01_the _machine_learning_landscape.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## CHAPTER 1 - The Machine Learning Landscape",
   "id": "b4fc95dc8531089c"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Notebook Setup",
   "id": "7744aa93ca407743"
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-11-30T02:09:39.409642Z",
     "start_time": "2024-11-30T02:09:35.275759Z"
    }
   },
   "source": [
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import seaborn as sns\n",
    "\n",
    "from packaging import version\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-30T02:09:39.416781Z",
     "start_time": "2024-11-30T02:09:39.415043Z"
    }
   },
   "cell_type": "code",
   "source": [
    "assert sys.version_info >= (3, 9)\n",
    "assert version.parse(sklearn.__version__) >= version.parse(\"1.2.0\")"
   ],
   "id": "c292dc7944d8d6d9",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-30T02:09:39.420439Z",
     "start_time": "2024-11-30T02:09:39.418950Z"
    }
   },
   "cell_type": "code",
   "source": "np.random.seed(42)",
   "id": "9daea33c0789cd00",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-30T02:09:39.424187Z",
     "start_time": "2024-11-30T02:09:39.422550Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.rc('font', size=12)\n",
    "plt.rc('axes', labelsize=14, titlesize=14)\n",
    "plt.rc('legend', fontsize=12)\n",
    "plt.rc('xtick', labelsize=10)\n",
    "plt.rc('ytick', labelsize=10)"
   ],
   "id": "74a093aef7db105f",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### Example 1-1 Training and running linear model using Scikit-Learn",
   "id": "acf1827e6ca1fecf"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-30T02:09:39.736786Z",
     "start_time": "2024-11-30T02:09:39.477333Z"
    }
   },
   "cell_type": "code",
   "source": [
    "data_root = \"https://github.com/ageron/data/raw/main\"\n",
    "data_file = \"lifesat/lifesat.csv\"\n",
    "\n",
    "# Reading data using pandas\n",
    "data = pd.read_csv(f\"{data_root}/{data_file}\")"
   ],
   "id": "3896098333b2a2d7",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-30T02:09:39.752831Z",
     "start_time": "2024-11-30T02:09:39.746656Z"
    }
   },
   "cell_type": "code",
   "source": "data.describe()",
   "id": "b5d581df26577d80",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       GDP per capita (USD)  Life satisfaction\n",
       "count             27.000000          27.000000\n",
       "mean           41564.521771           6.566667\n",
       "std             9631.452319           0.765607\n",
       "min            26456.387938           5.400000\n",
       "25%            33938.289305           5.900000\n",
       "50%            41627.129269           6.800000\n",
       "75%            49690.580269           7.300000\n",
       "max            60235.728492           7.600000"
      ],
      "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>GDP per capita (USD)</th>\n",
       "      <th>Life satisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41564.521771</td>\n",
       "      <td>6.566667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9631.452319</td>\n",
