xCAb54tKmLAuYXqBP0yKTNSYjUztIuGOUsLQlIQdvyXcUapOe9Bh6s7GnAIMmevbuzpkrh69qxLOGQmJW2iIA3PHBl7ROndsL1vn60iYsC7h/egPkyITFZapHWDVOCtIuDBJGJqScNJLuKOUMFwB1CRET67aW+/cLSy7hCc16F3lOdN8SJiwLuH96A+TIhOdUdz8UzXOChIuTH2SWpsaZ4W2UYoThBXjrCDhjlLCcEW128CMNZ8HjJm55nNbe1clDOdK+KCUMMQX1iIEd/doFzDm7h7tbJ1zKaUKfEOYFJkoNkptAz7VOCtIuDCt/uSoqXGWUO09t3HYR8IdpYThiqwjxUHnR5SUVyHrSHETtag+CcO5EhI3CT3p1W4DOScDbzOSc7LM1sRNwv6V/jApMpFLcedk1TgrSOiFkTDsc1qxt081zgoylkDrLzNXLdlRjbOChB5gCYmbhNdRwtxVyXg1NJGnyzAQu7sMJfTCJLVRG85RjbNC2xsUh88U46wyc1QKnrg9ud4deogDeOL2ZMy0sdYTIGO4QkLtGgk9wBISDgmv46kStZtB1TgryDivG8akyEShIQ6M7h24GN7o3gm2dhlK6IXp7oo2Nc4S+n9Oes0clYIvXv0OZt3XAxPTOmLWfT3wxavfsT0hAmQMV9zaSW0+k2qcFW5JjkVUWGjAmKjwUFtvyCQkHBJubLNPnDU1zgoSzmt/mBSZqNptYP3+wKtQ1u8vsDU7ljD59oxiTRrVOCucvqA4fKYYZ7XQEAdSEp3o37ENUhKd2ozlS+g9CAlVe61U46xQ7TYCbk4MABcqqm299kiYfBsa4kBq+8D7mqW2j7H5/NF/QqOE89ofJkUmkjDWK2HyrYSVXRLuej10rl4u4XWUMH/srV35psZZQcLkWwlbaNyoeMOqGmcFCee1P0yKTCQhOxYx+Vb/GyHckhyLyCDDFZFh9g5XAP6rlxdoUr1cQu+BhAv87ny1Sd6qcc2VhIKnN33rBlPjrCDhvPaHSZGJJPRwAPpPvpVwZ17tNnCxKvBwxcUqe4crAlUvB/TYdsbTewDUz3E939vdeyDhAh8Z1sLUOCtImHx75PQFU+OssPuY2kiDapwVJJzX/jApMpOAHg6PmaNS8NnsezG8Rzvc7IrG8B7t8Nnse21PiAAZd+YrM/NhBLl2GzbfUUoYzgWAkakJWPxYP8TH+B5Pl7MVFj/WDyNTAy9esFrtC7w/dl/g/6dfB1PjrCBh8m2R4m4DqnFWkLLGw3Neu5x6ntf+2HfbcB2S0MPhkbHhIJbtyPN2FX9ZeA7ffuUDTL3N/p4iz515YemlBk9sB2pOLDvvzCWs4jt19qKpcVYz6mSZbo2W645MTcDjtyf7nDNATe/q1NuSbb/AD+7aFpFhoSgPMNk6KiwUg7vat12KhOkF7aLVevFV46zQOkKt+K9qnJVGpiZgeIoLn+adQdG5S2gXXXPd1rGHyIM9RSaS0MMB1CRES7bn1Rs7dxvAku15yNjgv4u7KUi4M5ewim/fMbUieKpxVvHsK/afc743C/85V6HFvmJATRuXNnDOGAawdHue7W0MDXFgwbjeAWNeG9fb1nNGwvWxU1u1eTiqcVaQsHOCZEyKTCRh7kHlZTeW7cgLGLNsR56tqyuAK3fmDc17evx2++/Mx9/a0dQ4K/xHsYtfNc4KEvYVCzQ3y/OY3XNhgJpz5s3H+iG+Ti+GKyYcb2owXCHh+tg9XrFGmmKcFc4qliJRjbOSzitf/WFSZCIJk8skrK4A9L8zzz5+1tQ4K0SFq42Oq8ZZQcK+YhLmwniMTE3Arpl34+2pg/C7R/rg7amD8PGMu21PiAAZ10cJ9cek9BT5W/laqMnKV3+YFJlM98llEubCSLgzlzA/4n/6Kk6+VYyzAvcVM19oiANpXeLwYJ/2SOsSp9X8Dd2vj2cuKBaOVYyzgoQ9NiVcw/3ReqJ1RkYG1qxZgy+++AIREREYPHgw5s2bh5tvvtkbYxgGXnnlFSxduhQlJSW49dZb8Yc//AE9e/a0rd06Ty6TMBemMXfmaV3s2VohVnESo2qcFQZ3Cz75NjIsFIO72Tf51lBcI6MaZwUJc2Ek0fn6GKu4V6FqnBX6d2yDEAcC9viHOGri7CLhGu6P1j1F27ZtwzPPPIOsrCxs3rwZly9fxogRI3DhwpUaEfPnz8eCBQuwaNEi7N69Gy6XC8OHD8e5c+dsbLm+d2sSKlpLuDP/4j9q7y/VOCuoTL5dYPPk29YRLU2Ns8ItybFoHRn4+dtEttSyEJ2udL0+umLUElvVOCvsOVqiNAViz1H7FlBIuIb7o3VStHHjRkyePBk9e/ZE7969sXz5chw7dgx79uwBUNNLtHDhQrz88ssYO3YsUlNT8dZbb6G8vByrV6+2ufV6klDRWsKd+XHFHahV46wyMjUBT9ye3OAcjic0mLDeVvGOWzXOLjoNAlS7DWTmFuO97JPIzC3WcohCVxI2hJWQcEi4hvujdVJUV2lpKQAgNrbmDZmXl4fCwkKMGDHCGxMeHo477rgDu3bt8vt7KioqUFZW5vPVnHgqWvv7oNShTpHud+YShiGBKxPWG/pY1GHCuoT5EZ/mnQk6GfxseZUWE60lrvbRiWcyuAMNTwZ3wP7J4BISDgnJpT9ikiLDMPD8889j6NChSE1NBQAUFhYCAOLj431i4+PjvT9rSEZGBpxOp/crKSnJuoZrqu+NbepVEI6PaYW+N9o3Dt0Ydt/7ShiGlDDZUcLFU8KdOSB3tY9udJ8MLuGcCQ1xYHTvwK/T6N4J2gyb1iYmKfrRj36Ezz77DG+//Xa9nzkcvi+sYRj1Hqtt5syZKC0t9X4dP37c9PbqzHPxLKxTn+Y/ZXpcPCXcmUsYhpSwlLz2nXlDeGeuRkICLMnI1ATsnH6XT2mDndPvsj0hAmrOmdT2MQFjUtvH2HrOVLsNrN8f+HNk/f4CLd+PIpKiZ599FuvXr8eWLVvQocOV5cMulwsA6vUKFRUV1es9qi08PBwxMTE+X82FhIunlDtz3TfWlfI6eu7M6979Jmh2Z65z0UEJCbA0uk4Gr7zsxoeHigLGfHioyNYCvFL2XWyI1kvyDcPAs88+i7Vr12Lr1q1ITva9M09OTobL5cLmzZvRt29fAEBlZSW2bduGefPm2dFk7UlYKinhztyj741t0DbqBIrOX+nZahvVUothSEmvo87LtD29WU+t2gsHfIdudSk6KCUBlqTabWj5fmxMAd4pt3VumkbVUaC4n6JqXFPSOil65plnsHr1arz33nuIjo729gg5nU5ERETA4XBg2rRpmDNnDrp164Zu3bphzpw5iIyMxPjx421uvZ4kXDwlbAgLXNmzq66i81V4ctVe27dWkPI6enjuzHXk6c165R8HfW4qXM5WmP1Aiu29WZISYAk25hTUO9YJmhzr/OILwYMaEWeFfccV9108XoKx/e0rHtsQrZOixYsXAwCGDRvm8/jy5csxefJkAMBLL72Eixcv4umnn/YWb9y0aROio+3bm0ZnEi6eEu7MVfbsmrHmcwxPcdnWTgmvoyQ692ZJS4B17YUBrsy5rPs6eias6zCkqzvVyRf6zSjSPCkyjOAvmcPhQHp6OtLT061v0HVAysVT9zvzrNzge3adLa9CVm4xhthYMVr311EaXXuzJCXAOvfCBJtz6UDNnEs7b3b6JLXByqxjSnF2SY6LMjWuKWmdFJH5JF08db4zzzxyWjnOzqQI0Pt1JPNISIB174WRMOcysbVazS7VOCtMSOuEX204FHQrEjtLlvjDpKgZknDx9ND1zrx+abdrjSMJdB72AfROgCX0wkiacxkoebN7NaSnZMmS7Xl+Y+wuWeIPkyKL8OJ5fUvrEodFW75WirPbxpwCpK8/gMKyCu9jrphwpI/uqVUCrDudh31q0/VGQkIvjLQ5l/6mQOjQ2+8pSbJsR55Pj1GIoyYhsrtkiT9MiizAi6d5dE0uB3aKhcMBBJr25nDUxNnJ3wq5wrIKLVbISaH7sI8EknphpM651O1zZuaoFLwwojtWZubj6JlydIyNxIS0Tlr2EHkwKTIZL57mqenhOOhTedsV0wrpo+0/6fccLQmYEAE1CdOeoyW2JZ4SVshJIGHYR4K2UYqb/yrGWYFzLs0X1iLEtnpJV0PfdE0gCdWipfD0cNTdiqSw7BKe1GArEgl3vY1ZIUf+sVq0SYRMw9N977PadK26LRl7ikwkYcxcApUejpk293BImHsgaYWcziQkwBKcPl8RPKgRcVaS0gtD5mNSZCJePM2RdSR4D0dJeRWyjhRjSFd7PsxlzD0QcmuuOQkJsATSXkcJcy7JfBw+M5G0k15XmYrDOapxVvDMPfA3EGrA/rkHqhd0XvgDk7AhrAT9O7apt3lyXSGOmjgdVLsNZOYW473sk8jMLea0h2aCPUUmktF7IIHkIvH6GNQ5Dq0jWwbsdWsT2RKDOjMpCkTS5Fud7TlaorSRqZ2LEzykrCAm87GnyEQSeg9q0/VO6NZktQuiapwVPJPq/fGsSLLzNQ0NcWDu2F4BYzLG9uL7UYGkybe6kjK9wLOCuO78UM8KYrsXeZC12FPUTOl8JxTiUPuQVo2zAifVm0vn96MHJ99eGwnTC1h+gdhTZCIJvQeA/ndCpy8orlJRjLOChLte1TpFfD+q4xLoqydhbhbLLxCTIhNJOKEk1FKScEcpoY0S6hRJeD+SOTzTC4D66x11mZsl4WaHrMWkyEQSTigJiZuEO0oJbWxMnSK7SHg/knl0n5sl4WaHrMU5RSaScEJJSNwkrPaR0EYJa/gkvB/JXDrPzeIKYvPpun+lP0yKTCThhJKQuAH+Nzx0aTT5Vvc2to4IMzXOClLej2QuXQsjSrjZkUTCAoq6mBSZSMIJ5SmgFmiKhi4F1HS+o/TQuY1tb1BLdlTjrCDhRoKaF91vdqSQujk6kyKT6X5CSSqgBuh7R1mbrm10OSNMjbOChBsJan50vtmRQHJpAyZFFtD5hOIcjubD0wsTaCKz3ZPBAf1vJKh50vVmRwLJddyYFFlE1xOKcziaj9q9MP6GpnTphdH5RoKIGkfyzTeX5DczEpaSk3k8vTAJdZZAJ2iyBLo2FkYkuj5IvvlmT1EzwzkczQ97YYioKUleQMGeomZI9wJqZD72whBRU5FQvdwfh2EYzb5+fllZGZxOJ0pLSxETE2N3c5qMtKJaREQkR1PUKTL785tJEZpvUkRERGQlq2++zf785pwiIiJqUuylbj50XYntD5MiIiJqMhK3fqDmgxOtiYioSXi2fqhb2M+z9cPGnAKbWkZUg0kRERFZLtjWD0DN1g/VwfYhIrIQkyIiIrJcY7Z+ILILkyIiIrKc5K0fqPlgUkRERJaTvPUDNR9MioiIyHLcd5EkYFJERESWk7z1AzUfTIqIiKhJcN9F0h2LNxIRUZMZmZqA4SkuVrQmLTEpIiKiJiVt6wdqPjh8RkRERAQmRUREREQAmBQRERERAWBSRERERASASRERERERACZFRERERACYFBEREREBYFJEREREBIBJEREREREAVrQGABiGAQAoKyuzuSVERESkyvO57fkcv1ZMigCcO3cOAJCUlGRzS4iIiKixzp07B6fTec2/x2GYlV4J5na7cerUKURHR8Ph4KaEZisrK0NSUhKOHz+OmJgYu5vTbPE46IPHQg88Dvq42mNhGAbOnTuHxMREhIRc+4wg9hQBCAkJQYcOHexuxnUvJiaGFx4N8Djog8dCDzwO+riaY2FGD5EHJ1oTERERgUkREREREQAmRdQEwsPDMXv2bISHh9vdlGaNx0EfPBZ64HHQhy7HghOtiYiIiMCeIiIiIiIATIqIiIiIADApIiIiIgLApIiIiIgIAJMiqiUjIwMDBw5EdHQ02rVrhzFjxuDLL7/0iTEMA+np6UhMTERERASGDRuGAwcO+MQsXboUw4YNQ0xMDBwOB86ePevz8/z8fEyZMgXJycmIiIhAly5dMHv2bFRWVgZs3/nz5/GjH/0IHTp0QEREBHr06IHFixeb8rfrpKmOAwCMHj0aN954I1q1aoWEhARMmDABp06dCtg+lee+Huh8HKqqqjB9+nT06tULUVFRSExMxMSJE4MeO6l0PhZ1PfHEE3A4HFi4cOHV/Klak3AcDh06hNGjR8PpdCI6OhqDBg3CsWPHlP9GJkXktW3bNjzzzDPIysrC5s2bcfnyZYwYMQIXLlzwxsyfPx8LFizAokWLsHv3brhcLgwfPty7fxwAlJeXY+TIkfjZz37W4PN88cUXcLvdWLJkCQ4cOIDf/va3ePPNN/3Ge/zkJz/Bxo0bsWrVKhw6dAg/+clP8Oyzz+K9994z5wXQRFMdBwC488478fe//x1ffvkl3n33XeTm5uKhhx4K2D6V574e6HwcysvLsXfvXsyaNQt79+7FmjVrcPjwYYwePdqcP14zOh+L2tatW4dPPvkEiYmJV//Hakz345Cbm4uhQ4eie/fu2Lp1K/bv349Zs2ahVatW6n+kQeRHUVGRAcDYtm2bYRiG4Xa7DZfLZcydO9cbc+nSJcPpdBpvvvlmvf+/ZcsWA4BRUlIS9Lnmz59vJCcnB4zp2bOn8Ytf/MLnsX79+hn/+7//q/DXyNWUx+G9994zHA6HUVlZ2eDPG/vc1xOdjkNDPv30UwOAcfToUeX/I5WOx+LEiRNG+/btjZycHKNjx47Gb3/720b9TRLpdhwefvhh47HHHmv8H1ILe4rIr9LSUgBAbGwsACAvLw+FhYUYMWKENyY8PBx33HEHdu3adc3P5Xkef4YOHYr169fj5MmTMAwDW7ZsweHDh3Hvvfde03PrrqmOw5kzZ/DXv/4VgwcPRsuWLRuMsfI9oDudjoO/9jkcDrRu3fqqn1sK3Y6F2+3GhAkT8OKLL6Jnz55X/XzS6HQc3G433n//fdx0002499570a5dO9x6661Yt25do56LSRE1yDAMPP/88xg6dChSU1MBAIWFhQCA+Ph4n9j4+Hjvz65Gbm4uXn/9dTz55JMB437/+98jJSUFHTp0QFhYGEaOHIk33ngDQ4cOvern1l1THIfp06cjKioKcXFxOHbsWMDhSKveA7rT7TjUdenSJcyYMQPjx4+/7jc21fFYzJs3Dy1atMBzzz3X6OeSSrfjUFRUhPPnz2Pu3LkYOXIkNm3ahO9+97sYO3Ystm3bpvycTIqoQT/60Y/w2Wef4e233673M4fD4fO9YRj1HlN16tQpjBw5Et/73vfwwx/+MGDs73//e2RlZWH9+vXYs2cPXnvtNTz99NP417/+dVXPLUFTHIcXX3wR+/btw6ZNmxAaGoqJEyfCCFLo3sz3gAS6HgegZtL1I488ArfbjTfeeKPRzyuNbsdiz549+N3vfocVK1Zc1+dAXbodB7fbDQB48MEH8ZOf/AR9+vTBjBkzcP/99+PNN99Ufs4WjW4lXfeeffZZrF+/Htu3b0eHDh28j7tcLgA1dwMJCQnex4uKiurdGag4deoU7rzzTqSlpWHp0qUBYy9evIif/exnWLt2Le677z4AwLe//W1kZ2fjN7/5De65555GP7/umuo4tG3bFm3btsVNN92EHj16ICkpCVlZWUhLS6sXa/ZzS6DjcfCoqqrCuHHjkJeXh48++ui67yXS8Vjs2LEDRUVFuPHGG72PVVdX44UXXsDChQuRn5/f6OfXnY7HoW3btmjRogVSUlJ8Hu/Rowd27typ/JzsKSIvwzDwox/9CGvWrMFHH32E5ORkn58nJyfD5XJh8+bN3scqKyuxbds2DB48uFHPdfLkSQwbNgz9+vXD8uXLERIS+K1YVVWFqqqqenGhoaHeO4TrRVMeh4aeGwAqKioa/LmVz60bnY8DcCUh+uqrr/Cvf/0LcXFx1/ScOtP5WEyYMAGfffYZsrOzvV+JiYl48cUX8cEHH1zTc+tG5+MQFhaGgQMH1isRcPjwYXTs2LFRT0RkGIZhPPXUU4bT6TS2bt1qFBQUeL/Ky8u9MXPnzjWcTqexZs0a4/PPPzceffRRIyEhwSgrK/PGFBQUGPv27TOWLVtmADC2b99u7Nu3zyguLjYMwzBOnjxpdO3a1bjrrruMEydO+DxXbTfffLOxZs0a7/d33HGH0bNnT2PLli3GkSNHjOXLlxutWrUy3njjDYtfmabVVMfhk08+MV5//XVj3759Rn5+vvHRRx8ZQ4cONbp06WJcunTJ+3vqHgeV574e6HwcqqqqjNGjRxsdOnQwsrOzfdpXUVHRRK9Q09H5WDTkel19pvtxWLNmjdGyZUtj6dKlxldffWW8/vrrRmhoqLFjxw7lv5FJEXkBaPBr+fLl3hi3223Mnj3bcLlcRnh4uHH77bcbn3/+uc/vmT17dsDfs3z5cr/PVbc9tZ+7oKDAmDx5spGYmGi0atXKuPnmm43XXnvNcLvdVr0ktmiq4/DZZ58Zd955pxEbG2uEh4cbnTp1Mp588knjxIkT9drT2Oe+Huh8HPLy8vy2b8uWLRa+KvbQ+Vg05HpNiiQchz/96U9G165djVatWhm9e/c21q1b16i/0fHfX0xERETUrHFOERERERGYFBEREREBYFJEREREBIBJEREREREAJkVEREREAJgUEREREQFgUkREREQEgEkREREREQAmRUQkXH5+PhwOB7Kzs+1uig+Hw4F169bZ3QwiagQmRURkucmTJ2PMmDH1Ht+6dSscDgfOnj171b87KSkJBQUFSE1NvfoGEhEBaGF3A4iIrlZlZSXCwsLgcrnsbgoRXQfYU0RE2nj33XfRs2dPhIeHo1OnTnjttdd8ft6pUyf88pe/xOTJk+F0OjF16tR6w2eTJ0+Gw+Go97V161YAQElJCSZOnIg2bdogMjIS3/nOd/DVV195n2PFihVo3bo1PvjgA/To0QM33HADRo4ciYKCAm/M7t27MXz4cLRt2xZOpxN33HEH9u7da/nrQ0TWYlJERFrYs2cPxo0bh0ceeQSff/450tPTMWvWLKxYscIn7te//jVSU1OxZ88ezJo1q97v+d3vfoeCggLv149//GO0a9cO3bt3B1CTNP373//G+vXrkZmZCcMwMGrUKFRVVXl/R3l5OX7zm99g5cqV2L59O44dO4af/vSn3p+fO3cOkyZNwo4dO5CVlYVu3bph1KhROHfunDUvDhE1DYOIyGKTJk0yQkNDjaioKJ+vVq1aGQCMkpISY/z48cbw4cN9/t+LL75opKSkeL/v2LGjMWbMGJ+YvLw8A4Cxb9++es/77rvvGuHh4caOHTsMwzCMw4cPGwCMjz/+2Btz+vRpIyIiwvj73/9uGIZhLF++3ABgfP31196YP/zhD0Z8fLzfv+/y5ctGdHS08Y9//MP7GABj7dq1wV8cItIGe4qIqEnceeedyM7O9vn64x//6P35oUOHMGTIEJ//M2TIEHz11Veorq72PjZgwACl59u3bx8mTpyIP/zhDxg6dKj3OVq0aIFbb73VGxcXF4ebb74Zhw4d8j4WGRmJLl26eL9PSEhAUVGR9/uioiI8+eSTuOmmm+B0OuF0OnH+/HkcO3ZM8dUgIh1xojURNYmoqCh07drV57ETJ054/20YBhwOh8/PDcNo8PcEU1hYiNGjR2PKlCmYMmVKwN/X0HO3bNnS5+cOh8Pn/06ePBnffPMNFi5ciI4dOyI8PBxpaWmorKwM2jYi0hd7iohICykpKdi5c6fPY7t27cJNN92E0NBQ5d9z6dIlPPjgg+jevTsWLFhQ7zkuX76MTz75xPtYcXExDh8+jB49eig/x44dO/Dcc89h1KhR3onhp0+fVv7/RKQn9hQRkRZeeOEFDBw4EK+++ioefvhhZGZmYtGiRXjjjTca9XueeOIJHD9+HB9++CG++eYb7+OxsbHo1q0bHnzwQUydOhVLlixBdHQ0ZsyYgfbt2+PBBx9Ufo6uXbti5cqVGDBgAMrKyvDiiy8iIiKiUe0kIv2wp4iItNCvXz/8/e9/xzvvvIPU1FT8/Oc/xy9+8QtMnjy5Ub9n27ZtKCgoQEpKChISErxfu3btAgAsX74c/fv3x/3334+0tDQYhoENGzbUGzIL5M9//jNKSkrQt29fTJgwAc899xzatWvXqHYSkX4chr9BdiIiIqJmhD1FRERERGBSRERERASASRERERERACZFRERERACYFBEREREBYFJEREREBIBJEREREREAJkVEREREAJgUEREREQFgUkREREQEgEkREREREQDg/wcxskTYpzYQXAAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter(df[\"X1 transaction date\"], df[\"Y house price of unit area\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e29285e3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.747109Z",
     "start_time": "2023-09-10T22:49:25.684920Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter(df[\"X2 house age\"], df[\"Y house price of unit area\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "12b46e71",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.815313Z",
     "start_time": "2023-09-10T22:49:25.750258Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter(\n",
    "    df[\"X3 distance to the nearest MRT station\"], df[\"Y house price of unit area\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3562d19",
   "metadata": {},
   "source": [
    "#### Convert to other formats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e0dae8a0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.823395Z",
     "start_time": "2023-09-10T22:49:25.816192Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "pandas.core.series.Series"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = df[\"Y house price of unit area\"]\n",
    "type(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "22e8de7b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.825692Z",
     "start_time": "2023-09-10T22:49:25.822061Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "list"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y1 = Y.to_list()\n",
