ict_competition_latam_2022-2023.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b65c1d0b-0e0c-416c-ad77-e5e4175029b7",
   "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>phone_name</th>\n",
       "      <th>brand</th>\n",
       "      <th>os</th>\n",
       "      <th>inches</th>\n",
       "      <th>resolution</th>\n",
       "      <th>battery</th>\n",
       "      <th>battery_type</th>\n",
       "      <th>ram(GB)</th>\n",
       "      <th>announcement_date</th>\n",
       "      <th>weight(g)</th>\n",
       "      <th>...</th>\n",
       "      <th>video_1080p</th>\n",
       "      <th>video_4K</th>\n",
       "      <th>video_8K</th>\n",
       "      <th>video_30fps</th>\n",
       "      <th>video_60fps</th>\n",
       "      <th>video_120fps</th>\n",
       "      <th>video_240fps</th>\n",
       "      <th>video_480fps</th>\n",
       "      <th>video_960fps</th>\n",
       "      <th>price(USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Y6II Compact</td>\n",
       "      <td>Huawei</td>\n",
       "      <td>Android 5.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>720x1280</td>\n",
       "      <td>2200</td>\n",
       "      <td>Li-Po</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>140.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K20 plus</td>\n",
       "      <td>LG</td>\n",
       "      <td>Android 7.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>720x1280</td>\n",
       "      <td>2700</td>\n",
       "      <td>Li-Ion</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>140.0</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P8 Lite (2017)</td>\n",
       "      <td>Huawei</td>\n",
       "      <td>Android 7.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1080x1920</td>\n",
       "      <td>3000</td>\n",
       "      <td>Li-Ion</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>147.0</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>420.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Redmi Note 4</td>\n",
       "      <td>Xiaomi</td>\n",
       "      <td>Android 6.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1080x1920</td>\n",
       "      <td>4100</td>\n",
       "      <td>Li-Po</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>165.0</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P10</td>\n",
       "      <td>Huawei</td>\n",
       "      <td>Android 7.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1080x1920</td>\n",
       "      <td>3200</td>\n",
       "      <td>Li-Ion</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>145.0</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>420.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       phone_name   brand           os  inches resolution  battery  \\\n",
       "0    Y6II Compact  Huawei  Android 5.1     5.0   720x1280     2200   \n",
       "1        K20 plus      LG  Android 7.0     5.3   720x1280     2700   \n",
       "2  P8 Lite (2017)  Huawei  Android 7.0     5.2  1080x1920     3000   \n",
       "3    Redmi Note 4  Xiaomi  Android 6.0     5.5  1080x1920     4100   \n",
       "4             P10  Huawei  Android 7.0     5.1  1080x1920     3200   \n",
       "\n",
       "  battery_type  ram(GB) announcement_date  weight(g)  ...  video_1080p  \\\n",
       "0        Li-Po        2        2016-09-01      140.0  ...        False   \n",
       "1       Li-Ion        2        2016-12-01      140.0  ...         True   \n",
       "2       Li-Ion        4        2017-01-01      147.0  ...         True   \n",
       "3        Li-Po        4        2017-01-01      165.0  ...         True   \n",
       "4       Li-Ion        4        2017-02-01      145.0  ...         True   \n",
       "\n",
       "   video_4K  video_8K  video_30fps  video_60fps  video_120fps  video_240fps  \\\n",
       "0     False     False         True        False         False         False   \n",
       "1     False     False         True        False         False         False   \n",
       "2     False     False         True        False         False         False   \n",
       "3     False     False         True        False          True         False   \n",
       "4      True     False         True         True         False         False   \n",
       "\n",
       "   video_480fps  video_960fps  price(USD)  \n",
       "0         False         False       120.0  \n",
       "1         False         False       100.0  \n",
       "2         False         False       420.0  \n",
       "3         False         False       150.0  \n",
       "4         False         False       420.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load data\n",
    "data = pd.read_csv(r\"C:\\Users\\abrao\\Documents\\Archive\\Cloud Track Exam Files (1)\\Cloud Track Exam Files\\Datasets\\AI\\ict_ai_question\\cleaned_all_phones.csv\")\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "317f2f8b-93c9-478f-b303-48d63a6ce450",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot\n",
    "brand_count = pd.DataFrame(data['brand'].value_counts(sort=True))\n",
    "brand_price = data.groupby('brand')['price(USD)'].mean()\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.bar(brand_count.index.values, brand_count['count'].values)\n",
    "plt.xlabel('Brand')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Brand Distribution')\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.bar(brand_price.index.values, brand_price.values)\n",
    "plt.xlabel('Brand')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Brand X price Distribution')\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "ecf06452-d0cf-45f9-a324-f279793bc186",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B3s9kZGQoISFBhYWFcjgcbuccDoeKioqUmJiojIwMH9UQAAAAAOBJBHs/ExQUpOLiYpWVlclisbitim+xWFRWVqY5c+awcB4AAAAA+IlgX1cAnpeTk6OSkhLl5+crPT3ddTwxMVElJSXKycnxYe0AAAAAAJ7EdnftZJbt7g5lt9tls9lUV1enuLg4ZWRk0FMPAAAAACbR3hxKj70fCwoKUmZmpq+rAQAAAADwIubYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACbm02BfVFSks846S5GRkRo4cKAsFou++eYbtzKGYei+++5TfHy8wsLClJmZqa+//tqtzP79+3XLLbeof//+ioiI0KWXXqrvvvvOrczOnTs1efJkRUdHKzo6WpMnT9auXbu8/RQBAAAAAPAqnwb7Dz74QDfddJM++eQTLV26VC0tLbrgggu0d+9eV5nZs2froYce0ty5c/X5558rNjZW559/vhobG11lbrvtNr3++ut6+eWX9dFHH2nPnj3Kzs6W3W53lZk0aZJWrVqlxYsXa/HixVq1apUmT57cpc8XAAAAAABPCzAMw/B1JZy2b9+ugQMH6oMPPtDZZ58twzAUHx+v2267TXfeeaekg73zMTEx+tvf/qbrr79eDQ0NGjBggObPn68rrrhCkrRlyxYNHjxYb731li688EKtWbNGJ598sj755BONGjVKkvTJJ59o9OjRWrt2rU466aRfrNvu3bsVHR2thoYGRUVFea8RAAAAAABQ+3Not5pj39DQIEnq27evJKmmpkZbt27VBRdc4CoTGhqqc845R8uXL5ckVVZW6sCBA25l4uPjlZKS4ipTUVGh6OhoV6iXpLS0NEVHR7vKHG7//v3avXu32xcAAAAAAN1Ntwn2hmHojjvu0K9+9SulpKRIkrZu3SpJiomJcSsbExPjOrd161aFhISoT58+P1tm4MCBrR5z4MCBrjKHKyoqcs3Hj46O1uDBg4/uCQIAAAAA4AXdJtjffPPN+uqrr7Rw4cJW5wICAty+Nwyj1bHDHV6mrfI/d52CggI1NDS4vjZt2tSepwEAAAAAQJfqFsH+lltu0Ztvvqlly5Zp0KBBruOxsbGS1KpXvb6+3tWLHxsbq+bmZu3cufNny2zbtq3V427fvr3VaACn0NBQRUVFuX0BAAAAANDd+DTYG4ahm2++WVarVe+//74SExPdzicmJio2NlZLly51HWtubtYHH3yg9PR0SdLIkSPVq1cvtzJ1dXWqqqpylRk9erQaGhr02Wefucp8+umnamhocJUBAAAAAMCMgn354DfddJNeeuklvfHGG4qMjHT1zEdHRyssLEwBAQG67bbbVFhYqGHDhmnYsGEqLCxUeHi4Jk2a5Cp79dVXKz8/X/369VPfvn01bdo0paam6rzzzpMkJScna9y4cbr22mv11FNPSZKuu+46ZWdnt2tFfAAAAAAAuiufBvu///3vkqTMzEy34/PmzdPUqVMlSX/84x/V1NSkG2+8UTt37tSoUaO0ZMkSRUZGuso//PDDCg4O1uWXX66mpiaNHTtWzz33nIKCglxlXnzxRd16662u1fMvvfRSzZ0717tPEAAAAAAAL+tW+9h3Z+xjDwAAAADoSqbcxx4AAAAAAHQMwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwsWBfVwAAjobdbpfNZlNdXZ3i4uKUkZGhoKAgX1cLAAAA6