       "      <td>0.765607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>26456.387938</td>\n",
       "      <td>5.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33938.289305</td>\n",
       "      <td>5.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41627.129269</td>\n",
       "      <td>6.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>49690.580269</td>\n",
       "      <td>7.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60235.728492</td>\n",
       "      <td>7.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-30T02:09:39.776755Z",
     "start_time": "2024-11-30T02:09:39.774553Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Split Data into features (X) and label (Y)\n",
    "X = data[[\"GDP per capita (USD)\"]].values\n",
    "y = data[[\"Life satisfaction\"]].values"
   ],
   "id": "9c45dbf7bebda819",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-30T02:09:39.852401Z",
     "start_time": "2024-11-30T02:09:39.785939Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Set a specific style\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# Create scatter plot\n",
    "sns.scatterplot(data=data,\n",
    "                x=\"GDP per capita (USD)\",\n",
    "                y=\"Life satisfaction\")\n",
    "\n",
    "# Set axis limits\n",
    "plt.xlim(23_500, 62_500)\n",
    "plt.ylim(4, 9)\n",
    "\n",
    "# Add grid\n",
    "plt.grid(True)\n",
    "\n",
    "# Show plot\n",
    "plt.show()\n"
   ],
   "id": "8cef541670dcf5e5",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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FVWJFZEq8vb1haVly6NV6npYtW7YgJSUFQgj07t0bQUFBCAwMLPQ6CwsLVKtWDVWqVCl7xQAiIyPx+PFj9OvXT6f1PT09S226NEqlEpGRkXrZlhSZc//m3Dtg3v2bc++Adv3H/JdV4jaqOdqjcWPvCqiu4pnz/jd076r314bWoaVq1aqoWrUqAGDp0qXw8/NDvXr1dKuwBKdPn4avry+cnJx0Wt/S0lJvH7o+tyVF5ty/OfcOmHf/5tw7UHL/NeS2JQ5friG3lfxnZ877Xwq963Qh7uDBg5GWloYZM2YgOfn/vrxLlizBu+++i5iYGJ0LunbtGtq1a6fz+kREpigtS4GoxExcvZ+CqKTMEi96rSiq4ctdm7tqPM/hy9JnYWGB9Jx8g3/HSqPTNP7Xr1/HuHHjkJeXh2nTpsHV9dkXWC6X4/z58xgxYgT27duHRo0alXnbd+7cwYABA3Qpi4jIJBnTXZI5fNk0OdZuiJl7wo3iO1YSnY60rF+/Hs7Ozvj111/RsmVL9fPvv/8+jhw5gipVqmDt2rU6FZScnAxHR0ed1iUiMjXlGWZcUTh82bSk5+Rj0S//GtV3rDg6hZZr165h7NixqF+/fqFl9evXx6hRo/D333/rVNC1a9c0ZsYlIjJn2twlmag8/nuqwOm7/xW5zNi+YzqFluzsbFhbF3+TKwcHB6SlpelcFBERPcO7JFNFy8jJL2W58XzHdAotTZs2RVhYWLHLjx8/rtP1LEREpIl3SaaKVtWu5Mtbjek7plNoGT58OC5duoS5c+fi5s2byMnJQW5uLm7duoX58+fj/PnzGD58uL5rJSIyO6q7JBeFd0kmfajuYIOAZtWLXGZs3zGdRg+NGDECERERCAkJwc8//6yxTAiBAQMGYOzYsXopkIjInPEuyVTRHO2ssKifOz49esfov2M6hRYA+PzzzzF48GAcO3YMDx48QH5+PurVq4eePXuic+fO+qyRiCQqLUuB5EwF0nPy4GhvDVcHDo3VBYcZU0VLfxSLNSO98eRpnlF/x3QOLQDg5+cHPz8/fdVCRCbEmOYWMQVOVYzvB4RMR0FBARztrODiYGvoUkpUrtCSmpqKzMxMjZsn5ufnIyMjA6dOncL06dPLXSARSU9pc4usG+XDH2AiKjOdQsuTJ08wdepURERElPg6hhYi86TN3CIMLURUVjqNHvryyy8RHh4OT09PdOrUCQDQv39/dOzYEZaWlrCzs8PmzZv1WigRSQfnFiGiiqBTaDlz5gy6du2Kffv2YcWKFRBCYMKECdi2bRt27NgBpVKJqKgofddKRBLBuUWIqCLoFFqSkpLQtWtXAED16tVRvXp1REZGAgB8fHwwaNAgHD58WH9VEpGkcG4RIqoIOoUWGxsb2Nr+3xXG9erVw927d9WPPT09kZCQUP7qiEiSVHOLvBhcjHHeByKSDp0uxG3SpAkiIiLUs942bNgQt27dUi9PT0+HQmE8N1giosrHuUWISN90OtLSp08fhISE4LPPPkNOTg46deqEy5cvY8+ePbh8+TJ27drFew8REZyq2KBpTTm8G7igaU05AwsRlYtOoWX8+PF4+eWXsXPnThQUFKBfv35o3LgxPv30U4wdOxYPHz7EpEmT9F0rERERmTGtTg89evQItWvXVj+2trbGxo0bcevWLVSpUgUAsHv3buzcuROpqakIDAxEhw4dKqZiIiIiMktahZYhQ4bgvffew4gRIwAA69evR69evdCyZUv1axwdHTF16tSKqZKIiIjMnlanhzIyMpCbm6t+vH79ety+fbvCiiIiIiJ6kVZHWurXr4+NGzfi4cOHcHBwAACEhYUhNja22HVkMhmmTZumnyqJiIjI7GkVWj744APMnj0b27dvB/AskISGhiI0NLTYdRhaiIiISJ+0Ci09e/bEn3/+iaioKCgUCrz11luYPHkyL7YlIiKiSqP15HIuLi7w9fUFAPj5+aFDhw7o2LFjhRVGRERE9DydZsTdsWNHoeeUSiXOnDkDS0tLdOrUCRYWOk0BQ0RERFQknUILAKxatQpRUVH4+uuvoVQqMWbMGERERAAAWrduje+++w5yuVxvhRIREZF50+lwyPbt27FlyxZkZGQAAI4dO4bw8HB0794dkyZNwu3bt7Fp0ya9FkpERETmTacjLYcOHULHjh2xbds2AMDvv/8OGxsbrFy5Eg4ODsjIyEBYWBjmzJmj12KJiIjIfOl0pCU2NhZ9+vSBTCYDAJw/fx5eXl7qOVxatWqFhw8f6q9KIjIZaVkKRCVm4ur9FEQlZSIti3eEJyLt6HSkxcbGBkIIAMDNmzfx5MkTjBkzRr08PT0dVatW1U+FRGQyElKzMW//NZy+k6x+rmtzVywb2hZ1ne0NWBkRSYFOR1qaNGmCEydOAABCQkIgk8nQrVs3AEBmZiZCQkLQtGlTvRVJRNKXlqUoFFgA4NSdZATtv8YjLkRUKp1Cy7hx43D69Gm0b98eu3btgpeXFzw8PBAZGYk+ffogOjoa48eP13etRCRhyZmKQoFF5dSdZCRnMrQQUcl0Oj3Ut29fWFlZISQkBLVr18aMGTMAAHZ2dnBwcMCHH36IHj166LVQIpK29Jy8EpdnlLKciEjneVp69eqFXr16aTzXvHlzHDt2rNxFEZHpcbSzLnF51VKWExFpdXqooKCg0GNt/iEiUnGV26Brc9cil3Vt7gpXuU0lV0REUqPVkRYPDw+sWLEC/fv3B/BsxlvVcOfiyGQy3Lx5s/wVEpFJcKpig2VD2yJo/zWcemH00PKhbeFUhaGFiEqmVWjx9fWFq+v//R+Sn59fhRVERKarrrM91o3yQXKmAhk5eahqZw1XuQ0DCxFpRavQ8uINEou6YSIRkTacqjCkEJFudBryPH/+fPXNEYty7tw5vP322zoXRURERPQinULLgQMHEBcXV+zyixcv4uLFizoXRURERPQirU4PxcbGol+/flAqlern5s6di7lz5xa7TvPmzctfHREREdH/p1VoadiwIaZOnYozZ84AAK5evYqGDRuiWrVqhV5raWmJ6tWr491339VvpURERGTWtJ5cburUqZg6dSoAoGXLlpg2bZp6CDQRERFRRdNpRtxbt27puw4iyUvLUiA5U4H0nDw42lvD1YGjZIiI9EnnafyVSiVu3bqFzMxMjdlvlUol0tPTcfr0aSxdulQvRRIZu4TU7EJ3MO7a3BXLhrZFXWd7A1ZGRGQ6dAot9+/fx4QJE/Dw4cMSX8fQQuYgLUtRKLAAz+5cHLT/GtaN8uERFyIiPdBpyPOaNWvw8OFD9O7dGwMHDgQATJo0CUOHDkXVqlVhZ2eHAwcO6LVQImOVnKkoFFhUTt1JRnKmopIrIiIyTTqFlgsXLqBv37746quvsGDBAggh0K1bNyxZsgQ//vgjLC0tERoaqu9aiYxSek5eicszSllORETa0Sm0pKamwtfXFwDg6OiIWrVqqW+O2LBhQwwZMgRhYWH6q5LIiDnaWZe4vGopy4mISDs6hRZ7e3uNuzy7ubnh7t276sfu7u549OhR+asjkgBXuQ26NnctclnX5q5wlfN6FiIifdAptLi7u+Ps2bPqx40aNcKNGzfUjxMTE8tfGZFEOFWxwbKhbQsFl67NXbF8aFtehEtEpCc6jR4aOHAgPv74Y0ybNg0rVqxAYGAgQkJCsGrVKjRv3hw7duyAu7u7vmslMlp1ne2xbpQPkjMVyMjJQ1U7a7jKOU8LEZE+6RRaXn/9dfz777/Ys2cPrKys0KNHD/j5+WHLli0AABsbG8yYMUOvhRIZO6cqDClERBVJ58nlFi5ciOnTp8PW1hYAsG3bNhw9ehSpqano0qULmjZtqrciiYiIiHQOLQDg7Oys/ndra2v1nC1ERERE+qbThbgAcPv2bfz000/qxzt37kTHjh0REBCAb7/9Vh+1EREREanpFFquXLmCoUOH4ptvvgEA/PPPP/jss8+gVCphbW2N5cuX4+jRo3otlIiIiMybTqFl8+bNcHZ2xrJlywAAhw8fBgB8//33CAsLQ7t27bBr1y79VUkmKy1LgajETFy9n4KopEykZXHKeyIiKppO17RcvXoVb731Fry8vAAAZ86cQcOGDdGyZUsAQN++fbF69Wr9VUkmiXdGJiKistDpSEtOTg5cXZ9NpJWcnIw7d+6gQ4cO6uWWlpYQQuinQjJJpd0ZmUdciIjoRTqFlrp16yImJgYAcPLkSchkMnTp0kW9/OLFi6hTp45+KiSTxDsjExFRWel0eqhr167YuXMnsrKycOzYMTg6OiIgIACJiYnYuHEjfv31V0ybNk3ftZIJ4Z2RiYiorHQKLR988AHu37+PH374AVWrVsWyZctga2uLBw8eYPfu3ejcuTPeeustfddKJoR3RiYiorLSKbTY2dlh48aNSElJgVwuh7X1sx+YFi1a4IcffkC7du30WiSZHtWdkU8VcYqId0YmIqKi6Dy5HAC4uLioAwsAODg4lCuwKBQKfPrpp/Dz80OnTp2wevVqXtBronhnZCIiKqtyTeOvb0uWLMGFCxfwzTff4OnTp/jggw9Qt25djBw50tClUQXgnZErTlqWAsmZCqTn5MHR3hquDvxciUj6jCa0pKamYv/+/di+fTvatm0LAHjrrbcQERHB0GLCeGdk/eP8N0RkqowmtFy+fBlyuRz+/v7q5yZNmmTAioikp7T5b9aN8mFIJCLJMprQEhcXBzc3Nxw8eBCbNm1CXl4ehgwZgilTpsDCQvtLb5RKZblrUW1DH9uSInPuX+q9J2Xmljj/TVJmLuS2lsWuL/X+y8OcewfYvzn3b+jey/K+RhNasrKyEBsbiz179mDp0qVISkrCxx9/DHt7+zINn46MjNRbTfrclhSZc/9S7V3pVL/E5U/Ss5EW92+p25Fq//pgzr0D7N+c+5dC7+UKLb/99hvCwsIQHx+PBQsWoEqVKjh16hRGjx4NOzu7shViZYXMzEysWrUKbm5uAICEhATs3r27TKHF09MTlpbF/5+kNpRKJSIjI/WyLSky5/6l3nvMf1klLq/maI/Gjb2LXS71/svDnHsH2L8592/o3lXvrw2dQktBQQHef/99hIWFQQgBmUyGp0+f4uHDh1ixYgVCQ0OxdetWyOVyrbdZo0YN2NraqgMLADRu3BgPHz4sU22WlpZ6+9D1uS0pMuf+pdp7DbltifPf1JDbatWXVPvXB3PuHWD/5ty/FHrXaZ6W7777DqGhoZgyZQoOHz6snkulS5cumDhxIsLDw7F9+/YybdPLywu5ubnqexoBQHR0tEaIoYqVlqVAVGImrsWnw75mI6Tn5Bu6JElLy1IgOikT/z7OwK1H6bgS+wRRSZkVejNIzn9DRKZMpyMtISEh6NGjB2bOnImUlBT18w4ODpgzZw7i4+Px66+/YsaMGVpvs0mTJujWrRvmz5+PRYsWISkpCZs3b8aUKVN0KZHKiMNk9SshNRsfH7qOkf4NsP2vGPx19z/1sor+XDn/DRGZKp2OtMTGxqJTp07FLu/YsSMSEhLKvN0vvvgCDRo0wKhRozBv3jyMGTMG48aN06VEKoPShslW5JEBU6T6PFvWcSwUWIDK+VydqtigaU05vBu4oGlNOQMLEZkEnY602NvbIyur+Av+EhMTYWtrW+btVq1aFStWrNClJCqH5ExFicNkkzMV/NErA9XnOaFTI6w/cbfI1/BzJSIqO52OtPj4+ODAgQPIzy98zcOTJ0+wZ88eeHt7l7c2qiTpOXklLs8oZTlpUn2eufkFJb6OnysRUdnoFFqmTp2K+/fvY+TIkfjxxx8hk8lw+fJlbNmyBQMHDkRKSgomTpyo71qpgjjaWZe4vGopy0mT6vO0tSr5z4ufKxFR2egUWtq2bYs1a9YgPj5efSfmDRs2YNWqVcjKysJnn30GPz8/fddKFcRVblNotIlK1+aucJXzFEZZqD7Pq3Gp6NysepGv4edKRFR2Wl3TEh4ejmbNmmnMu9KjRw907twZf/31F2JiYlBQUIB69eohICAAVatWrbCCSf9Uw2SD9l/TmN+Dw2R1o/o8Pzl0HW92bgwAhUYP8XMlIio7rULL5MmTMWfOHAwfPhwAMH/+fIwcORJeXl7o0aNHhRYoZWlZCiRnKpCekwdHe2u4OhjvsNPnh8mm5+TBRlaAutXkcHEo+wXV9Ozz/GK4F/57qsCi/h5QFghkKZRwsufwYyIiXWkVWrKzszXmYzlw4AA6deoELy+vCitM6qQ474lTlWc/pkqlEuHh4XCs623okiRN9XkSEZF+aBVamjVrhq+//hrXr1+Hg4MDAGDfvn04e/ZssevIZDJ8/vnn+qlSYkqb92TdKB/+mBEREZWRVqFlwYIFmDlzJkJDQwE8CySXLl3CpUuXil3HnEML5z0hIiLSP61Ci6+vL86cOYPExETk5uaid+/eCAoKQmBgYEXXJ0mc94SIiEj/tJ4R18LCArVr1wYADB48GD4+PmjQoEGFFSZlnPeEiIhI/3Saxn/p0qX6rsOkqObpOFXEKSLOz0FERKQbrUJLt27dsHDhQrzyyivqx6WRyWQ4efJkuYqTKs57YpqkNISdiMgUaRVaLCwsIJPJNB5TyZ6f9yQjJw9V7Tg/h5RJcQg7EZGp0Sq0nDhxosTHVDTO02EaOISdiMg4VMghk7S0NCQkJFTEpokqnTZD2ImIqOJVSGhZvXo1p/cnk8Eh7ERExoEXpxCVgkPYiYiMA0MLUSlUQ9iLwiHsRESVh6GFyi0tS4GoxExcvZ+CqKRMpGWZ1jUeqiHsLwYXDmEnIqpcOk0uR6RiLkOBOYSdiMjweKSFdFbaUGBTPOLStKYc3g1c0LSmnIGFiKiSaXWkpaS7ORclMTFRp2JIWng3ayIiqkxahZZx48ZpzIhbGiFEmV5P0sShwEREVJm0Ci2DBg1iCKFCOBSYiIgqk1ahZdmyZRVdB0kQ72ZNRESViRfiks44FJiIiCoThzxLWFqWAsmZCqTn5MHR3hquDpU/BJdDgYmIqLIwtEiUMc2PwrtZExFRZeDpIQkyt/lRiIiIAIYWSdJmfhQiIiJTw9AiQZwfhYiIzBFDiwRxfhQiIjJHDC0SpJofpSicH4WIiEwVQ4sRSMtSICoxE1fvpyAqKbPUC2lNZX6UsvZNRETmjUOeDUzXoctSnx/FmIZsExGRNPBIiwGVd+iyUxUbNK0ph3cDFzStKZdMYOGQbSIi0gVDiwGZ69Blc+2biIjKh6HFgMx16LK59k1EROXD0GJA5jp02Vz7JiKi8mFoMSBzHbpsrn0TEVH5MLQYkKkMXS4rc+2biIjKh0OeDUzqQ5d1Za59ExGR7hhajIBTFfP8sTbXvomISDc8PURERESSwNBCREREksDQQkRERJLA0EJERESSwNBCREREksDQQkRERJLA0EJERESSwNBCREREksDQQkRERJLA0EJERESSwNBCREREksDQQkRERJLA0EJERESSwNBCREREksDQQkRERJLA0EJERESSwNBCREREksDQQkRERJLA0EJERESSYFShJSwsDC1atND4Z+bMmYYui4iIiIyAlaELeN7du3fRvXt3LF68WP2cra2tASsiIiIiY2FUoSUqKgru7u6oUaOGoUshIiIiI2NUp4eioqLQqFEjQ5dBRERERshojrQIIRATE4MzZ84gODgYSqUSffr0wcyZM2FjY6P1dpRKZblrUW1DH9uSInPu35x7B8y7f3PuHWD/5ty/oXsvy/vKhBCiAmvRWnx8PAIDAzF48GCMHz8eDx48wJIlS9CzZ08sXLiw1PWVSiXCw8MrvlAiIiLSO29vb1haWpb4GqMJLQCQmpoKJycnyGQyAMCxY8cwd+5cXL16tdRGVKHF09Oz1NeWRqlUIjIyUi/bkiJz7t+cewfMu39z7h1g/+bcv6F7V72/NqHFaE4PAYCzs7PG46ZNmyI3NxdpaWmoVq2aVtuwtLTU24euz21JkTn3b869A+bdvzn3DrB/c+5fCr0bzYW4p0+fxksvvYTs7Gz1c//88w+cnZ21DixERERkuowmtPj4+MDW1hYLFy5EdHQ0/vzzT6xYsQITJ040dGlERERkBIzm9JBcLsc333yDzz//HEOHDoWDgwNGjhzJ0EJEREQAjCi0AEDz5s2xfft2Q5dBRERERshoTg8RERERlYShhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCSBoYWIiIgkgaGFiIiIJIGhhYiIiCTBaEPLpEmTEBQUZOgyiIiIyEgYZWj55Zdf8Oeffxq6DCIiIjIiRhdaUlNTsWLFCnh6ehq6FCIiIjIiVoYu4EXLly/HwIEDkZiYaOhSiIiIyIgYVWg5d+4c/v77bxw5cgSLFi0q07pCCACAQqGApaVluepQKpV625YUmXP/5tw7YN79m3PvAPs35/4N3bvq/VW/4yWRCW1eVQlyc3PRv39/fPTRRwgICFBfhLts2TKt1lcoFIiMjKzIEomIiKiCeHp6wsbGpsTXGM2RlvXr16NNmzYICAjQaX0rKyt4enrCwsICMplMz9URERFRRRBCoKCgAFZWpUcSoznSEhgYiOTkZPWhKYVCAQCwsbHB1atXDVkaERERGQGjCS3x8fHIz89XP/7iiy8AAHPmzEHDhg0NVRYREREZCaM5PeTm5qbx2MHBAQAYWIiIiAiAEc7TQkRERFQUozk9RERERFQSHmkhIiIiSWBoISIiIklgaCEiIiJJMLnQ8vjxY8ycORP+/v4ICAjA0qVLkZubCwBYsmQJWrRoofHPzp071ev+/PPPeOWVV+Dl5YVp06bhyZMn6mVCCHzxxRfo0KED/P39sWLFChQUFKiXp6SkYMaMGfDx8UFgYCAOHTpUeU0/JzY2Fm+//TZ8fHzQrVs3bN26Vb0sLi4OEyZMgLe3N/r27YszZ85orHv27Fm89tpr8PLywhtvvIG4uDiN5d9++y0CAgLg4+ODBQsWIDs7W70sNzcXCxYsgK+vL7p06YJt27ZVbKPFKKl/c9j/KpMmTVLPKg0AN2/exPDhw+Hl5YWhQ4fi+vXrGq835d6nTJlSaL+fPHlSvbw83+vS/qYqS1hYWKEeZ86cCcA89n1J/Zv6/lcoFPj000/h5+eHTp06YfXq1erp8E1y3wsTUlBQIF5//XUxceJE8e+//4pLly6Jnj17imXLlgkhhJgwYYIIDg4WiYmJ6n+ysrKEEEJERESItm3bigMHDoh//vlHjB07VkyaNEm97W+++Ua8/PLL4tKlS+LcuXOiS5cuYuvWrerlkydPFuPHjxe3b98W+/btE23atBERERGV2r9SqRS9evUSs2fPFjExMeKPP/4Q7dq1E4cPHxYFBQWif//+Yvbs2eLu3bti06ZNwsvLS8THxwshhIiPjxfe3t7im2++Ef/++6947733xGuvvSYKCgqEEEL89ttvon379uLEiRMiIiJC9O3bV3z66afq9/7f//4n+vfvL65fvy5CQ0OFj4+P+PXXX42mfyFMf/+r/Pzzz8Ld3V3MmzdPCCHE06dPRefOncWyZcvE3bt3xeLFi0WnTp3E06dPhRCm3bsQQvTs2VMcOnRIY7/n5uYKIcr3vS7tb6oyff3112Ly5MkaPaalpZnNvi+ufyFMf/9/9NFHolevXiIiIkKcPXtWvPTSS2L37t0mu+9NKrTcvXtXuLu7i6SkJPVzR44cEV26dBFCCBEQECBOnz5d5Lpz587V+A9dQkKCaNGihbh//74QQoiXX35Z7N+/X7384MGDonv37kIIIWJjY4W7u7uIi4tTL1+wYIHG9irD48ePxXvvvScyMjLUz02bNk188skn4uzZs8Lb21v9hRVCiPHjx4u1a9cKIYT46quvxNixY9XLsrKyhI+Pjzh//rwQQojRo0erXyuEEJcuXRJt27YVWVlZ4unTp8LT01P9WiGE2LBhg8b2KkNJ/Qth+vtfCCFSUlJE165dxdChQ9Xv/+OPP4rAwEB1AC0oKBA9e/ZU92PKvefm5opWrVqJ6OjoItcpz/e6tL+pyjR79myxatWqQs+by74vrn9T3/8pKSmidevW4sKFC+rngoODRVBQkMnue5M6PVSjRg1s3boVrq6uGs9nZmYiMzMTjx8/RqNGjYpcNyIiAr6+vurHderUQd26dREREYHHjx/j4cOH8PPzUy9v37494uPjkZiYiIiICNSpUwf16tXTWF7Ztx+oWbMmvvrqK8jlcgghcPnyZVy6dAn+/v6IiIhA69atUaVKFY0aw8PDARTu397eHh4eHggPD4dSqURkZKTGcm9vb+Tl5eHWrVu4desW8vPz4ePjo7HtiIgIjcOJFa2k/s1h/wPA8uXLMXDgQDRr1kz9XEREBNq3b6++J5dMJkO7du2K3fem1Ht0dDRkMhnq169f6PXl/V6X9jdVmaKioor8bpvLvi+uf1Pf/5cvX4ZcLoe/v7/6uUmTJmHp0qUmu+9NKrQ4Ojpq3HCxoKAAO3fuRIcOHRAVFQWZTIZNmzaha9euGDBgAA4cOKB+bWJiImrWrKmxverVq+PRo0dISkoCAI3lqmCkWl7Uuo8fP9Z7j9oKDAzE6NGj4ePjg969exdb46NHjwCgxOXp6enIzc3VWG5lZQVnZ2d1/y4uLhp353R1dUVubi5SU1MrrskSvNi/Oez/c+fO4e+//8bUqVM1ni9t35ty79HR0ZDL5fjwww/RpUsXDBs2DH/++ScAlPt7XdrnWlmEEIiJicGZM2fQu3dvvPLKK/jiiy+gUCjMYt+X1L+p7/+4uDi4ubnh4MGD6NOnD3r06IENGzagoKDAZPe90UzjXxFWrlyJmzdv4qeffsKNGzcgk8nQpEkTjB07FpcuXcJHH30EuVyOnj17Iicnp9AtsW1sbKBQKJCTk6N+/