    "type(Y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6d1c1506",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.836740Z",
     "start_time": "2023-09-10T22:49:25.824070Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "numpy.ndarray"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y2 = Y.to_numpy()\n",
    "type(Y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a73d2ae1b44cfd77",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.946685Z",
     "start_time": "2023-09-10T22:49:25.828018Z"
    }
   },
   "outputs": [],
   "source": [
    "# save as an excel xlsx file\n",
    "\n",
    "df.to_excel(\"./data/real_estate_valuation_dataset.xlsx\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deb88db1214b8e35",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Split dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "186709eb",
   "metadata": {},
   "source": [
    "#### Convert to numpy arrays\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7fddee9f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.953419Z",
     "start_time": "2023-09-10T22:49:25.947558Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(414, 6)"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = [\n",
    "    \"X1 transaction date\",\n",
    "    \"X2 house age\",\n",
    "    \"X3 distance to the nearest MRT station\",\n",
    "    \"X4 number of convenience stores\",\n",
    "    \"X5 latitude\",\n",
    "    \"X6 longitude\",\n",
    "]\n",
    "X = df[features].to_numpy()\n",
    "\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8301aa09",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.955737Z",
     "start_time": "2023-09-10T22:49:25.951619Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "numpy.ndarray"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c3e88922",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.962570Z",
     "start_time": "2023-09-10T22:49:25.955185Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(414,)"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df[\"Y house price of unit area\"].to_numpy()\n",
    "\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2a484283",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:25.965581Z",
     "start_time": "2023-09-10T22:49:25.959131Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "numpy.ndarray"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e3ff8b0f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:26.446233Z",
     "start_time": "2023-09-10T22:49:25.965760Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a17d38e7",
   "metadata": {},
   "source": [
    "#### Split by ratio\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5ac946c2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:26.453280Z",
     "start_time": "2023-09-10T22:49:26.447509Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape = (331, 6)\n",