DL02AMwLavVqqSkJGVlZWnSpEnKyspSUlKSrFarr6sGAAAAdBmCPQBTslqtys3NVWpqqioqKtTY2KiKigqlpqYqNzeXcA8AAIAeI8AwDMPXlTCD3bt3Kzo6Wg0NDYqKivJ1dYAezW63KykpSampqSotLVVg4E+fUTocDlksFlVVVam6upph+QAAADCt9uZQeuwBmI7NZlNtba1mzJjhFuolKTAwUAUFBaqpqZHNZvNRDQEAAICuQ7AHYDp1dXWSpJSUlDbPO487ywEAAAD+jGAPwHTi4uIkSVVVVW2edx53lgMAAAD8GcEegOlkZGQoISFBhYWFcjgcbuccDoeKioqUmJiojIwMH9UQAAAA6DoEewCmExQUpOLiYpWVlclisbitim+xWFRWVqY5c+awcB4AAAB6hGBfVwAAOiMnJ0clJSXKz89Xenq663hiYqJKSkqUk5Pjw9oBAAAAXYft7tqJ7e6A7slut8tms6murk5xcXHKyMigpx4AAAB+ob05lB57AKYWFBSkzMxMX1cDAAAA8Bnm2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABPzabD/8MMPdckllyg+Pl4BAQEqLS11Oz916lQFBAS4faWlpbmV2b9/v2655Rb1799fERERuvTSS/Xdd9+5ldm5c6cmT56s6OhoRUdHa/Lkydq1a5eXnx0AAAAAwJfsdrvKy8u1cOFClZeXy263+7pKXuHTYL93716ddtppmjt37hHLjBs3TnV1da6vt956y+38bbfdptdff10vv/yyPvroI+3Zs0fZ2dluv7BJkyZp1apVWrx4sRYvXqxVq1Zp8uTJXnteAAAAAADfslqtSkpKUlZWliZNmqSsrCwlJSXJarX6umoeF+zLBx8/frzGjx//s2VCQ0MVGxvb5rmGhgY9++yzmj9/vs477zxJ0oIFCzR48GC9++67uvDCC7VmzRotXrxYn3zyiUaNGiVJeuaZZzR69Gh98803Oumkkzz7pAAAAAAAPmW1WpWbm6vs7GwtXLhQKSkpqqqqUmFhoXJzc1VSUqKcnBxfV9Njuv0c+/Lycg0cOFAnnniirr32WtXX17vOVVZW6sCBA7rgggtcx+Lj45WSkqLly5dLkioqKhQdHe0K9ZKUlpam6OhoVxkAAAAAgH+w2+3Kz89Xdna2SktLlZaWpt69eystLU2lpaXKzs7WtGnT/GpYfrcO9uPHj9eLL76o999/X8XFxfr888917rnnav/+/ZKkrVu3KiQkRH369HH7uZiYGG3dutVVZuDAga2uPXDgQFeZtuzfv1+7d+92+wIAAAAAdG82m021tbWaMWOGAgPdI29gYKAKCgpUU1Mjm83moxp6nk+H4v+SK664wvX/KSkpOvPMMzVkyBAtWrToZ4dNGIahgIAA1/eH/v+RyhyuqKhIs2bN6mTNAQAAAAC+UFdXJ+lghmyL87iznD/o1j32h4uLi9OQIUNUXV0tSYqNjVVzc7N27tzpVq6+vl4xMTGuMtu2bWt1re3bt7vKtKWgoEANDQ2ur02bNnnwmQAAAAAAvCEuLk6SVFVV1eZ553FnOX9gqmC/Y8cObdq0yfULGDlypHr16qWlS5e6ytTV1amqqkrp6emSpNGjR6uhoUGfffaZq8ynn36qhoYGV5m2hIaGKioqyu0LAAAAANC9ZWRkKCEhQYWFhXI4HG7nHA6HioqKlJiYqIyMDB/V0PN8Guz37NmjVatWadWqVZKkmpoarVq1Shs3btSePXs0bdo0VVRUqLa2VuXl5brkkkvUv39//frXv5YkRUdH6+qrr1Z+fr7ee+89rVy5Unl5eUpNTXWtkp+cnKxx48bp2muv1SeffKJPPvlE1157rbKzs1kRHwAAAAD8TFBQkIqLi1VWViaLxaKKigo1NjaqoqJCFotFZWVlmjNnjoKCgnxdVY8JMAzD8NWDl5eXKysrq9XxKVOm6O9//7ssFotWrlypXbt2KS4uTllZWfrLX/6iwYMHu8r++OOPmj59ul566SU1NTVp7NixevLJJ93K/PDDD7r11lv15ptvSpIuvfRSzZ07V8cee2y767p7925FR0eroaGB3nsAAAAA6OasVqvy8/NVW1vrOpaYmKg5c+aYZqu79uZQnwZ7MyHYAwAAAIC52O122Ww21dXVKS4uThkZGabqqW9vDu3Wq+Lj6Jj9JgYAAACAoxEUFKTMzExfV8PrTLV4HtrParUqKSlJWVlZmjRpkrKyspSUlCSr1errqgEAAAAAPIgeez9ktVqVm5uriy++WNOnT1dYWJiampr09ttvKzc3VyUlJaaZUwIAAAAA+HnMsW8ns8yxt9vtSkpKUv/+/bV9+3Z9++23rnNDhgzRgAEDtGPHDlVXVzMsHwAAAAC6sfbmUIbi+xmbzaba2lp98cUXOvXUU922djj11FP1xRdfqKamRjabzddVBQAAAAB4AMHez2zevFmSNH78eJWWliotLU29e/dWWlqaSktLNX78eLdyAAAAAABzI9j7me3bt0uScnJyFBjo/usNDAyUxWJxKwcAAAAAMDeCvZ8ZMGCApIML6DkcDrdzDodDpaWlbuUAAAAAAOZGsPczxx13nCRp8eLFslgsbnPsLRaLFi9e7FYOAAAAAGBurIrfTmZcFf/7779XbW2t61xiYqL69evHqvgAAAAAYALtzaHsY+9ngoKCVFxc7NrHftq0aa597BcvXqxFixappKSEUA8AAAAAfoJg74dycnJUUlKiO+64Q2VlZa7jCQkJKikpUU5Ojg9rB3iW3W6XzWZTXV2d4uLilJGRwQdXAAAA6FGYY+/HAgICfF0FwKusVquSkpKUlZWlSZMmKSsrS0lJSbJarb6uGgAAANBlCPZ+yGq1Kjc3V6mpqW6L56Wmpio3N5fQA7/AfQ4AAAAcxOJ57WS2xfNSU1NVWlrqtpe9w+GQxWJRVVUVi+fB1LjPAQAA0BO0N4fSY+9nbDabamtrNWPGDLewI0mBgYEqKChQTU2NbDabj2oIHD3ucwAAAOAnBHs/U1dXJ0lKSUlp87zzuLMcYEbc5wAAAMBPCPZ+Ji4uTpJUVVXV5nnncWc5wIy4zwEAAICfMMe+nZhjD3Qf3OcAAADoCZhj30MFBQWpuLhYZWVlslgsbquFWywWlZWVac6cOYQdmBr3OQAAAPATeuzbySw99k5Wq1X5+fmqra11HUtMTNScOXOUk5Pju4oBHsR9DgAAAH/W3hxKsG8nswV76eBwZZvNprq6OsXFxSkjI4MeTPgd7nMAAAD4K4biAwAAAADQAxDs/ZTValVSUpKysrI0adIkZWVlKSkpSVar1ddVAzyG+xwAAAAg2Pslq9Wq3Nxcpaamui0qlpqaqtzcXEIP/AL3OQAAAHAQc+zbySxz7NkGDD0B9zkAAAB6AubY91A2m021tbWaMWOGW9iRpMDAQBUUFKimpkY2m81HNQSOHvc5AAAA8BOCvZ+pq6uTJKWkpLR53nncWQ4wI+5zAAAA4CcEez8TFxcnSaqqqmrzvPO4sxxgRtznAAAAwE+YY99OzLEHug/ucwAAAPQEzLHvoYKCglRcXKyysjJZLBa31cItFovKyso0Z84cwg5MjfscAAAA+Ak99u1klh57J6vVqvz8fNXW1rqOJSYmas6cOcrJyfFdxQAP4j4HAACAP2tvDiXYt5PZgr10cLiyzWZTXV2d4uLilJGRQQ8m/A73OQAAAPwVwd7DzBjsAQAAAADmxRx7AAAAAAB6AII9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiR1VsF+/fr3eeecdNTU1SZIMw/BIpQAAAAAAQPt0Ktjv2LFD5513nk488URddNFFqqurkyRdc801ys/P92gFAQAAAADAkXUq2N9+++0KDg7Wxo0bFR4e7jp+xRVXaPHixR6rHAAAAAAA+HnBnfmhJUuW6J133tGgQYPcjg8bNkzffvutRyoGAAAAAAB+Wad67Pfu3evWU+/0/fffKzQ09KgrBQAAAAAA2qdTwf7ss8/WCy+84Po+ICBADodDDz74oLKysjxWOQAAAAAA8PM6NRT/wQcfVGZmpr744gs1Nzfrj3/8o77++mv98MMP+vjjjz1dRwAAAAAAOsxut8tms6murk5xcXHKyMhQUFCQr6vlcZ3qsT/55JP11Vdf6f/9v/+n888/X3v37lVOTo5WrlypoUOHerqOAAAAAAB0iNVqVVJSkrKysjRp0iRlZWUpKSlJVqvV11XzuACDzefbZffu3YqOjlZDQ4OioqJ8XR0AAAAAwBFYrVbl5uYqOztbM2bMUEpKiqqqqlRYWKiysjKVlJQoJyfH19X8Re3NoZ0K9vPmzVPv3r31m9/8xu34q6++qn379mnKlCkdr3E3R7AHAAAAgO7PbrcrKSlJqampKi0tVWDgTwPVHQ6HLBaLqqqqVF1d3e2H5bc3h3ZqKP4DDzyg/v37tzo+cOBAFRYWduaSAAAAAAAcNZvNptraWs2YMcMt1EtSYGCgCgoKVFNTI5vN5qMael6nFs/79ttvlZiY2Or4kCFDtHHjxqOuFDyjpywUAQAAAABOdXV1kqSUlJQ2zzuPO8v5g0712A8cOFBfffVVq+Nffvml+vXrd9SVwtHrSQtFAAAAAIBTXFycJKmqqqrN887jznL+oFPB/sorr9Stt96qZcuWyW63y2636/3339cf/vAHXXnllZ6uIzrIuVBEamqqKioq1NjYqIqKCqWmpio3N5dwDwAAAMBvZWRkKCEhQYWFhXI4HG7nHA6HioqKlJiYqIyMDB/V0PM6tXhec3OzJk+erFdffVXBwQdH8zscDv32t7/VP/7xD4WEhHi8or5mlsXz/GmhCAAAAADojENXxS8oKHCtil9UVMSq+Idbt26dvvzyS4WFhSk1NVVDhgzp7KW6PbME+/LycmVlZamiokJpaWmtzldUVCg9PV3Lli1TZmZm11cQAAAAALqA1WpVfn6+amtrXccSExM1Z84cU4R6qf05tFOL5zmdeOKJOvHEE4/mEvCwnrhQBAAAAAAcLicnRxMmTOgRC4q3O9jfcccd+stf/qKIiAjdcccdP1v2oYceOuqKoXMOXSiirR57f1woAgAAAADaEhQU1CNGKrc72K9cuVIHDhyQJK1YsUIBAQFtljvScXSNQxeKaGuOvT8uFAEAAAAAPVm7g/2yZctc/19eXu6NusADgoKCVFxcrNzcXFksliMuFOGPw08AAAAAoCfq8HZ3LS0tCg4OPuKegPC9nJwclZSUaPXq1UpPT1dUVJTS09NVVVVlmtUfAQAAAADt0+HF84KDgzVkyBDZ7XZv1Ace0pMWigAAAACAnqxT293NmzdPr776qhYsWKC+fft6o17djlm2uwMAAAAA+Aevbnf32GOPaf369YqPj9eQIUMUERHhdn7FihWduSwAAAAAAOigTgV7i8WigIAAdaKzHwAAAAAAeFCHgv2+ffs0ffp0lZaW6sCBAxo7dqwef/xx9e/f31v1AwAAAAAAP6NDwX7mzJl67rnndNVVVyksLEwvvfSS/u///k+vvvqqt+oHmIrdbmfBQgAAAABdqkPB3mq16tlnn9WVV14pSbrqqqs0ZswY2e12wgt6PKvVqvz8fNXW1rqOJSQkqLi4mC0GAQAAAHhNh/ax37RpkzIyMlzf/7//9/8UHBysLVu2eLxigJlYrVbl5uYqNTVVFRUVamxsVEVFhVJTU5Wbmyur1errKgIAAADwUx3a7i4oKEhbt27VgAEDXMciIyP11VdfKTEx0SsV7C7Y7g5HYrfblZSUpNTUVJWWliow8KfPyxwOhywWi6qqqlRdXc3IFgAAAADt5pXt7gzD0NSpUxUaGuo69uOPP+qGG25w2/KO3kn0JDabTbW1tVq4cKFbqJekwMBAFRQUKD09XTabTZmZmb6pJAAAAAC/1aFgP2XKlFbH8vLyPFYZwIzq6uokSSkpKW2edx53lgMAAAAAT+pQsJ83b5636gGYVlxcnCSpqqpKaWlprc5XVVW5lQMAAAAAT+rQHPuejDn2OJJD59i/9tpr+vjjj13b3Y0ZM0aXXXYZc+wBAAAAdJhX5tgDaC0oKEjFxcW67LLLFB0draamJte5sLAwNTU16bXXXiPUAwAAAPCKDm13B+DIAgIC2jzW1nEAAAAA8BSG4rcTQ/FxJAzFBwAAAOANDMUHusih29316tWr1ZZ2bHcHAAAAwJsYig8cJba7AwAAAOBLBHs/ZrfbVV5eroULF6q8vFx2u93XVfJLh2531xa2uwMAAADgTcyxbyezzbG3Wq3Kz89XbW2t61hCQoKKi4uVk5Pju4r5oUPn2JeWliow8KfPyxwOhywWC3Psvchut8tms7nWNcjIyKCdAQAA4Bfam0PpsfdDVqtVubm5Sk1NVUVFhRobG1VRUaHU1FTl5ubKarX6uop+xbndXVlZmSwWi1ubWywWlZWVac6cOYRNL7BarUpKSlJWVpYmTZqkrKwsJSUlcY8DAACgRyHY+xm73a78/HxlZ2frtdde048//qj//Oc/+vHHH/Xaa68pOztb06ZNY1i+h+Xk5KikpESrV69Wenq6oqKilJ6erqqqKpWUlDBKwgv4AAsAAAA4iKH47WSWofjl5eXKyspSUVGRnnrqqVZD8a+77jrNmDFDy5YtY4V2L2BYeNdg+gMAAAB6Ara766GcK6/PmDFDF198saZPn66wsDA1NTXp7bff1t133+1WDjCjQ7cYNAxD5eXlbh+msMUgAAAAehKCvZ8ZOHCgJGn48OFavXq1ysrKXOeGDBmik046SWvXrnWVg+ewYGHXcX4wtWHDBk2cOLFVm99///1u5QAAAAB/xhx7P7VmzZo25x6vXbvW11XzS8z37lrOrQPz8vLabPO8vDy3cgAAAIA/o8fez2zdutXte8MwXF8/Vw6dd+iChYfO905LS1NpaaksFoumTZumCRMmMN/bQ9LT0xUcHKx+/frJarUqOPjgS1laWpqsVqsGDRqkHTt2KD093cc1BQAAALyPHns/s337dknS//3f/7W5QvsNN9zgVg5Hzznfe8aMGWppadEjjzyiW265RY888ohaWlpUUFCgmpoa2Ww2X1fVbyxfvlwtLS2qr69XTk6OW499Tk6O6uvr1dLSouXLl/u6qgAAAIDX+TTYf/jhh7rkkksUHx+vgIAAlZaWup03DEP33Xef4uPjFRYWpszMTH399dduZfbv369bbrlF/fv3V0REhC699FJ99913bmV27typyZMnKzo6WtHR0Zo8ebJ27drl5WfnGwMGDJAkffLJJ63OGYahTz/91K0cjp5zHvfLL7+siIgI3X777Zo7d65uv/12RURE6JVXXnErh6PnbMv58+e3+QHW/Pnz3coBAAAA/synwX7v3r067bTTNHfu3DbPz549Ww899JDmzp2rzz//XLGxsTr//PPV2NjoKnPbbbfp9ddf18svv6yPPvpIe/bsUXZ2tts+7ZMmTdKqVau0ePFiLV68WKtWrdLkyZO9/vx84bjjjpMkrVy5Uj/++KOefvppbdmyRU8//bR+/PFHrVy50q0cjp5zHvejjz6qfv366ZlnnlFdXZ2eeeYZ9evXT48++qhbORw9Z1sOHTpU69ev17Jly/TSSy9p2bJlqq6u1gknnOBWDgAAAPBn3WYf+4CAAL3++uuyWCySDvYux8fH67bbbtOdd94p6WDvfExMjP72t7/p+uuvV0NDgwYMGKD58+friiuukCRt2bJFgwcP1ltvvaULL7xQa9as0cknn6xPPvlEo0aNknSwN3v06NFau3atTjrppHbVzyz72Dc3NysiIkIRERHq06eP22rhiYmJ+uGHH7R3717t3btXISEhvquoH2lqalJ4eLhCQkLU2Njo1q7Nzc2KjIxUc3Oz9u3bp7CwMB/W1H+wjz0AAAB6gvbm0G47x76mpkZbt27VBRdc4DoWGhqqc845xzVvtrKyUgcOHHArEx8fr5SUFFeZiooKRUdHu0K9dHCBrejo6J+df7t//37t3r3b7csMnHOPGxoalJKSorlz5+rZZ5/V3Llzdcopp6ihoYG5xx721FNPSZIOHDig3Nxct/neubm5OnDggFs5HL2goCAVFxerrKxMFovFrc0tFovKyso0Z84cQj0AAAB6hG4b7J2rtsfExLgdj4mJcZ3bunWrQkJC1KdPn58t09ae7QMHDvzZleGLiopcc/Kjo6M1ePDgo3o+XcU5p3jBggWqqqrSzTffrKuvvlo333yzvv76ay1YsMCtHI7ehg0bJEnPPPNMm/O9n376abdy8IycnByVlJS02eYlJSXKycnxdRUBAACALtHtt7sLCAhw+94wjFbHDnd4mbbK/9J1CgoKdMcdd7i+3717tynC/eFzj202m+rq6hQXF6eMjAx99tlnbuVw9IYOHSrp4D3VVps/++yzbuXgOTk5OZowYUKrNqenHgAAAD1Jtw32sbGxkg72uB8aQuvr6129+LGxsWpubtbOnTvdeu3r6+td+1fHxsZq27Ztra6/ffv2VqMBDhUaGqrQ0FCPPJeulJGRoYSEBBUWFqq0tFSZmZmucw6HQ0VFRUpMTFRGRobvKulnbrzxRk2fPl333HOPpk6d6tbmLS0tuvfeexUcHKwbb7zRd5X0Y0FBQW5tDgAAAPQ03XYofmJiomJjY7V06VLXsebmZn3wwQeu0D5y5Ej16tXLrUxdXZ2qqqpcZUaPHq2GhgZXT7Ukffrpp2poaHCV8SfMPe56ISEhuv3227Vt2zYNGjTIbSeCQYMGadu2bbr99ttZrBAAAACAV/i0x37Pnj1av3696/uamhqtWrVKffv21fHHH6/bbrtNhYWFGjZsmIYNG6bCwkKFh4dr0qRJkqTo6GhdffXVys/PV79+/dS3b19NmzZNqampOu+88yRJycnJGjdunK699lrX4mXXXXedsrOz270ivtk45x7n5+e7fXiRmJjI3GMvmT17tiTp4Ycf1vXXX+86HhwcrOnTp7vOAwAAAICn+TTYf/HFF8rKynJ975zTPmXKFD333HP64x//qKamJt14443auXOnRo0apSVLligyMtL1Mw8//LCCg4N1+eWXq6mpSWPHjtVzzz3n1iP94osv6tZbb3Wtnn/ppZdq7ty5XfQsfefwnQwdDoePatIzzJ49W/fff7+efPJJbdiwQUOHDtWNN95ITz0AAAAAr+o2+9h3d2bZx16SrFarcnNzlZ2drRkzZiglJUVVVVUqLCxUWVkZvfYAAAAAYALtzaEE+3YyS7C32+1KSkpSamqqSktLFRj40zIKDodDFotFVVVVqq6uZp49AAAAAHRj7c2h3XbxPHSOzWZTbW2tZsyY4RbqJSkwMFAFBQWqqamRzWbzUQ0BAAAAAJ5EsPczdXV1kqSUlJQ2zzuPO8sBAAAAAMyt2+5jj86Ji4uTJFVVVemss86SzWZTXV2d4uLilJGRoaqqKrdygNnZ7fZW9znTTAAAANCTMMe+ncw2x75///7avn27vv32W9e5IUOGaMCAAdqxYwdz7OEXrFar8vPzVVtb6zqWkJCg4uJiFogEAACA6THHvocKCgrSb37zG33xxRfatGmT27lNmzbpiy++UG5uLqEepufc/SE1NVUVFRVqbGxURUWFUlNTlZubK6vV6usqAgAAAF2CHvt2MlOPfVxcnLZv366wsDA1NTW5zjm/HzhwoLZs2UK4h2mx+wMAAAB6Anrse6jy8nJt375dv/rVr9TQ0KBly5bppZde0rJly9TQ0KBf/epXqq+vV3l5ua+rCnQauz8AAAAAP2HxPD/jDOyzZs1Sr169lJmZ6XZ+5syZOv/881VeXq6xY8d2fQUBD2D3BwAAAOAn9NgDMJ1Dd39oC7s/AAAAoCch2PsZZw/9zJkzdeDAAZWXl2vhwoUqLy/XgQMHdN9997mVA8woIyNDCQkJKiwslMPhcDvncDhUVFSkxMREZWRk+KiGAAAAQNdhKL6fyczM1MCBA/XRRx8pOjr6iIvnEexhZkFBQSouLlZubq4sFosKCgqUkpKiqqoqFRUVqaysTCUlJSycBwAAgB6BHns/ExQUpClTpkiS9u/f73auublZkjRlyhQCD0wvJydHJSUlWr16tdLT0xUVFaX09HRVVVWppKSEfewBAADQY7DdXTuZabu7pKQk9e/fX9u3b9e3337rOpeQkKD+/ftrx44dbAMGv2G322Wz2VRXV6e4uDhlZGRwbwMAAMAvtDeHMhTfzzi3AVu4cKHOOuusVoHns88+U3p6umw2G8PxAQAAAJjSvn37tHbt2naVbWpqUm1trRISEhQWFtbuxxg+fLjCw8M7W8UuRbD3M2wDhp7EarUqPz9ftbW1rmMJCQkqLi5mKD4AAIAfW7t2rUaOHOnVx6isrNSIESO8+hieQrD3M87tvebOnaunnnqqVeC57rrr3MoBZmW1WpWbm6vs7GwtXLjQtXheYWGhcnNzmWcPAADgx4YPH67Kysp2lV2zZo3y8vK0YMECJScnd+gxzII59u1kpjn28fHxqq+v18UXX6yLLrrItRr+W2+9pUWLFmngwIHasmUL85BhWs61JFJTU/Xaa6/p448/dk05GTNmjC677DJVVVWxloSXsK4BAAAwkxUrVmjkyJGm6oF3Yo59D+b8rOb999/XokWLXMc7Mp8E6M6ca0lcf/31OvHEE9scmfKf//yHtSS8gOkPAAAA3Q/b3fkZm82m7du3t3kuICBAklRfXy+bzdaV1QI8yrlGxIwZM5SamqqKigo1NjaqoqJCqampuvvuu93KwTOc0x/aavPc3FxZrVZfVxEAAKBHItj7mc2bN0uSxo8fr4aGBi1btkwvvfSSli1bpl27dmn8+PFu5QAzGjhwoCRpzJgxKi0tVVpamnr37q20tDSVlpZqzJgxbuVw9Ox2u/Lz85Wdnd1mm2dnZ2vatGmy2+2+rioAAECPQ7D3M87e+pycHAUGuv96AwMDZbFY3MoB/oilQzzPOf1hxowZbb62FBQUqKamhtFAAAAAPsAcez8zYMAASdKTTz6pv/71r63mwfbp08etHGBG9fX1kqSPPvpIFotFBQUFrlXxi4qK9PHHH7uVw9FjK00AAIDuix57P3PcccdJklauXKmmpiY9/fTT2rJli55++mk1NTVp5cqVbuUAM3Ju11hUVKTVq1crPT1dUVFRSk9Pd215d2g5HD1nW1ZVVbV53nmcNgfQWXa7XeXl5Vq4cKHKy8uZ2gMAHcB2d+1klu3umpubFRERoYiICEVHR2vjxo2uc0OGDNGuXbu0d+9e7d27VyEhIT6sKdB5bHfX9Q5t89LSUrfh+A6HQxaLhTYH0GnsuAHAm3rCdnf02PuZ5cuXq6WlRbt379app56quXPn6tlnn9XcuXOVmpqq3bt3q6WlRcuXL/d1VYFOCwoKUnFxscrKynTZZZcpNDRU2dnZCg0N1WWXXaaysjLNmTOHgOlBh7a5xWJxWxXfYrHQ5gA6jR03AODoMcfezzjnt86fP1/33HOPysrKXOcSExM1f/585eXlMQ8WppeTk6OSkhLl5+crPT3ddTwxMVElJSX08HgBbQ7A0w7fccM5Gsi544bFYtG0adM0YcIEPjgEgJ9BsPczzvmtQ4cO1fr162Wz2VxDlDMyMvTZZ5+5lQPMLCcnRxMmTGh1n/Pmz3tocwCe5NxxY+HChUfccSM9PV02m02ZmZm+qSQAmADB3s9kZGQoISFBhYWFeu2119zOORwOFRUVKTExURkZGT6qIeBZQUFBvNnrYrQ5AE9hxw0A8AyCvZ9xzoPNzc1VdHS0mpqaXOfCwsL0448/qqSkhN41AADgc4fuuJGWltbqPDtuAED7sHien2prs4OAgIA2jwMAAPjCoSMNHQ6H2zlGGgJA+xHs/YxzEZpLLrlEDQ0NWrZsmV566SUtW7ZMu3bt0iWXXKJp06axNywAAPA5dtwAAM9gKL6fOXQRmpaWFpWUlKi6ulrDhg3TqFGjWIQGAEzIbrezYCH8FjtuAMDRI9j7GefiMvfff78WLVrkOr5kyRI98cQTuvjii93KAQC6N6vVqvz8fNXW1rqOJSQkqLi4mMADv8GOGwBwdBiK72eci8ssWrRIISEhmjRpkh566CFNmjRJISEhrrDPIjQA0P1ZrVbl5uYqNTXVbYhyamqqcnNzZbVafV1FwGOcO25MnDhRmZmZhHoA6IAAg9XU2mX37t2Kjo5WQ0ODoqKifF2dI9qzZ48iIyMVEBCgwYMHa+PGja5zxx9/vDZt2iTDMNTY2KjevXv7sKYAgJ9jt9uVlJSk1NRUlZaWuu3x7XA4ZLFYVFVVperqagIQAAA/Y8WKFRo5cqQqKys1YsQIX1enQ9qbQ+mx9zN33XWXpIOr4u/fv19PP/20tmzZoqefflr79+93rYrvLAcA6J6ca6bMmDHDLdRLUmBgoAoKClRTUyObzeajGgIAgO6COfZ+Zt26dZKkU045RXv27NF1113nOpeQkKBTTjlFX3/9tascAKB7cq6FkpKS0uZ553HWTAEAAPTY+5mIiAhJ0ogRI7Rhwwa37e7Wr1+v008/3a0cAKB7cq6FUlVV1eZ553HWTAEAAAR7P2OxWCRJr7zyiux2u9siNHa7Xa+++qpbOQBA95SRkaGEhAQVFhbK4XC4nXM4HCoqKlJiYqIyMjJ8VEMAANBdEOz9zJAhQyRJzc3NioyM1J133ql169bpzjvvVGRkpJqbm93KAQC6p6CgIBUXF6usrEwWi8VtVXyLxaKysjLNmTOHhfMAAACr4reXWVbFd66i3NTUpG3btrU6HxMTo/DwcFZRBgCTaGsf+8TERM2ZM4d97AEAaIeesCo+i+f5GWcPz2WXXaawsDA1NTW5zoWFhWnbtm167bXXCPUAYBI5OTmaMGGCbDab6urqFBcXp4yMDF7HAQCAC8HeTwUEBLR5rK3jAIDuLSgoSJmZmb6uBgAA6KaYY+9n7Ha78vPzlZ2drYaGBrdV8Xft2qXs7GxNmzZNdrvd11UFAAAAAHgAPfZ+xmazqba2VgsXLlRgoPvnNoGBgSooKFB6erpsNhu9P+jW9u3bp7Vr17arbFNTk2pra5WQkKCwsLB2P8bw4cMVHh7e2SoCAAAA3QLB3s/U1dVJkjZs2KCJEye6LbaUkJCg+++/360c0F2tXbtWI0eO9OpjmHEBFQAAAOBwBHs/ExcXJ0nKy8vTJZdcooULFyolJUVVVVUqLCxUXl6eWzmguxo+fLgqKyvbVXbNmjXKy8vTggULlJyc3KHHAAAAAMyOYO9n0tPTFRwcrH79+slqtSo4+OCvOC0tTVarVYMGDdKOHTuUnp7u45oCPy88PLzDvenJycn0wAMAAKDHIdj7meXLl6ulpUX19fX69a9/rXHjxrm2vVu8eLHq6+tlGIaWL1/OHHsvsNvtbEkFAAAAoEsR7P2Mc+78rbfeqieeeEJlZWWuc8HBwbr11lv16KOPMsfeC6xWq/Lz81uta1BcXKycnBzfVQwAAACAX2O7Oz/jnDv/2GOPady4cXriiSf0r3/9S0888YTGjRunxx57zK0cPMNqtSo3N1epqamqqKhQY2OjKioqlJqaqtzcXFmtVl9XEQAAAICfCjAMw/B1Jcxg9+7dio6OVkNDg6KionxdnSNqbm5WRESE+vXrp++++841x16SWlpaXHPs9+7dq5CQEB/W1H/Y7XYlJSUpNTVVpaWlbtsMOhwOWSwWVVVVqbq6mmH5XrJixQqNHDmSVe4BAADQipnfK7Y3h9Jj72ecc+y3bdumnJwct97jnJwcbdu2TS0tLVq+fLmvq+o3bDabamtrNWPGDLdQL0mBgYEqKChQTU2NbDabj2oIAAAAwJ8R7P2Mc+78ggUL9NVXXyk9PV1RUVFKT0/X6tWrtWDBArdyOHrOtkxJSZHdbld5ebkWLlyo8vJy2e12paSkuJUDAAAAAE9i8Tw/45w7v2nTJgUEBLQ6v3HjRrdyOHrOtpw7d66eeuqpVovnXXfddW7lAAAAAMCT6LH3MxkZGRo4cKAKCgqUkpLiNhQ/JSVFM2bM0MCBA5WRkeHrqvqNjIwMDRgwgDYHAAAA4BMEez906HqIhmG4vuA9h46OoM0BAAAAdCWCvZ+x2Wzavn27ioqKVFVV5TbH/uuvv1ZhYaHq6+tZyM2DbDab6uvraXMAAAAAPkGw9zPOBdoGDx7cqsfY4XDo+OOPdyuHo+dsy5tvvlnr16/XsmXL9NJLL2nZsmWqrq7WzTff7FYOAAAAADyJYO9nnAu05eXl6dRTT3Wb733qqacqLy/PrRyOnrMtq6qqFBQUpMzMTE2cOFGZmZkKCgpSVVWVWzkAAAAA8CRWxfcz6enpCg4OVr9+/WS1WhUcfPBXnJaWJqvVqkGDBmnHjh1KT0/3cU39R0ZGhhISElRYWKjXXntNH3/8serq6hQXF6cxY8aoqKhIiYmJLJ4HAAAAwCsI9n5m+fLlamlpUX19vXJyclwrtVdVVamoqEj19fUyDEPLly9XZmamr6vrF4KCglRcXKzc3FxFR0erqanJdS4sLEw//vijSkpKFBQU5MNaAgAAAPBXDMX3M8553PPnz9fq1avdFnKrqqrS/Pnz3crBc460Ej6r4wMAAADwJoK9n3HO4960aVObi+dt3LjRrRyOnt1uV35+vs4880zFxsa6nYuJidGZZ56padOmyW63+6iGAAAAAPwZwd7PZGRkaMCAASooKNApp5yiyy67TOeee64uu+wynXLKKZoxY4YGDhzIfG8Pstlsqq2tVWVlpVJTU90WLExNTVVlZaVqamrY7g4AAACAVzDH3g8FBARIkt566y0f16Rn2Lx5syRp3LhxKi0tVWDgwc/L0tLSVFpaquzsbL399tuucgAAAADgSQR7P2Oz2VRfX/+zZerr62Wz2Vg8z0O2b98uScrJyXGFeqfAwEBZLBa9/fbbrnIAAAAAWquurlZjY6PHr7tmzRq3/3pDZGSkhg0b5rXr/xKCvZ/59ttvJUnBwcGKj493zamXpOOPP15btmxRS0uLqxyO3oABAyRJVqtVv//9793CvcPhUGlpqVs5AAAAAO6qq6t14oknevUx8vLyvHr9devW+SzcE+z9jDNEtrS06LTTTtMrr7zi2u6usLDQFfRLS0s1ZcoUH9bUfxx33HGSpLffflsTJkzQuHHjFBYWpqamJi1evFhvv/22WzkAAAAA7pw99QsWLFBycrJHr93U1KTa2lolJCQoLCzMo9eWDo4EyMvL88pog/Yi2PsZ583Ur18/Wa1WBQcf/BWnpaXJarUqJiZGP/zwg09vOn+TkZGhhIQEBQUF6e2331ZZWZnrXFBQkIYOHSqHw8GChQAAAMAvSE5O1ogRIzx+3TFjxnj8mt0Jwd7PREZGSpJ27NihX//61616j3/44Qe3cjh6QUFB+s1vfqMHH3xQMTExysvL0wknnKD//e9/WrBggTZs2KDp06crKCjI11UFAAAA4IcI9n7GYrG4VmY/vPc4ODhYgYGBcjgcslgsvqukn7Hb7Xr11Vd15pln6vvvv1dxcbHrXGJios4880yVlJSoqKiIcA8AAADA4wj2fmbIkCGSDi7aFhAQoPPOO0/nnnuu3n//fb333nsyDMOtHI6ecx/7hQsX6qyzzpLNZlNdXZ3i4uKUkZGhzz77TOnp6exEAAAA0MPZ7fZW7xXp+IEnEOz9THp6uqtnvrm5We+++67effdd1/mQkBA5HA6lp6f7sJb+pa6uTpKUkpKioKCgVuE9JSXFrRwAAAB6HqvVqvz8fNXW1rqOJSQkqLi4WDk5Ob6rGPxC4C8XgZksX75cLS0tam5u1kUXXaScnByde+65ysnJ0UUXXaTm5ma1tLRo+fLlvq6q34iLi5MkVVVVtXneedxZDgAAAD2L1WpVbm6uUlNTVVFRocbGRlVUVCg1NVW5ubmyWq2+riJMjh57P+PsFV6wYIHuuecet08EExMTtWDBAuXl5dF77EHOVfELCwtd6xs4ORwOFRUVKTExkVXx4TcYRgjAG3htgb+y2+3Kz89Xdna223vFtLQ0lZaWymKxaNq0aZowYQL3PDqNHns/4+wVHjp0qNavX69ly5bppZde0rJly1RdXa0TTjjBrRyOXlBQkIqLi1VWViaLxeL2KazFYlFZWZnmzJnDCzX8gtVqVVJSkrKysjRp0iRlZWUpKSmJngYAR4XXFvgz53pMM2bMcOsAkqTAwEAVFBSopqZGNpvNRzWEPyDY+5lDe48DAgKUmZmpiRMnKjMzUwEBAfQee0lOTo5KSkq0evVqpaenKyoqSunp6aqqqlJJSQnzpuAXGEboO3a7XeXl5Vq4cKHKy8tlt9t9XSXAY3htgb87dD2mtrAeEzyBYO9n6D32nZycnDZHSRDq4Q8OH0aYlpam3r17u4YRZmdna9q0aQROL6AnE/6M1xb0BKzHhK7AHHsT2rdvn9auXXvE8wkJCZo9e7Yefvhht9XvjzvuOM2ePVsJCQlasWLFzz7G8OHDFR4e7rE6AzC3Q7d1PNIwQrZ19DxnT2Z2drYWLlyolJQUVVVVqbCwULm5uYwIgunx2uIdv/Re8VBNTU2qra1VQkKCwsLC2v0YvFdsP9ZjQlcg2JvQ2rVrNXLkyA7/3ObNmzV9+vR2la2srNSIESM6/Bg9GVuYwJ8xjLDrsdgSegJeW7yjs+8VO4L3iu3nHFGbm5sri8WigoIC1we1RUVFKisrU0lJCa/lOCoEexMaPny4Kisr21V2zZo1ysvL04IFC5ScnNyhx0D70asGf3foMMK0tLRW5xlG6Hn0ZKIn4LXFO3iv2P0412PKz893G1GbmJjI+0R4BMHehMLDwzv8CWlycjKfqnoJvWroCRhG2PXoyURPwGuLd/BesXvKycnRhAkT2NYRXkGwB44SvWroCQ4dRjhhwgSNGzdOYWFhampq0uLFi7Vo0SKGEXoYPZnoCRiijJ4mKCiI94PwCoI9cJToVUNPkZOTo2nTpunhhx9WWVmZ63hwcLCmTZvGMEIPoycTPQVDlAHg6BHsgaNErxp6CqvVqjlz5ujiiy/W+PHjXT32b7/9tubMmaO0tDTegHsQPZnoSRiiDABHh2APHCV61dATHGktCUm64YYbWEvCS+jJRE/CEGX0BHa7nQ+w4BWBv1wEwM9x9qqVlZXJYrGooqJCjY2NqqiokMViUVlZmebMmcOLNkzNuZbEjBkzjriWRE1NjWw2m49q6L9ycnK0fv16LVu2TC+99JKWLVum6upqQj0AmIzValVSUpKysrI0adIkZWVlKSkpSVar1ddVgx8g2AMe4OxVW716tdLT0xUVFaX09HRVVVXRqwa/wFoSvuXsyZw4caIyMzP5oBAATMa5NXJqaqpbJ1Bqaqpyc3MJ9zhqDMUHPIT5gfBnrCUBAEDnsDUyugLBHvAg5gfCX7GWBAAAncPWyOgKDMUHAPwi1pIAAKBzmM6GrkCPPeBBrHQKf8YK7QAAdBzT2dAVCPaAh1itVuXn56u2ttZ1LCEhQcXFxQQe+A3WkgAAoGOYzoauQLAHPMC50ml2drYWLlyolJQUVVVVqbCwULm5ufRmAgAA9FDO6Wy5ubmyWCwqKChwvVcsKipSWVmZSkpK+JBcUmzvAIXtWidtMdeM8bBd6xTbO8CndSDYA0eJlU7RkzAyBQCAjmM6W/tcPzJEyR9eL33o65p0TLIO1t2XCPbAUWKlU/QUjEwB0Bn79u3T2rVr21W2qalJtbW1SkhIUFhYWLsfY/jw4QoPD+9sFYEuwXS2X/ZUZbOuuPc5JQ8f7uuqdMiatWv1VPEkXerDOnTrYH/fffdp1qxZbsdiYmK0detWSZJhGJo1a5aefvpp7dy5U6NGjdITTzyhU045xVV+//79mjZtmhYuXKimpiaNHTtWTz75pAYNGtSlzwX+69CVThsaGnTxxRdr48aNOv7447Vo0SJWOoVfYGQKehoWQ/WctWvXauTIkV59jMrKSo0YMcKrjwF4Alsj/7yteww1HXuiFH+6r6vSIU1bHdq6x/BpHbp1sJekU045Re+++67r+0P/qM6ePVsPPfSQnnvuOZ144om6//77df755+ubb75RZGSkJOm2227Tf/7zH7388svq16+f641pZWUlf6DhEc4VTIcPH67Nmze7jm/atEnHHnusjjvuOLdygBkxMgU9CVNOPGv48OGqrKxsV9k1a9YoLy9PCxYsUHJycoceAwB6sm4f7IODgxUbG9vquGEYeuSRR3T33Xe7/sg+//zziomJ0UsvvaTrr79eDQ0NevbZZzV//nydd955kqQFCxZo8ODBevfdd3XhhRd26XOBf8rIyFBwcLAr1I8bN04zZ87UrFmztHjxYm3evFnBwcGsdApTYw9e9BRMOfG88PDwDvemJycn0wMPAB3Q7ZcbrK6uVnx8vBITE3XllVfqf//7nySppqZGW7du1QUXXOAqGxoaqnPOOUfLly+XdHBY1oEDB9zKxMfHKyUlxVUGOFp79uxRS0uLJOmiiy7Svffeq1NOOUX33nuvLrroIklSS0uL9uzZ48tqAkfl0D1428IevPAHh085SUtLU+/evV1TTrKzszVt2jTZ7XZfVxUAADfdOtiPGjVKL7zwgt555x0988wz2rp1q9LT07Vjxw7XPPuYmBi3nzl0Dv7WrVsVEhKiPn36HLHMkezfv1+7d+92+wLacvHFF0uSzjjjDP33v/9Venq6oqKilJ6erjVr1ui0005zKweY0aF78DocDrdz7MELf+GccjJjxgwZhqHy8nItXLhQ5eXlMgxDBQUFqqmpkc1m83VVAQBw062H4o8fP971/6mpqRo9erSGDh2q559/XmlpaZKkgAD3/QINw2h17HDtKVNUVNRq4T6gLRs3bpQkPfnkkzrrrLNaLba0fPlynX322a5ygBmxBy96AudUkg0bNmjixImt5tjff//9buUAAOguunWP/eEiIiKUmpqq6upq17z7w3ve6+vrXb34sbGxam5u1s6dO49Y5kgKCgrU0NDg+tq0aZMHnwn8yfHHHy9JmjVrlmul04kTJyozM1NBQUGuN4LOcoBZOffgXb16tdvIlKqqKuYdwy84p5Lk5eUpNTVVFRUVamxsVEVFhVJTU5WXl+dWDgCA7sJUwX7//v1as2aN4uLilJiYqNjYWC1dutR1vrm5WR988IHS09MlSSNHjlSvXr3cytTV1amqqspV5khCQ0MVFRXl9gW0ZdGiRZKkxYsXa9++fW7n9u3bpyVLlriVA8wsJydH69ev17Jly/TSSy9p2bJlqq6uJtTDL6Snpys4OFgxMTGyWq1uc+ytVqtiYmIUHBz8i+8hAADoat16KP60adN0ySWX6Pjjj1d9fb3uv/9+7d69W1OmTFFAQIBuu+02FRYWatiwYRo2bJgKCwsVHh6uSZMmSZKio6N19dVXKz8/X/369VPfvn01bdo0paamulbJB45WdHS0hg4dqg0bNigiIkIXXHCB7rnnHt1///2uUD906FBFR0f7uKbAz9u3b5/Wrl3brrK9evWSw+FQr1699OWXX7b7MYYPH67w8PDOVhHwquXLl6ulpUX19fXKyclpNeWkvr5ehmFo+fLlbOsIAOhWunWw/+677zRx4kR9//33GjBggNLS0vTJJ59oyJAhkqQ//vGPampq0o033qidO3dq1KhRWrJkiWsPe0l6+OGHFRwcrMsvv1xNTU0aO3asnnvuOeaBwqPWr1+vpKQkbdiwQUuWLHEFeulgqF+/fr0Pawe0z9q1azVy5EivPkZlZSVbWKHbcs6dnz9/vu655x63nvnExETNnz9feXl5zLEHAHQ73TrYv/zyyz97PiAgQPfdd5/uu+++I5Y55phj9Pjjj+vxxx/3cO3Qk7SnJ/Pf//63GhoadOutt2rLli2Kj4/XY489pujoaK1YseIXH4OeTPja8OHDVVlZ2a6ya9asUV5enhYsWKDk5OQOPQZ+0pFREk1NTaqtrVVCQoLCwsLa/Ri8trSfc+688wPZwxdD/eyzz9zKAQDQXXTrYA90F53pyfzhhx907rnntrs8PZnwtfDw8A7fg8nJydy3R4FREt3Lods6lpaWug23Z1tHAEB3RrAH2oGeTADewGtL98K2jgAAsyLYA+1AT6ZnVFdXq7Gx0ePXXbNmjdt/vSEyMlLDhg3z2vXRM/Ha0v04t3XMz89vNceebR0BAN0VwR5Al6iurtaJJ57o1cdw7jHtLevWrSPcAz1ATk6OJkyY0GqOPT31AIDuimAPoEs4e+o7Ooy4PTq7qFh7OYdAe2O0AYDuKSgoiC3tAACmQbAH0KW8NYx4zJgxHr8mAAAAYAaBvq4AAAAAAPQETU1Nuvnmm3XhhRfq5ptvVlNTk6+rBD9BsAcAAAAAL7NYLAoPD9cTTzyhJUuW6IknnlB4eLgsFouvqwY/QLAHAAAAAC+yWCx64403FBISorvuukvr16/XXXfdpZCQEL3xxhuEexw15tgDAAAAgJc0NTW5Qn1jY6NCQkIkSUVFRZo1a5YiIyP1xhtvqKmpySuLAKNnINgDAIAeY9++fVq7dm27ynZ2x43hw4crPDy8s1UE4GemT58uSbrjjjtcod4pJCREt912m2bPnq3p06dr7ty5vqgi/ADBHgAA9Bhr167VyJEjvfoYlZWVXtn9A4A5VVdXS5KuueaaNs9fffXVmj17tqsc0BkEewAA0GMMHz5clZWV7Sq7Zs0a5eXlacGCBUpOTu7QYwCA07Bhw7RkyRL985//VFFRUavzzz77rKsc0FkEewAA0GOEh4d3uDc9OTmZHngAnfbggw/qiSee0EMPPaRZs2a5Dcdvbm7WI4884ioHdBar4gMAAACAl4SFhWnChAlqbm5WZGSk7rzzTq1bt0533nmnIiMj1dzcrAkTJrBwHo4KwR4AAAAAvKi0tNQV7mfPnq2TTjpJs2fPdoX60tJSX1cRJsdQfAAAAADwstLSUjU1NWn69Omqrq7WsGHD9OCDD9JTD48g2AMAAABAFwgLC2NLO3gFQ/EBAAAAADAxeuwBAACAbq66ulqNjY0ev+6aNWvc/usNkZGRbOUGeBnBHgAAAOjGqqurdeKJJ3r1MfLy8rx6/XXr1hHuAS8i2HcTfAoLAACAtjjfIy5YsEDJyckevXZTU5Nqa2uVkJDglUXc1qxZo7y8PK+8z+1O9u3bp7Vr17arbGfbfPjw4QoPD+9sFbu9ffv2SZJWrFjh8Wt3xX3uawT7boBPYQEAAPBLkpOTNWLECI9fd8yYMR6/Zk+zdu1ajRw50quPUVlZ6ZXff3fh/GDk2muv9XFNOi8yMtJnj02w7wb4FBYAAJgJIw0Bd8OHD1dlZWW7yjrfP3f0vf/w4cM7Wz1TsFgskrwzMqGzbd4Rvn5tIdh3I3wKCwAAujtGGgKthYeHd/h9vLfe+5tV//79dc0113j1Mfy5zQn2AAAAaDdGGgJA90OwBwAAQIcx0hAAuo9AX1cAAAAAAAB0HsEeAAAAAAATI9gDAAAAAGBiBHsAAAAAAEyMYA8AAAAAgIkR7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxAj2AAAAAACYWLCvKwAA8J7q6mo1NjZ6/Lpr1qxx+683REZGatiwYV67PgAAgL8g2AOAn6qurtaJJ57o1cfIy8vz6vXXrVtHuAcAAPgFBHsA8FPOnvoFCxYoOTnZo9duampSbW2tEhISFBYW5tFrSwdHAuTl5XlltAGAoxfbO0Bhu9ZJW8w1qzNs1zrF9g7wdTUAwOMI9gDg55KTkzVixAiPX3fMmDEevyYAc7h+ZIiSP7xe+tDXNemYZB2sOwD4G4I9AAAAOuSpymZdce9zSh4+3NdV6ZA1a9fqqeJJutTXFQEADyPYA+gyDN0EAP+wdY+hpmNPlOJP93VVOqRpq0Nb9xi+rkan8DcUwM8h2APoMgzdBACgc/gbCuDnEOy7CT6FRU/A0E0AADqHv6EAfg7BvpvgU9iux/7eXY+hm+gJeG0B4A38DQXwcwj23QSfwnYt9vcG4A28tgCA/+CDWpgJwb6b4FPYrsX+3gC8gdcWAPAPfFALsyHYo0djf28A3sBrS9eiVw2ApzU2Niq2d4CefeSvSkxM9Oi19+/fry1btig+Pl6hoaEevbYk1dTU6Orb7uaD2h6GYA8AAEyLXjUA3nL9yBBdtOkBaZPnr3265JXrSuZeAwudR7AHAACmxfQHAN7CGlgwE4I9AAAwPaY/APA01sCCmZhr03QAAAAAAOCGYA8AAAAAgIkR7AEAAAAAMDHm2AMAAKDd9u3bJ0lasWKFx6/dFQsWAoA/ItgDAACg3dauXStJuvbaa31ck86LjIz0dRUAwKMI9gDgx2J7Byhs1zppi7lmXoXtWqfY3gG+rgaANlgsFknS8OHDFR4e7tFrO7cA9Mb2hU6RkZEaNmyYV64NAL5CsO