Pwy4NlFUNnZ2cWuayhr165FcnIyFi1ahKVLl5ZaY0nLi+r/+eVCiCKXATDYZ/Bi/x4eHia9/3Nzc/HJJ5/g448/hp2dncay0uoz5d6jo6ORk5ODLl26YNKkSQgLC8OUKVOwd+9e9X+Edf1eG0PvAJCQkKCu5auvvsKDBw+wZMkS5OTkmPy+B0ru39nZ2aT3f1ZWFmJjY7Fnzx4sXboUSUlJ+Pjjj2Fvb2+y+95kQ8vKlSvx3Xff4csvv4S7uzuaN2+O7t27w9nZGQDQsmVL3Lt3D7t370bPnj1ha2tb6ANXKBSwt7fX2Fm2trbqfweenUYpbt0X/wNamTw9PQE8+w/6nDlzMHToUI2r4gHNGovrwdHRsVDPzy+3t7eHUqkschkAg30GL/Z/5coVk97/69evR5s2bTSONKqUVp8p9z516lSMGzcOTk5OAJ7t9xs3bmDfvn344IMP1PU+T9vvta2tbaEjiYb4u3dzc8OFCxfg5OQEmUyGVq1aoaCgAHPnzoW/v79J73ug5P6vXr1q0vvfysoKmZmZWLVqlfr+fQkJCdi9ezcaNmxokvvepE4PqSxevBjbt2/HypUr0bt3bwDPzuepfrBUmjRpoj6cVatWLSQnJ2ssT05ORo0aNVCrVi0AUB8ye/7fVcuLW7cyJScn4/fff9d4rlmzZsjLy0ONGjWKrFF1iK+kHpydnWFra6uxPD8/H6mpqer+U1JSNO7SnZSUBDs7Ozg6Ouq7zWKV1H9mZqZJ7/9ffvkFv//+O3x8fODj44MjR47gyJEj8PHxKbY+bfa91Hu3sLBQ/2CpqPZ7eb/XpX2ulcnZ2Vl97QIANG3aFLm5ueX6u5fCvlcprv+0tDST3v81atSAra2txg2HGzdujIcPH5rs373JhZb169djz549WL16Nfr166d+fs2aNZgwYYLGa2/duoUmTZoAALy8vHD58mX1socPH+Lhw4fw8vJCrVq1ULduXY3lly9fRt26dVGzZk14e3sjPj5e41zm5cuX4e3tXTFNFuPBgweYPn26xnnF69evo1q1amjfvj1u3LihPuynqtHLywtA4f6zs7Nx8+ZNeHl5wcLCAp6enhrLw8PDYWVlhZYtW6JVq1awsrLSuADt8uXL8PT0hIVF5X3FSup/x44dJr3/d+zYgSNHjuDgwYM4ePAgAgMDERgYiIMHD8LLywtXr15Vz90ghMCVK1eK3fem1HtQUBDmz5+v8XrVfi/v99rLy6vEv6nKcvr0abz00ksaR1L/+ecfODs7qy+ONNV9D5Tc/4oVK0x6/3t5eSE3NxcxMTHq56Kjo+Hm5ma6f/cVPj6pEt29e1e0atVKfPnllxpj8hMTE0VERIRo3bq12Lp1q4iNjRW7du0Sbdq0EVeuXBFCCHHlyhXh4eEh9u3bpx6zPnnyZPW2g4ODRZcuXcT58+fF+fPnRZcuXcS2bdvUy9966y0xduxY8c8//4h9+/YJT0/PSp+vID8/XwwZMkS89dZb4s6dO+KPP/4QnTp1Et9++63Iz88Xffv2Fe+//774999/RXBwsPD29lbPKRAXFyc8PT1FcHCwep6W/v37q4fL/fzzz6Jdu3YiLCxMREREiH79+onFixer3/ujjz4S/fr1ExERESIsLEy0a9dOHDt2zGj6N4f9/7x58+aphx9mZGSIDh06iMWLF4s7d+6IxYsXi86dO6uHappy78eOHRMeHh7iwIED4t69e2LdunWibdu26qGa5flel/Y3VVkyMjJEQECAmDVrloiKihJ//PGH6NKli9i8ebNZ7PuS+jeH/T9p0iQxYsQI8c8//4hTp06JDh06iO+++85k971JhZbg4GDh7u5e5D9CCBEWFib69+8vPD09RZ8+fQr9qO7fv1+8/PLLwtvbW0ybNk08efJEvSw/P198/vnnwtfXV7z00kti5cqV6h90IYRITk4WkydPFp6eniIwMFAcOXKkcpp+waNHj8S0adNEu3btROfOncXGjRvVdd67d0+MGTNGtGnTRvTr10/89ddfGuv+8ccfolevXqJt27Zi/Pjx6vH6KsHBwaJjx46iffv2Yv78+SInJ0e9LCsrS3z44YfC29tbdOnSRWzfvr3Cey1KSf2bw/5Xef6HW4hnE0kNGjRIeHp6imHDhokbN25ovN6Ue9+3b5/o1auXaNOmjRg8eLC4ePGixuvL870u7W+qsvz7779iwoQJwtvbW3Tu3FmsW7dOvY/MYd+X1L+p7//09HQxd+5c4e3tLTp27Gjy+14mxP8/dkRERERkxEzumhYiIiIyTQwtREREJAkMLURERCQJDC1EREQkCQwtREREJAkMLURERCQJDC1EREQkCSZ7w0SiinblyhWEhITg8uXLePz4MYQQqF27Njp16oQ33ngDDRs21Hh9SEhIoSnFZTIZ7OzsUKtWLXTu3Blvv/22xn1EilsPeHazNEdHR7Ru3Rrjxo1Dt27d9N6jqRk3bhwuXryIGzduwMrq//7zd+/ePTRq1Ejv75efn49hw4Zh0KBB6ttItGjRAm5ubjhx4kSx6wUGBiI+Ph63b9/WeD45ORlbt27FqVOnkJCQAJlMhtq1a6Nz58548803C313VNt5nrW1NeRyOVq0aIHBgwdj0KBBGsuzs7PRp08fBAUF4dVXX9W9eaIKwNBCVEYKhQLLli3Drl27UKdOHbz66qto1KgRCgoKcPPmTYSEhGDv3r1YsWIF+vbtW2j9nj17omfPngCe3Q8kMzMT//zzD3788UccPHgQW7duRbt27UpcDwAKCgqQlJSEPXv2YPLkyViyZAmGDx9ecY2bgHfffRfDhg2DpaUlgGef/+TJk5GdnY0dO3bo/f2Cg4ORnZ2NMWPGlHtbUVFRGDNmDJRKJQYOHIjGjRsDAG7fvo19+/Zh//792LJlC3x9fQutu2LFCvW/5+bmIikpCWFhYZg3bx5OnjyJL7/8Un2fMHt7e8yaNQv/+9//8NJLL6FatWrlrp1Ibypl3l0iE7Js2TLh7u4u5s+fL3Jzcwstf/DggQgMDBReXl7iwYMH6uf3798v3N3dxdq1a4vc7s2bN4W3t7fw9/cXqampWq/36NEj4eXlJfz9/Yush4qXl5cn3N3dxdixY/W+7fv37wsPDw9x+PBhjefd3d1F9+7dS1y3e/fu6tuPqLzzzjvC09NTREdHF3r9tWvXRJs2bUSvXr00plovajsq+fn5Yt68ecLd3V2sX79eY1lBQYHo16+fCAoKKrFOosrGa1qIyuDWrVvYvn07WrVqhf/973+wsbEp9Bo3NzcsXLgQ2dnZ2Ldvn9bbbtWqFebMmYPU1FR8//33Wq9Xq1YtdOjQAampqbh7967W61HF2rZtG5ycnNCnTx+9bO/SpUto1KiR+gjL8zw9PdG5c2c8ePBA4y7nJbG0tMSnn36KunXrYtu2bcjMzFQvk8lkGDVqFI4cOaJxJ18iQ2NoISqDkJAQCCEwffp0jWsiXvTyyy9j69atmDJlSpm2P2jQIFhbW+P48eNlWk91aD8/P7/Y1wQFBaF169aIi4vDpEmT4OPjgw4dOmDWrFmIi4sr9Pro6GjMmjULHTt2RJs2bdCrVy989dVXyMnJ0XhdixYt8NFHH2Hx4sXw9vaGv78/QkNDS6z3t99+w9ixY9G+fXv4+/tj3LhxOHv2rMZrEhMT8dlnn6F3795o27Yt2rZti759+2LDhg0afa5btw4tWrTAzZs3MXv2bLRv3x5+fn6YPHkybt68qbHNcePGoUWLFsjPz8eFCxfg4eEBALh48SJatGiBkJAQAM9OG+3btw+jR4+Gr68vPDw80KVLF8yaNQuxsbEl9gYAqampCAkJQZ8+fWBtbV3q67Uhl8sRFRVV6HNSWbt2LW7cuIHatWtrvU1bW1u89tpryMzMxPnz5zWW9e3bF0qlEt9991256ibSJ4YWojI4d+4cZDIZOnXqVOLrLCwsEBAQADs7uzJt38HBAfXr18etW7dKDCDPy8zMxNWrV2Fvb4/mzZuX+FohBN544w1kZGTggw8+wMCBAxEaGorXX39d44LNa9euYdiwYfj7778xZswYLFiwAN7e3ti0aRPGjx+P3Nxcje3+/PPPOHfuHD788EMMHToUfn5+xdawYcMGvPfee0hPT8e7776L6dOn47///sPEiRNx8uRJAEBGRgZGjBiBkJAQ9OrVCx999BFmzJgBmUyGtWvXYs2aNYW2O336dPz777+YPn06xo4di7///hujR49GZGRkkXU0bdoUy5cvBwA0adIEK1asUNf92Wef4aOPPkK1atUwa9YsLFiwAP7+/jh69CgmTJiAvLy8Ej/nM2fOICcnB507dy7xdWUxbtw45Ofn480338Tw4cOxdu1anD17Fk+fPgWAIo/6aaNVq1YAgBs3bmg87+LigtatW+P3338vX+FEesQLcYnKICEhAS4uLqhSpUqhZU+ePCn0nKWlJZycnMr0Hs7OzhBCIDU1Fa6ururns7OzNd4jLy8PMTEx+Prrr/HkyRNMmzYN9vb2JW67oKAATZs2RXBwsPpiVD8/P0ybNg1fffUVVq5cCSEEFixYAEdHRxw8eBDOzs4AgNGjR8PPzw8LFy7E999/j3feeUe93aysLAQHB6N+/folvn9cXBw2bNgAPz8/bNu2Tf1DO2DAAPTu3Rtr1qxB9+7dceDAASQkJGDt2rXo3bu3ev0RI0agU6dOOHnyJGbPnq2xbblcjr1796o/g1deeQXDhg3D0qVL8cMPPxSqxdXVFa+99hrmzZsHV1dXDBw4EACQkpKC3bt3o3v37li/fr369aqLYH/77TfcunULnp6exfapOmrRsmXLEj+PsnjnnXdQUFCAjRs34tq1a7h27RqAZ6OB2rVrh7Fjx6JXr15l3q5q/xb1/W3VqhV+/PFHxMfHFxqZRGQIDC1EZVBQUICCgoIil3Xs2LHQc7Vq1cKpU6fK9B6q/4uXyWQaz3/zzTf45ptvCr3eyckJM2bMwNSpU7Xa/vTp09WBBXj24960aVMcP34cSqUSd+7cwZ07dzBy5EgUFBRo/Jh1794dtra2CAsL0wgt9erVKzWwAFC/x7hx4zSODDg7O2PXrl3qMPjGG2+gX79+cHFx0Vj/yZMnqFq1qsb1FyqTJ0/WCG2qUzqnT59GcnKyRgAsiYuLC/7++28olUqN59PT09XbL+r9nxcbGwtra2vUrVtXq/fUhkwmw7vvvovRo0fjjz/+wLlz53Dp0iXExcXhwoULuHDhAoYMGYLPP/+80HenJMV93wCgQYMGAID79+8ztJBRYGghKoM6deogOjoaCoWi0OH47du3azyeO3euTu/x5MkTWFtbw9HRUeP5gQMHasypYWNjAxcXFzRq1EgjhJTG3d290HNNmjRBVFQU/vvvP0RHRwMA9uzZgz179hS5jRfn/tA2EKiunWnSpEmhZc2aNdN4bGFhgW3btiEiIgL3799HXFyc+lRIrVq1Cq1fXF+nTp1CXFyc1jUCz671OHHiBI4fP4579+4hPj4ejx8/Vv+wCyFKXP/JkyeQy+XFbru0U3/5+fnFnlp0dHTEgAEDMGDAAADPPtNDhw5h8+bNCAkJwSuvvIIePXqU1qJGrQBQvXr1It8LAP777z+tt0dUkRhaiMrA398fUVFR+Ouvv9C9e3eNZS9e56LNj9OLnjx5gvj4eHh4eBS6gLN+/fqlXktTGplMVuQFxKo6rays1D/II0eO1Dg187wXt6G6ELg0Jf1f/fOuXbuGt956C/n5+ejQoQMCAgLQvHlztGvXDuPGjSvyaFdR13SojpaUdNH0ixQKBd5++21cvHgRnp6e8PDwQN++fdG6dWv8+eefCA4OLnUbFhYWxR6Rc3JyQkZGRrHrCiGQnp6uEVqvXLmCY8eOYeDAgWjdurXG6+vXr4/p06fDzc0NQUFBOHfuXJlCi+qanzZt2hRapvr8tN2/RBWNoYWoDIYPH449e/Zgy5Yt6Nq1a5mOcGjj8OHDAKC3YbIvEkLg/v37hY5qxMTEwMXFBS4uLqhXr576tS+GpIKCAhw7dkyrU0FFUW07JiamUA3ff/89bt++jfnz52P16tXIzMzE4cOHNY6g5OXlISUlpcjrhO7du1doFuLo6GhYWVmpT3No49dff8XFixfx9ttv48MPP9RYduDAAa22UaNGDdy5cwdKpbLQd6Rly5bqoz9FfY4xMTHIzs7GSy+9pH4uISEB3377LSwsLAqFlue3C6DU65qep1Ao8Ntvv6Fq1apFBuKUlBQAQM2aNbXeJlFFYnwmKgMPDw+8/fbbuHz5MoKCgpCVlVXoNQqFAsHBwWWe3yIqKgrr169H9erVMWrUKH2VXMjWrVs1Hh89ehT37t1Dv379IJPJ0KZNG7i5ueHQoUOIiYnReO3evXvx/vvvY//+/Tq99yuvvAKZTIadO3dqHIVKS0vD5s2bcfXqVcjlcqSkpMDOzq5QCNmxYwdycnIKXW8CPLvm5/nnw8PDcfbsWQQEBBR7MbQqUDx/VET1Q/3i6abY2FgcO3YMQMlDy4Fn4UwIgYSEhELLVFPjr1mzptDRGCEENm/eDAB47bXX1M8HBgaievXq2L17d6GhySq7du0CgGKPjr1ICIElS5bgv//+wzvvvANbW9tCr1GdBlSFTSJD45EWojKaNWsWLC0tsWXLFpw5cwa9e/dG8+bNYWFhgTt37iA0NBRJSUlwc3PDggULCq1/+/ZtHDp0CMCzH46nT5/ixo0bOHr0KKysrLBu3TpUrVq1wuo/evQoUlNTERAQgJiYGOzevRsNGjTAzJkzATz7IV+yZAkmT56MYcOGYeTIkWjYsCEiIyOxf/9+NGjQQOuLfl/UpEkTvPvuu9i4cSNGjBiB/v37q+dESU1NxapVqwAAPXr0wIYNG/Dmm2/itddegxACp06dwh9//AE7OztkZmZCCKFxmik8PBzjxo3Dq6++isTEROzcuRPOzs5F7gMVmUyGatWq4datW/jhhx/g6+uLgIAArFq1CsuWLUN8fDxq1qyJO3fuYP/+/eqwUtLpHQDo2rUr9u7diytXrhQ6mjJ48GCcOHECR44cwb1799C7d29Ur14dycnJOHHiBK5evYqBAweif//+6nWqVKmCdevWYdKkSZgwYQJefvlldOjQAY6OjkhKSkJoaChu3LiBDz74oMjTPKrvG/AsVD9+/BjHjx/HzZs38eqrr2pcVP28K1euwN3dvUxzvxBVJIYWojKytLTErFmz0K9fP4SEhODs2bP45ZdfkJubC1dXV/j6+qJXr17o1atXkddShIWFISwsTP24SpUqcHNzw+uvv44JEybodcRJUTZu3IitW7di+fLlcHJywogRIzBjxgyNoxGdOnXCvn37sHHjRoSEhCAjIwO1a9fG6NGjMXnyZNSoUUPn93///ffRpEkT7NixA19++SXs7e3Rpk0bLF++HG3btgUATJ06FZaWljh48CCWLl0KJycnNG7cGBs2bEBkZCQ2bdqEixcvapxCWbp0KY4dO4bVq1fD1tYWPXv2xHvvvVfqqJegoCCsWrUKn3/+uXremM2bN2Pt2rXYtm0bgGcXYI8dOxZ9+vTBoEGDcPr0afTr16/YbXbu3Bl2dnY4f/68eii1ikwmw7p163DgwAEcOnQI3377LdLS0uDs7IxmzZph9erVRW67ffv2OHbsGL777jucOXMGGzduRFZWFpydndGuXTsEBQXB39+/yHqeP81lZWUFZ2dntGrVCqtWrVIfYXvR48ePce/ePbz77rslfn5ElUkmSrsMnohMQlBQEA4cOIDQ0NBCp12kbN26dVi/fj22b99e7guV9WnRokU4fPgwzpw5U+S8PsZu48aN2LBhA0JDQys8SBNpi9e0EBFVgMmTJ0OhUOCXX34xdCllplQq8dNPP2HIkCEMLGRUGFqIiCpAnTp18Pbbb2Pjxo1QKBSGLqdMQkJCkJmZienTpxu6FCINDC1ERBVk2rRpkMvlZbprt6FlZWVhzZo1+OSTTzjUmYwOr2khIiIiSeCRFiIiIpIEhhYiIiKSBIYWIiIikgSGFiIiIpIEhhYiIiKSBIYWIiIikgSGFiIiIpIEhhYiIiKSBIYWIiIikoT/ByOHkdtSaa2vAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "##### Using LinearRegression",
   "id": "8748f55419cce71d"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-30T02:09:39.871510Z",
     "start_time": "2024-11-30T02:09:39.865928Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Select a linear model\n",
    "model = LinearRegression()\n",
    "\n",
    "# Train the model\n",
    "model.fit(X, y)\n",
    "\n",
    "# Make a prediction for Cyprus\n",
    "X_new = [[37_655.2]]            # Cyprus' GDP per capita in 2020\n",
    "print(model.predict(X_new))     # outputs [[6.30165767]]"
   ],
   "id": "133f7b255a50ec43",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.30165767]]\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "##### Using KNeighborsRegressor",
   "id": "580e18ae78f523a9"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-30T02:09:39.939379Z",
     "start_time": "2024-11-30T02:09:39.936767Z"
    }
   },
   "cell_type": "code",
   "source": [
    "model = KNeighborsRegressor(n_neighbors=3)\n",
    "model.fit(X, y)\n",
    "print(model.predict(X_new)) "
   ],
   "id": "84f26bf8c2f7d6ff",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.33333333]]\n"
     ]
    }
   ],
   "execution_count": 10
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}