      "X_test.shape = (83, 6)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "print(\"X_train.shape =\", X_train.shape)\n",
    "print(\"X_test.shape =\", X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdc1b8af",
   "metadata": {},
   "source": [
    "#### Split by number\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "313aa149",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:26.455263Z",
     "start_time": "2023-09-10T22:49:26.450728Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape = (314, 6)\n",
      "X_test.shape = (100, 6)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=100, random_state=0)\n",
    "\n",
    "print(\"X_train.shape =\", X_train.shape)\n",
    "print(\"X_test.shape =\", X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b619d1ac",
   "metadata": {},
   "source": [
    "#### Use random seed to make it reproducible\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "84fce1a7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:26.533572Z",
     "start_time": "2023-09-10T22:49:26.454025Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([2013.5833333,   35.4      ,  318.5292   ,    9.       ,\n         24.97071  ,  121.54069  ])"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=100)\n",
    "\n",
    "X_train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a543d94",
   "metadata": {},
   "source": [
    "### Train, validation, test split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "36cd5f51",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:26.534152Z",
     "start_time": "2023-09-10T22:49:26.474088Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape = (334, 6)\n",
      "X_val.shape = (38, 6)\n",
      "X_test.shape = (42, 6)\n"
     ]
    }
   ],
   "source": [
    "X_dev, X_test, y_dev, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X_dev, y_dev, test_size=0.1, random_state=1\n",
    ")\n",
    "\n",
    "print(\"X_train.shape =\", X_train.shape)\n",
    "print(\"X_val.shape =\", X_val.shape)\n",
    "print(\"X_test.shape =\", X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd8b8f6f",
   "metadata": {},
   "source": [
    "## Fit linear model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Linear regression "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "8a4ac621c083c2e5"
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7d15367f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:26.534488Z",
     "start_time": "2023-09-10T22:49:26.477107Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f9f7d34a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:26.535493Z",
     "start_time": "2023-09-10T22:49:26.515776Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "LinearRegression()",
      "text/html": "<style>#sk-container-id-1 {color: black;}#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>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dcfc44ad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:26.535679Z",