8GGNIGwFuuHxmi5A+vlz70dU06JlkH6w6g++nfv7+uueYarz6Gt7YvBAB/RbDvBhjSBsBbnqps1hX3Pqfk4cN9XZUOWbN2rZ4qnqRLfV0RAAAAEyDYdwMMaQPgLVv3GGo69kQp/nRfV6VDmrY6tHWP4etqAEC3wOhOAL+EYN8NMKQNPQFvSgAA6BxGdwL4JQR7AF2CNyUAAHQOozsB/BKCPYAuwZsSAAA6h9GdAH4JwR5Al+BNCQAAMAumEMJsCPYAAAAAcAimEMJsCPYA4KfobUBPEds7QGG71klbAn1dlQ4J27VOsb0DfF0NAG1gCiHMhmAPAH6K3gb0FNePDFHyh9dLH/q6Jh2TrIN1B9D9MIUQZkOwR49FDw/8Hb0N6CmeqmzWFfc+p+Thw31dlQ5Zs3atniqepEt9XREAgOkR7NFj0cMDf0dvA3qKrXsMNR17ohR/uq+r0iFNWx3ausfwdTUAAH6AYI8eix4eAN7AaCAAANDVCPbosejhAeANjAYCAABdjWAPAIAHMRoIAAB0NYI9AAAexGggAADQ1Qj2AAAAgB/Zt2+fa8vTX7JmzRq3/7aXN3ZcAdB5BHsAAADAj6xdu1YjR47s0M/k5eV1qHxlZSW7ogDdCMEeAAAA8CPDhw9XZWVlu8o2NTWptrZWCQkJCgsL69BjAOg+CPYAAMC09u3bJ0lasWKFx6/d2cDTXh0d+mxWDAvveuHh4R3qTR8zZowXawOgKxDs0SPxRhAA/IMzMF577bU+rknnRUZG+roKXsWwcADwPoI9eiTeCAKt0at29PjQsOtZLBZJ3rm31qxZo7y8PC1YsEDJyckevbZTZGSkhg0b5pVrdxcMCwcA7yPYo0fijSDQGr1qR48PDbte//79dc0113j1MZKTk/36vvU2hoUDgPcR7E2IXrWjxxtBoDV61Y4eHxoCAABfINibEL1qALyBXrWjx4eGAADAFwj2JkSvGnoCRqYAAACz4H1L16PN3RHsTYheNfQEjEwBAABmwfuWrkebuyPYA+iWGJkCAADMgvctXY82d0ewB9AtMTIFAACYBe9buh5t7o5gD7QDc3gAAAAAdFcEe6AdmMMDAP6BD2oBAP6IYA+0A3N4AMA/8EEtAMAfEeyBdmAODwBvoPe46/FBLQDAHwUYhmH4uhJmsHv3bkVHR6uhoUFRUVG+rg4AwA+sWLGiw73HHUXvMQAA5tXeHEqPPQAAPkLvMQAA8AR67NuJHnsAAAAAQFdqbw4N7MI6AQAAAAAADyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADCxHhXsn3zySSUmJuqYY47RyJEjZbPZfF0lAAAAAACOSo8J9q+88opuu+023X333Vq5cqUyMjI0fvx4bdy40ddVAwAAAACg03rMPvajRo3SiBEj9Pe//911LDk5WRaLRUVFRb/48+xjDwAAAADoSuxjf4jm5mZVVlbqggsucDt+wQUXaPny5T6qFQAAAAAARy/Y1xXoCt9//73sdrtiYmLcjsfExGjr1q1t/sz+/fu1f/9+1/e7d+/2ah0BAAAAAOiMHtFj7xQQEOD2vWEYrY45FRUVKTo62vU1ePDgrqgiAAAAAAAd0iOCff/+/RUUFNSqd76+vr5VL75TQUGBGhoaXF+bNm3qiqoCAAAAANAhPSLYh4SEaOTIkVq6dKnb8aVLlyo9Pb3NnwkNDVVUVJTbFwAAAAAA3U2PmGMvSXfccYcmT56sM888U6NHj9bTTz+tjRs36oYbbvB11QAAAAAA6LQeE+yvuOIK7dixQ3/+859VV1enlJQUvfXWWxoyZIivqwYAAAAAQKf1mH3sjxb72AMAAAAAuhL72AMAAAAA0AMQ7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxHrMqvhHy7nG4O7du31cEwAAAABAT+DMn7+05j3Bvp0aGxslSYMHD/ZxTQAAAAAAPUljY6Oio6OPeJ7t7trJ4XBoy5YtioyMVEBAgK+r0267d+/W4MGDtWnTJrbp6yK0edejzbsebd71aPOuR5t3Pdq869HmXY8273pmbnPDMNTY2Kj4+HgFBh55Jj099u0UGBioQYMG+boanRYVFWW6m9jsaPOuR5t3Pdq869HmXY8273q0edejzbsebd71zNrmP9dT78TieQAAAAAAmBjBHgAAAAAAEyPY+7nQ0FDNnDlToaGhvq5Kj0Gbdz3avOvR5l2PNu96tHnXo827Hm3e9WjzrtcT2pzF8wAAAAAAMDF67AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxAj2AAAAAACYGMHegz788ENdcsklio+PV0BAgEpLS93OG4ah++67T/Hx8QoLC1NmZqa+/vprtzJbt27V5MmTFRsbq4iICI0YMUIlJSVuZXbu3KnJkycrOjpa0dHRmjx5snbt2tXuev7www+65ZZbdNJJJyk8PFzHH3+8br31VjU0NLRZfv/+/Tr99NMVEBCgVatWuZ3buHGjLrnkEkVERKh///669dZb1dzc7FZm9erVOueccxQWFqbjjjtOf/7zn+WtNRsTEhIUEBDQ6uumm27SgQMHdOeddyo1NVURERGKj4/Xb3/7W23ZsqXV873lllvUv39/RURE6NJLL9V3333XoXpYrVZdeOGF6t+/f5vt5lRRUaFzzz1XEREROvbYY5WZmammpibX+fb8rtvzO/Akf7vPL730Uh1//PE65phjFBcXp8mTJ7e6J5x27NihQYMGKSAgoFVdvHmf+1ubOx3ta8s777yjtLQ0RUZGasCAAbrssstUU1PT7vp2hJleWzIzM1vV88orr3Sdr62t1dVXX63ExESFhYVp6NChmjlzZqv27erXlsOZpc3be9+vW7dOEyZMUP/+/RUVFaUxY8Zo2bJlbmV4PW+bJ19b/vCHP2jkyJEKDQ3V6aef3upny8vLNWHCBMXFxSkiIkKnn366XnzxxXbX9Zf4W5v/9a9/VXp6usLDw3Xsscce8XrPPfecTj31VB1zzDGKjY3VzTff7Haev6EHPf3008rMzFRUVFSb7zUkacWKFTr//PN17LHHql+/frruuuu0Z88e1/kvv/xSEydO1ODBgxUWFqbk5GQ9+uijra7jyzY/1PXXX6+AgAA98sgjbZ43DEPjx49v8zqeaPPrr79eQ4cOVVhYmAYMGKAJEyZo7dq1rvOe/Jvp9UxkwGPeeust4+677zZee+01Q5Lx+uuvu51/4IEHjMjISOO1114zVq9ebVxxxRVGXFycsXv3bleZ8847zzjrrLOMTz/91NiwYYPxl7/8xQgMDDRWrFjhKjNu3DgjJSXFWL58ubF8+XIjJSXFyM7Obnc9V69ebeTk5BhvvvmmsX79euO9994zhg0bZlx22WVtlr/11luN8ePHG5KMlStXuo63tLQYKSkpRlZWlrFixQpj6dKlRnx8vHHzzTe7yjQ0NBgxMTHGlVdeaaxevdp47bXXjMjISGPOnDntrm9H1NfXG3V1da6vpUuXGpKMZcuWGbt27TLOO+8845VXXjHWrl1rVFRUGKNGjTJGjhzpdo0bbrjBOO6444ylS5caK1asMLKysozTTjvNaGlpaXc9XnjhBWPWrFnGM88806rdnJYvX25ERUUZRUVFRlVVlbFu3Trj1VdfNX788UdXmV/6Xbfnd+Bp/nafP/TQQ0ZFRYVRW1trfPzxx8bo0aON0aNHt3nNCRMmuP4t7Ny503Xc2/e5v7W509G8tmzYsMEIDQ01CgoKjPXr1xuVlZXG2WefbZx++untrm9HmOm15ZxzzjGuvfZat/ru2rXLdf7tt982pk6darzzzjvGhg0bjDfeeMMYOHCgkZ+f7yrji9eWw5mlzdt73yclJRkXXXSR8eWXXxrr1q0zbrzxRiM8PNyoq6szDIPX85/jqdcWwzCMW265xZg7d64xefJk47TTTmv1s3/961+Ne+65x/j444+N9evXG48++qgRGBhovPnmm+2u78/xtza/9957jYceesi44447jOjo6DavVVxcbMTHxxsvvviisX79eqOqqsqtPfkb+pOHH37YKCoqMoqKilq91zAMw9i8ebPRp08f44YbbjDWrl1rfPbZZ0Z6errb7+XZZ581brnlFqO8vNzYsGGDMX/+fCMsLMx4/PHHXWV83eZOr7/+unHaaacZ8fHxxsMPP9xmmYceesj17/nw63iizZ966injgw8+MGpqaozKykrjkksuMQYPHuz6O+Gpv5ldkYkI9l5y+M3ncDiM2NhY44EHHnAd+/HHH43o6GjjH//4h+tYRESE8cILL7hdq2/fvsY///lPwzAM47///a8hyfjkk09c5ysqKgxJxtq1aw3DMIxZs2YZcXFxxvfff+8qc8kllxgZGRmG3W5vs77//ve/jZCQEOPAgQNux9966y1j+PDhxtdff93qD+Rbb71lBAYGGps3b3YdW7hwoREaGmo0NDQYhmEYTz75pBEdHe0WVouKioz4+HjD4XC03Xge9Ic//MEYOnToER/rs88+MyQZ3377rWEYhrFr1y6jV69exssvv+wqs3nzZiMwMNBYvHixYRiG8fzzzxsRERHGunXrXGVuvvlmY9iwYcaePXvcrl9TU3PEN9+jRo0y7rnnniPWvT2/6/b8DrzJX+7zQ73xxhtGQECA0dzc7Hb8ySefNM455xzjvffea/XHtivvc39p86N9bXn11VeN4OBgt8d988032/zdeUN3fm0555xzjD/84Q8dej6zZ882EhMTXd/7+rWlLd25zQ93+H2/fft2Q5Lx4Ycfusrs3r3bkGS8++67hmH4vs17wmvLoWbOnNlmsG/LRRddZPzud79rV9mO8Jc2NwzDmDdvXpvB/ocffjDCwsJc93lb+Bvaus2XLVvWZrB/6qmnjIEDB7qVX7lypSHJqK6uPuLzvvHGG42srCzX975sc6fvvvvOOO6444yqqipjyJAhbQb7VatWGYMGDTLq6upaXcdb9/mXX35pSDLWr19/xOfUmb+ZXdHmDMXvIjU1Ndq6dasuuOAC17HQ0FCdc845Wr58uevYr371K73yyiv64Ycf5HA49PLLL2v//v3KzMyUdHDodnR0tEaNGuX6mbS0NEVHR7uuc/fddyshIUHXXHONJOkf//iHPvzwQ82fP1+BgW3/yhsaGhQVFaXg4GDXsW3btunaa6/V/PnzFR4e3upnKioqlJKSovj4eNexCy+8UPv371dlZaWrzDnnnKPQ0FC3Mlu2bFFtbW17m69TmpubtWDBAv3+979XQEBAm2UaGhoUEBDgGj5WWVmpAwcOuP2e4uPjlZKS4mrf3/72t7rooot01VVXqaWlRYsXL9ZTTz2lF198UREREe2qW319vT799FMNHDhQ6enpiomJ0TnnnKOPPvrIVaY9v+v2/A66khnv80P98MMPevHFF5Wenq5evXq5jv/3v//Vn//8Z73wwgttXtuX97kZ29wTry1nnnmmgoKCNG/ePNntdjU0NGj+/Pm64IIL3H533tCdX1ucXnzxRfXv31+nnHKKpk2bpsbGxp8t39DQoL59+7q+726vLWZo88Prcuh9369fPyUnJ+uFF17Q3r171dLSoqeeekoxMTEaOXKkpO7X5v742tJZh//78BYztvkvWbp0qRwOhzZv3qzk5GQNGjRIl19+uTZt2uQqw9/QI7f54fbv36+QkBC38mFhYZLk9h7ycG29xvuqzSXJ4XBo8uTJmj59uk455ZQ2y+zbt08TJ07U3LlzFRsb2+q8N9p87969mjdvnhITEzV48OAj1r8zfzO7os0J9l1k69atkqSYmBi34zExMa5zkvTKK6+opaVF/fr1U2hoqK6//nq9/vrrGjp0qOs6AwcObHX9gQMHuq4TFBSkBQsW6L333tNdd92l/Px8PfHEExoyZEibdduxY4f+8pe/6Prrr3cdMwxDU6dO1Q033KAzzzzziM/p8OfTp08fhYSEuOrSVhnn94c+b28oLS3Vrl27NHXq1DbP//jjj7rrrrs0adIkRUVFueoUEhKiPn36tKrzofV96qmnVFdXp1tvvVVTp07VzJkzddZZZ7W7bv/73/8kSffdd5+uvfZaLV68WCNGjNDYsWNVXV3tqssv/a7b8zvoSma7z53uvPNORUREqF+/ftq4caPeeOMN17n9+/dr4sSJevDBB3X88ccf8Xn76j43W5t76rUlISFBS5Ys0YwZMxQaGqpjjz1W3333nV5++eWfbS9P6M6vLZJ01VVXaeHChSovL9ef/vQnvfbaa8rJyTli+Q0bNujxxx/XDTfc4DrW3V5bunubH6qt+z4gIEBLly7VypUrFRkZqWOOOUYPP/ywFi9e7Pogoru1uT++tnRGSUmJPv/8c/3ud7/z2DWPxGxt3h7/+9//5HA4VFhYqEceeUQlJSX64YcfdP7557vmH/M3tO02b8u5556rrVu36sEHH1Rzc7N27typGTNmSJLq6ura/JmKigr9+9//dvvd+bLNJelvf/ubgoODdeuttx6xzO2336709HRNmDChzfOebPMnn3xSvXv3Vu/evbV48WItXbpUISEhbT5uZ/9mdkWbE+y72OE9DYZhuB275557tHPnTr377rv64osvdMcdd+g3v/mNVq9efcRrtHWdE044QXPmzNHf/vY3XXLJJbrqqqvarM/u3bt18cUX6+STT9bMmTNdxx9//HHt3r1bBQUFHXo+bdWlred8pJ/1pGeffVbjx493+/TM6cCBA7ryyivlcDj05JNP/uK1Dn9Offr00bPPPqu///3vGjp0qO66664O1c3hcEg6uGDH7373O51xxhl6+OGHddJJJ+lf//qXq1xn2retMl3NLPe50/Tp07Vy5UotWbJEQUFB+u1vf+u6TwsKCpScnKy8vLwOP+cjPQ9vMEube+q1ZevWrbrmmms0ZcoUff755/rggw8UEhKi3Nxcry3O6dSdX1sk6dprr9V5552nlJQUXXnllSopKdG7776rFStWtCq7ZcsWjRs3Tr/5zW9cPRpO3em1pbu3udOR7nvDMHTjjTdq4MCBstls+uyzzzRhwgRlZ2e7vRnvTm3u5G+vLR1RXl6uqVOn6plnnjlir6I3mKXN28PhcOjAgQN67LHHdOGFFyotLU0LFy5UdXW12+KR/A1tn1NOOUXPP/+8iouLFR4ertjYWJ1wwgmKiYlRUFBQq/Jff/21JkyYoHvvvVfnn3/+Lz7nIz0PT6qsrNSjjz6q55577oiP9eabb+r9998/4oJ6Tp5q86uuukorV67UBx98oGHDhunyyy/Xjz/+2Krc0f7N9HabE+y7iHMIyeGfyNTX17s+rdmwYYPmzp2rf/3rXxo7dqxOO+00zZw5U2eeeaaeeOIJ13W2bdvW6vrbt29v9SnQhx9+qKCgINXW1qqlpaXVzzQ2NmrcuHHq3bu3Xn/9dbfhq++//74++eQThYaGKjg4WElJSZIODn+dMmWKqy6HP5+dO3fqwIEDrrq0Vaa+vl5S609HPenbb7/Vu+++2+ofnXTwTeDll1+umpoaLV261NW746yv8xPQw+t8pPbdsmWL9u7d26H6xcXFSZJOPvlkt+PJycnauHGjqy6/9Ltuz++gK5ntPnfq37+/TjzxRJ1//vl6+eWX9dZbb+mTTz6RdPDfwquvvqrg4GAFBwdr7Nixrp9xvsHx1X3ufGzJPG3uqdeWJ554QlFRUZo9e7bOOOMMnX322a5P5T/99NP2N2AHdffXlraMGDFCvXr1co0GctqyZYuysrI0evRoPf30027nutNri1na/Jfu+7KyMr388ssaM2aMRowYoSeffFJhYWF6/vnnXfXtLm3urI/kX68tHfHBBx/okksu0UMPPaTf/va3Hf75zjBbm7dHW+93BgwYoP79+7u93+FvaPtNmjRJW7du1ebNm7Vjxw7dd9992r59uxITE93K/fe//9W5556ra6+9Vvfcc0+r5+2rNrfZbKqvr9fxxx/vem/17bffKj8/XwkJCZIO/nvesGGDjj32WFcZSbrssstc0x882ebR0dEaNmyYzj77bJWUlGjt2rV6/fXX3coc7d/Mrmhzgn0XSUxMVGxsrJYuXeo61tzcrA8++EDp6emSDs4lkdRqzkdQUJCrh3f06NFqaGjQZ5995jr/6aefqqGhwXUd6eCQIavVqvLycm3atEl/+ctf3K65e/duXXDBBQoJCdGbb76pY445xu38Y489pi+//FKrVq3SqlWr9NZbb7mu+9e//tVVl6qqKrfehiVLlig0NNQ1Z3D06NH68MMP3bZ7WLJkieLj413/eL1h3rx5GjhwoC6++GK34843gdXV1Xr33XfVr18/t/MjR45Ur1693H5PdXV1qqqqcmvf5cuXa/bs2frPf/6jqKgo3XLLLR2qX0JCguLj4/XNN9+4HV+3bp1reFB7ftft+R10JbPd521xfnq6f/9+SdJrr73m9m/hn//8p6SDf5huuukmV319cZ9L5mtzT7227Nu3r1XvhPN753Pyhu7+2tKWr7/+WgcOHHC9wZakzZs3KzMzUyNGjNC8efNa3Rvd6bXFDG3+S/f9kf4NBgYGuv0b7C5tLvnna0t7lZeX6+KLL9YDDzyg6667rkM/ezTM1ubtMWbMGElye7/zww8/6Pvvv3d7v8Pf0I6LiYlR79699corr+iYY45x65H/+uuvlZWVpSlTprR5//uyzSdPnqyvvvrK9W911apVio+P1/Tp0/XOO+9Iku66665WZSTp4Ycf1rx581zPwVttbhiG632g5Jm/mV3S5h5Zgg+GYRhGY2OjsXLlStfqlA899JCxcuVK1wq9DzzwgBEdHW1YrVZj9erVxsSJE92202hubjaSkpKMjIwM49NPPzXWr19vzJkzxwgICDAWLVrkepxx48YZp556qlFRUWFUVFQYqampbls7bNq0yejTp4/x2GOPGYZhGEuWLDF69eplVFRUGIZxcCXeUaNGGampqcb69evdthM60hZAba0G7NzaYezYscaKFSuMd9991xg0aJDb1g67du0yYmJijIkTJxqrV682rFarERUV5bXt7gzDMOx2u3H88ccbd955p9vxAwcOGJdeeqkxaNAgY9WqVW7Pe//+/a5yN9xwgzFo0CDj3XffNVasWGGce+65btsj7d692zjhhBOMO+64wzAMw6iqqjKOOeYY49///rfrGjt27DBWrlxpLFq0yJBkvPzyy8bKlStd2xoZxsEtTaKiooxXX33VqK6uNu655x7jmGOOcVuF85d+1+35HXiaP93nn376qfH4448bK1euNGpra43333/f+NWvfmUMHTrUbdXSQ7W1Uq2373N/avPDdfa15b333jMCAgKMWbNmGevWrTMqKyuNCy+80BgyZIixb98+j7T74czw2rJ+/Xpj1qxZxueff27U1NQYixYtMoYPH26cccYZrsfZvHmzkZSUZJx77rnGd99951ZfJ1+8trTFDG3envt++/btRr9+/YycnBxj1apVxjfffGNMmzbN6NWrl7Fq1SrDMHg974rXFsMwjOrqamPlypXG9ddfb5x44omu5++8b5YtW2aEh4cbBQUFbo+zY8cO2ryNNv/222+NlStXGrNmzTJ69+7tem6NjY2uMhMmTDBOOeUU4+OPPzZWr15tZGdnGyeffLJrBxP+hla4ytTV1RkrV650ba/54YcfGitXrnS7/x5//HGjsrLS+Oabb4y5c+caYWFhxqOPPuo6X1VVZQwYMMC46qqr3H5v9fX1rjK+bvPDHWlV/EPpCNvdHU2bb9iwwSgsLDS++OIL49tvvzWWL19uTJgwwejbt6+xbds2wzA89zezKzIRwd6DnG/4D/+aMmWKYRgHt9SYOXOmERsba4SGhhpnn322sXr1ardrrFu3zsjJyTEGDhxohIeHG6eeemqr7TV27NhhXHXVVUZkZKQRGRlpXHXVVa6Q4XA4jLFjxxoXXnih29YJt99+uzF06FCjsbHxiPWUZNTU1LT53I70B/Lbb781Lr74YiMsLMzo27evcfPNN7cKRF999ZWRkZFhhIaGGrGxscZ9993n1a3u3nnnHUOS8c0337T5HNr6WrZsmatcU1OTcfPNNxt9+/Y1wsLCjOzsbGPjxo2u87/73e+M1NRUt+f56KOPGn379jW+++47wzAObvvS1uPMnDnTrU5FRUXGoEGDjPDwcGP06NGGzWZzO/9zv2un9vwOPMmf7vOvvvrKyMrKMvr27WuEhoYaCQkJxg033OD6Pf7c8z/89+DN+9yf2vxwR/PasnDhQuOMM84wIiIijAEDBhiXXnqpsWbNmo43cDuZ4bVl48aNxtlnn2307dvXCAkJMYYOHWrceuutbm8Kj3SNwz/r7+rXlraYoc3be99//vnnxgUXXGD07dvXiIyMNNLS0oy33nrL7Xnxeu7915ZzzjnnZ68zZcqUNs+fc845nW1mN/7W5kdqr0P/HTY0NBi///3vjWOPPdbo27ev8etf/9rt36Fh8DfU+UHIzJkz26zrvHnzXD8zefJk12t8W/U40jWGDBniVs6XbX64zgb7o23zzZs3G+PHjzcGDhxo9OrVyxg0aJAxadIk13Z5huHZv5nezkQB/39DAQAAAAAAE2KOPQAAAAAAJkawBwAAAADAxAj2AAAAAACYGMEeAAAAAAATI9gDAAAAAGBiBHsAAAAAAEyMYA8AAAAAgIkR7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxAj2AAAAAACYGMEeAAAAAAAT+/8ApuCHA2WR2vUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resolutions = data['resolution'].value_counts().index.values[0:10]\n",
    "to_plot = []\n",
    "labels = []\n",
    "\n",
    "for i in resolutions:\n",
    "    to_plot.append(data[data['resolution'] == i]['price(USD)'])\n",
    "    labels.append(i)\n",
    "\n",
    "plt.figure(figsize=(12, 7))\n",
    "plt.boxplot(to_plot, labels=labels)\n",
    "plt.ylabel('Price')\n",
    "plt.title('Price Box Plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "d5c151cf-ce4e-4908-abd7-2b26c042c5e6",
   "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>inches</th>\n",
       "      <th>battery</th>\n",
       "      <th>ram(GB)</th>\n",
       "      <th>weight(g)</th>\n",
       "      <th>storage(GB)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2200</td>\n",
       "      <td>2</td>\n",
       "      <td>140.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.3</td>\n",
       "      <td>2700</td>\n",
       "      <td>2</td>\n",
       "      <td>140.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3000</td>\n",
       "      <td>4</td>\n",
       "      <td>147.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.5</td>\n",
       "      <td>4100</td>\n",
       "      <td>4</td>\n",
       "      <td>165.0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3200</td>\n",
       "      <td>4</td>\n",
       "      <td>145.0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   inches  battery  ram(GB)  weight(g)  storage(GB)\n",
       "0     5.0     2200        2      140.0           16\n",
       "1     5.3     2700        2      140.0           16\n",
       "2     5.2     3000        4      147.0           16\n",
       "3     5.5     4100        4      165.0           32\n",
       "4     5.1     3200        4      145.0           32"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.copy()\n",
    "x = df[['inches', 'battery', 'ram(GB)', 'weight(g)', 'storage(GB)']]\n",
    "y = df['price(USD)']\n",
    "\n",
    "x.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "468ea107-6df3-44d2-b2e8-8a24d9d39cc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number transactions x_train dataset: (1209, 5)\n",
      "Number transactions y_train dataset: (1209, 1)\n",
      "Number transactions x_test dataset: (303, 5)\n",
      "Number transactions y_test dataset: (303, 1)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "def preprocessing(data, label, test_size=0.2):\n",
    "    scaler = StandardScaler()\n",
    "    x_scaled = scaler.fit_transform(data)\n",
    "    y_scaled = scaler.fit_transform(np.asarray(y).reshape(-1, 1))\n",
    "    return train_test_split(x_scaled, y_scaled, test_size=0.2)\n",
    "\n",
    "x_train, x_test, y_train, y_test = preprocessing(x, y)\n",
    "\n",
    "print('Number transactions x_train dataset:', x_train.shape)\n",
    "print('Number transactions y_train dataset:', y_train.shape)\n",
    "print('Number transactions x_test dataset:', x_test.shape)\n",
    "print('Number transactions y_test dataset:', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "5a27cbdc-87f0-4264-959a-a6e287f23b1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.metrics import mean_squared_error, accuracy_score\n",
    "\n",
    "models = [['KNeighbors', KNeighborsRegressor()],\n",
    "         ['Random Forest', RandomForestRegressor()],\n",
    "         ['SVM', SVR()],\n",
    "         ['SGD', SGDRegressor()],\n",
    "         ['GradientBoosting', GradientBoostingRegressor()]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "a067b3f7-97f2-4bc5-b8a4-8fe55eb3ef4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighbors: [[-0.74946592]\n",
      " [-0.27694099]\n",
      " [ 0.33057456]\n",
      " [ 0.02214478]\n",
      " [-0.7794675 ]\n",
      " [ 2.53119065]\n",
      " [-0.37444614]\n",
      " [ 1.56439114]\n",
      " [-0.27844857]\n",
      " [-0.66696157]\n",
      " [-0.22488825]\n",
      " [-0.48590202]\n",
      " [-0.20943743]\n",
      " [-0.23943902]\n",
      " [ 0.4678483 ]\n",
      " [-0.53195445]\n",
      " [-0.6219592 ]\n",
      " [-0.56195603]\n",
      " [ 0.06807721]\n",
      " [ 0.28253452]\n",
      " [-0.19443664]\n",
      " [ 0.21633003]\n",
      " [ 1.5824071 ]\n",
      " [ 2.48320462]\n",
      " [-0.48766461]\n",
      " [-0.41944851]\n",
      " [ 1.50065278]\n",
      " [-0.13818367]\n",
      " [-0.27844857]\n",
      " [ 0.86138506]\n",
      " [-0.05950452]\n",
      " [ 0.06057681]\n",
      " [-0.71270798]\n",
      " [-0.05193662]\n",
      " [-0.02192754]\n",
      " [-0.26202271]\n",
      " [-0.48590202]\n",
      " [ 0.50130006]\n",
      " [ 1.87974528]\n",
      " [-0.4551504 ]\n",
      " [-0.59945801]\n",
      " [-0.46445089]\n",
      " [ 0.17308275]\n",
      " [ 0.54810253]\n",
      " [-0.07863953]\n",
      " [ 0.367343  ]\n",
      " [ 0.11307958]\n",
      " [-0.4419587 ]\n",
      " [-0.04443623]\n",
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      " [-0.73144397]\n",
      " [-0.14118383]\n",
      " [ 0.52635138]\n",
      " [-0.34426005]\n",
      " [ 0.187326  ]\n",
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      " [ 0.5247688 ]\n",
      " [-0.2429117 ]\n",
      " [-0.12693308]\n",
      " [-0.52604414]\n",
      " [ 0.87341719]\n",
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      " [ 0.48809937]\n",
      " [ 0.39059422]\n",
      " [ 0.19557643]\n",
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      " [-0.25444731]\n",
      " [ 0.28253452]\n",
      " [-0.43069911]\n",
      " [-0.29194179]\n",
      " [ 1.06487979]\n",
      " [-0.26088265]\n",
      " [-0.27694099]\n",
      " [-0.3984654 ]\n",
      " [-0.41194812]\n",
      " [-0.41194812]\n",
      " [-0.31069277]\n",
      " [ 0.10527917]\n",
      " [-0.47945168]\n",
      " [ 0.89910455]\n",
      " [-0.20943743]\n",
      " [ 0.28312105]\n",
      " [-0.05942952]\n",
      " [ 0.06807721]\n",
      " [ 0.33809145]\n",
      " [ 0.39059422]\n",
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      " [ 0.01932464]\n",
      " [ 0.49559076]\n",
      " [-0.7944683 ]\n",
      " [ 0.48809937]\n",
      " [ 0.00687398]\n",
      " [ 0.62237895]\n",
      " [-0.32944376]\n",
      " [-0.54695524]\n",
      " [-0.28873162]\n",
      " [ 0.47234853]\n",
      " [-0.1974518 ]\n",
      " [ 3.51075882]\n",
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      " [-0.4306766 ]\n",
      " [-0.45695049]\n",
      " [ 0.19558393]\n",
      " [ 0.05437398]\n",
      " [ 0.44459707]\n",
      " [ 0.45794777]\n",
      " [-0.41194812]\n",
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      " [ 0.187326  ]\n",
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      " [ 0.05982677]\n",
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      " [-0.6219592 ]\n",
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      " [ 0.55110269]\n",
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      " [-0.49714811]\n",
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      " [-0.24693941]\n",
      " [ 0.6043555 ]\n",
      " [ 1.65137323]\n",
      " [-0.3984654 ]\n",
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      " [ 0.00567392]\n",
      " [-0.7205834 ]\n",
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      " [-0.65013818]\n",
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      " [ 0.55110269]\n",
      " [-0.54695524]\n",
      " [-0.51636112]\n",
      " [-0.34426005]\n",
      " [-0.35375255]\n",
      " [-0.28147873]\n",
      " [-0.17944335]\n",
      " [-0.15993482]\n",
      " [-0.20943743]\n",
      " [ 0.57810411]\n",
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      " [-0.50136033]\n",
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      " [ 1.65816109]\n",
      " [-0.87683614]\n",
      " [-0.7794675 ]\n",
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      " [ 0.21283484]\n",
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      " [ 0.187326  ]\n",
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      " [ 0.23947625]\n",
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      " [ 0.01549943]\n",
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      " [ 0.02040469]\n",
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      " [ 0.05126882]\n",
      " [-0.07443031]\n",
      " [-0.72696473]\n",
      " [ 1.9881785 ]\n",
      " [ 0.17341276]\n",
      " [ 0.46484814]\n",
      " [-0.57156554]\n",
      " [-0.07893055]\n",
      " [ 0.0414433 ]\n",
      " [-0.27694099]\n",
      " [-0.49222035]\n",
      " [ 0.28558868]\n",
      " [-0.47195128]\n",
      " [ 0.00799904]\n",
      " [-0.85944422]\n",
      " [ 0.21283484]\n",
      " [ 0.5361019 ]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\abrao\\AppData\\Local\\Temp\\ipykernel_312\\351532293.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  model = i[1].fit(x_train, y_train)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest: [-0.56773628 -0.3690396   0.31655234  0.87981312 -0.90930747  3.80186268\n",
      " -0.50292792  4.82967528  0.01743618 -0.45207936 -0.51724517  0.9807296\n",
      " -0.08920142 -0.04626282 -0.25508522 -0.48951166 -0.88006656 -0.52715181\n",
      " -0.19141461  0.15564382 -0.10742493  0.10588745  0.88441458  1.18964748\n",
      "  0.12632528 -0.87754054  3.05448725  0.01900889 -0.27929193  0.27892658\n",
      "  0.49853697  0.93023269 -0.89904346  0.25036555  0.01275448  0.03263896\n",
      "  0.9807296   0.12943353  0.85525123 -0.03010035 -0.48358985 -0.43474244\n",
      "  0.30343253  0.33725873  0.24875763  0.0620415  -0.13359638 -0.15458196\n",
      "  0.06359771 -0.26360244 -0.66634145 -0.13416941  0.92236915 -0.80695523\n",
      "  0.01850322  0.46895231  0.08484115  0.86769364  0.047617    0.09932986\n",
      "  0.01260892 -0.08975587 -0.1974691   2.27230699 -0.36933962 -0.32863122\n",
      "  1.97996498  0.2294991  -0.27859853  0.39482189 -0.13890945 -0.17132717\n",
      "  0.15564382 -0.46836559 -0.02767784  0.89699356 -0.37297912 -0.00917012\n",
      " -0.3456852   0.4143626  -0.34947057 -0.10094421  0.37995169 -0.69621311\n",
      "  2.07136989  0.3555814   0.45611293  0.83940627  0.37427119  0.63999738\n",
      " -0.03880136  0.0397825   0.20161472  0.23836407  0.93923574 -0.33564409\n",
      "  0.31543713 -0.18800792  0.22850842  0.13295526 -0.70172285  0.15124961\n",
      " -0.09083236 -0.23239877  1.55193629  1.67215125 -0.24546378 -0.56060596\n",
      "  0.16172277  1.02738385 -0.39947321 -0.3077744   0.98982821 -0.13416941\n",
      "  0.11659764 -0.85874992 -0.37053794 -0.37985417 -0.55033042 -0.08326488\n",
      " -0.17318552 -0.38716522 -0.66002995  1.8384796   0.77768614 -0.57515601\n",
      "  3.12988086 -0.28137598 -0.68277582  0.02931574  0.047617   -0.42807397\n",
      " -0.21871972  0.37738854  0.74203806 -0.39244709  0.81977864  0.3749615\n",
      " -0.91604985  0.61888482 -0.50084131 -0.53048557 -0.21871972 -0.28472265\n",
      " -0.22331316  0.14052194 -0.41868985  0.37813924  0.11700286  2.0773032\n",
      " -0.53681133 -0.08954572  0.12150315 -0.10094421 -0.6662199   0.20893517\n",
      " -0.83966481  0.54911321  1.6018789  -0.04788829  0.45215564 -0.42988731\n",
      " -0.39486761  1.14782339 -0.37415446  0.35459382  0.01850322 -0.4248238\n",
      "  0.26041244 -0.5822571   3.63350242 -0.93245946 -0.05226839 -0.36657105\n",
      " -0.0899455   0.34913963 -0.07222382  0.255243   -0.28824159 -0.53362954\n",
      "  0.27027647 -0.29783932 -0.50513169 -0.13312484 -0.64037779  0.04595248\n",
      " -0.54545214 -0.3787355  -0.24947955 -0.21814357  1.8123966  -0.45000347\n",