     "start_time": "2023-09-10T22:49:26.520388Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE on training set = 82.41466453682675\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_train)\n",
    "mae = mean_squared_error(y_train, y_pred)\n",
    "\n",
    "print(\"MSE on training set =\", mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "721bad05",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:26.535826Z",
     "start_time": "2023-09-10T22:49:26.523050Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE on test set = 51.83750374286805\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "mae = mean_squared_error(y_test, y_pred)\n",
    "\n",
    "print(\"MSE on test set =\", mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9d8b092e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:26.615662Z",
     "start_time": "2023-09-10T22:49:26.526108Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7da3b2be",
   "metadata": {},
   "source": [
    "### Cross validation\n",
    "\n",
    "Q: What is the effect of data splitting on the model performance?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6c709b31",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:26.616069Z",
     "start_time": "2023-09-10T22:49:26.599815Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([ -49.89284058,  -89.03269132,  -57.8698601 , -134.81795278,\n        -60.0483656 ])"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_validate\n",
    "\n",
    "model = LinearRegression()\n",
    "cv_results = cross_validate(\n",
    "    model, X, y, cv=5, scoring=\"neg_mean_squared_error\", return_train_score=True\n",
    ")\n",
    "\n",
    "cv_results[\"test_score\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a524efe2",
   "metadata": {},
   "source": [
    "### Ridge regression\n",
    "\n",
    "Q: What is the effect of regularization on the model performance?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5a42a540",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:26.616144Z",
     "start_time": "2023-09-10T22:49:26.607814Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "model = Ridge(alpha=0.1)\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e803f1cf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:26.616930Z",
     "start_time": "2023-09-10T22:49:26.610704Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "55.2204325346289"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ebacf0fd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:26.700673Z",
     "start_time": "2023-09-10T22:49:26.615295Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "35ec7bc7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-10T22:49:26.700905Z",
     "start_time": "2023-09-10T22:49:26.682912Z"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}