      " -0.40876545 -0.19556245 -0.37749617  0.61449939 -0.3912723  -0.08954572\n",
      " -0.5352368  -0.17028537 -0.09393274 -0.24275593  0.61415289 -0.648997\n",
      " -0.56060596  0.59562334 -0.60894458 -0.08775296 -0.4297195  -0.62969085\n",
      " -0.63032589 -0.35175202  0.01204138 -0.15055468 -0.06781111 -0.44738599\n",
      "  0.01969966  1.16029908 -0.03875181 -0.36437332 -0.28533848 -0.60373799\n",
      " -0.75916136 -0.38037208  0.25585016 -0.27336306 -0.77577981 -0.47763293\n",
      " -0.60758082 -0.03229938 -0.08954572  0.32571022 -0.53827568 -0.42887985\n",
      "  0.01850322 -0.41838861 -0.47163039 -0.73026581 -0.57674456  0.80658973\n",
      "  3.63350242 -0.87950446 -0.8094734   3.10600622 -0.60894458 -0.34534239\n",
      " -0.6201562   0.01850322 -0.4557583  -0.26769616 -0.34874775 -0.52715181\n",
      " -0.3794534  -0.61480841 -0.77345931 -0.16668683  0.23143358  1.40937109\n",
      " -0.17643069 -0.41144884 -0.57165704  0.08833109 -0.14783788 -0.39073987\n",
      "  0.38744706 -0.67523961  0.15124961  0.48334631 -0.34588019  0.21498427\n",
      "  0.59612551  0.19892971  2.45238399 -0.62120916 -0.2813041  -0.20240381\n",
      "  0.45471277 -0.41870966 -0.54545214 -0.42113779 -0.28739155 -0.41137384\n",
      " -0.06203411  0.52310438 -0.64322295 -0.30402792  0.21360364 -0.04640433\n",
      " -0.54545214  0.5531653   0.19095356  1.47100097 -0.49402787  0.2926403\n",
      "  0.42229083 -0.49448435  0.57090261 -0.07293229 -0.26909683  0.10240074\n",
      " -0.37554553 -0.34534239  0.7621522 ]\n",
      "SVM: [-0.59262    -0.64245381  0.33715417 -0.01928819 -0.79719897  1.98019808\n",
      " -0.38201261  0.0515352  -0.17527267 -0.58087872 -0.25800934 -0.50550792\n",
      " -0.21298425 -0.3874113  -0.1898132  -0.46286901 -0.63269643 -0.43129525\n",
      "  0.07343563 -0.04385958 -0.19726669  0.22051707 -0.2120935   1.76514864\n",
      " -0.20120812 -0.6220948   1.36381871 -0.30858893 -0.17314475  0.62405763\n",
      " -0.16217587  0.47735423 -0.78611952 -0.34092767  0.03400886  0.07038654\n",
      " -0.50550792 -0.16873008 -0.13205876 -0.42617098 -0.62207024 -0.22733137\n",
      " -0.49286635  0.08303358 -0.20312834 -0.09097735  0.14632001 -0.67415631\n",
      " -0.19198555 -0.61724981 -0.53777852 -0.23621521  0.53022045 -0.67644077\n",
      " -0.14142846 -0.30789576  0.2299525   0.21244955 -0.18721539 -0.12945021\n",
      " -0.19030484  0.10369928 -0.41587479  0.9020931  -0.40605587 -0.48904276\n",
      " -0.07375173  0.21846328 -0.3898855  -0.65602236 -0.1691927  -0.12676534\n",
      " -0.04385958 -0.24770762 -0.30224067  0.89575574 -0.22890675  0.23548805\n",
      " -0.7071747  -0.17289359 -0.42949105 -0.29779767 -0.05165391 -0.4409474\n",
      "  1.6646008  -0.55628317 -0.35859474  0.34782046  0.16405921  0.30761378\n",
      " -0.0656519  -0.16288316 -0.43222786  0.2106966  -0.25110173 -0.39100906\n",
      "  0.02620928 -0.26180776 -0.35352758 -0.51508628 -0.66339572 -0.61097141\n",
      " -0.41341742 -0.31842476  1.28454964 -0.32542722 -0.42603707 -0.59159541\n",
      " -0.24228293 -0.25056068 -0.38934175 -0.18236699  0.78609387 -0.23621521\n",
      "  0.11161541 -0.83591457 -0.41126718 -0.15870328 -0.60872841 -0.18732388\n",
      " -0.66871254 -0.20642957 -0.74500512  0.02982324 -0.18875565 -0.72380215\n",
      "  2.64456144 -0.34868343 -0.60732388 -0.02429151 -0.18721539 -0.5038631\n",
      " -0.18254508 -0.13036529 -0.69230034 -0.29579586 -0.062222    0.9025299\n",
      " -0.79730371 -0.15483986 -0.49056764 -0.62313465 -0.18254508 -0.626568\n",
      " -0.74196824 -0.43024687 -0.7843199   0.17400317 -0.16326505  1.04963442\n",
      " -0.60887079 -0.26360952  0.40814237 -0.29779767 -0.63523411 -0.35997042\n",
      " -0.83700625  0.07381175 -0.75356246 -0.00676851 -0.11635177 -0.36569766\n",
      "  0.03299997 -0.44546811 -0.34112305 -0.04655099 -0.14142846 -0.46518571\n",
      "  0.10614734 -0.46647274  0.36552389 -0.82419447 -0.18974577 -0.16652323\n",
      " -0.46469365 -0.25924995  0.38185381 -0.2505565  -0.67794835 -0.53822293\n",
      " -0.49968419 -0.22127649 -0.51727476 -0.68389966 -0.6163027  -0.70955168\n",
      " -0.792739   -0.22845053  0.00844572 -0.2428317   0.94208996 -0.67947515\n",
      " -0.25276203 -0.25099637 -0.28218046  0.02616553 -0.5232699  -0.26360952\n",
      " -0.38311684 -0.70090231 -0.47017555 -0.47656455  0.53333385 -0.46393553\n",
      " -0.59159541  0.0044892  -0.55164121 -0.13644785 -0.20097975 -0.47852476\n",
      " -0.53413065 -0.68288774 -0.20030232 -0.70228408 -0.26409072 -0.33937622\n",
      " -0.47229518 -0.46891128 -0.23985111 -0.56199745 -0.20333294 -0.75916104\n",
      " -0.60887733 -0.23177042  0.0266332  -0.34620416 -0.76364498 -0.54786612\n",
      " -0.53421695 -0.09265786 -0.26360952 -0.06195047 -0.59335597 -0.20635492\n",
      " -0.14142846 -0.38782621 -0.20712246 -0.6925555  -0.36638    -0.07858329\n",
      "  0.36552389 -0.71689359 -0.79168849  1.17157503 -0.55164121  0.18508219\n",
      " -0.52346758 -0.14142846 -0.45388516 -0.18523505 -0.21753443 -0.43129525\n",
      " -0.7506897  -0.41256131 -0.70346228 -0.21237352 -0.33905398  0.88042335\n",
      " -0.12935499 -0.23269159 -0.35793123 -0.63804776 -0.15436449 -0.56365262\n",
      "  0.23767125 -0.609611   -0.61097141 -0.25871129 -0.25427743 -0.40229715\n",
      " -0.17441074 -0.3655093   1.20702401 -0.6827149  -0.45440913 -0.59437924\n",
      " -0.01620598 -0.52833367 -0.792739   -0.22922374 -0.18163262 -0.59954857\n",
      " -0.0899773  -0.14408382 -0.63194511 -0.31567531 -0.2047453  -0.01265074\n",
      " -0.792739    1.00517059 -0.26116002  0.14600777 -0.70024022 -0.7463728\n",
      " -0.19713535 -0.5085096  -0.65924266 -0.16156685 -0.3358707  -0.03821102\n",
      " -0.68309055  0.18508219  0.44856647]\n",
      "SGD: [-7.93441645e-01 -4.28399898e-01  1.01402163e+00 -1.83444705e-01\n",
      " -5.55769755e-01  9.23003439e-01 -9.60138114e-02  2.02387606e-01\n",
      " -3.16125945e-02 -6.52601629e-02 -6.47473648e-02 -2.98664283e-01\n",
      " -4.70818057e-03 -2.69032847e-02  1.53711198e-02 -2.78471275e-01\n",
      " -3.71089200e-01 -3.64950670e-01  8.44238128e-01 -1.46027546e-01\n",
      " -4.23789372e-02  2.62579576e-01 -3.19808168e-02  1.79789375e+00\n",
      "  1.61278794e-01 -5.86479242e-01  9.15502618e-01  3.33310727e-02\n",
      " -3.96492312e-02  7.95738156e-01  2.54472134e-01  4.50559154e-01\n",
      " -5.79879665e-01 -1.25423231e-01  4.36372045e-01  1.20847903e-01\n",
      " -2.98664283e-01  8.70349187e-02  1.15987615e-01 -1.66720197e-01\n",
      " -6.04609576e-01  1.01548472e-01 -4.60320321e-01  2.68834516e-01\n",
      " -1.01838335e-01  1.64606121e-01  8.36202153e-01 -4.69790431e-01\n",
      " -1.37324316e-01 -2.62634161e-01 -2.55977654e-01 -5.41129575e-02\n",
      "  6.52619573e-01 -5.02609947e-01  1.40031929e-01 -2.90970418e-02\n",
      "  1.81040469e-01  2.21586912e+00  8.10258783e-02  1.47296741e-01\n",
      "  6.49526048e-02  2.86441604e-01 -1.00323775e-01  7.04810343e-01\n",
      " -2.06991478e-01 -6.59417053e-01  2.23903641e-01  1.37539910e+00\n",
      " -9.79355150e-02 -5.18683220e-01  1.01301912e-01  1.53355609e-01\n",
      " -1.46027546e-01 -8.49090786e-03  4.56736668e-02  8.38252385e-01\n",
      "  2.11834271e-02  1.45977095e-01 -4.71646538e-01  3.83552102e-01\n",
      " -2.16586513e-01  4.10177059e-02  1.99597659e-01 -1.28160358e-01\n",
      "  1.30248214e+00 -2.64378345e-01 -2.92812994e-02  2.29209982e-01\n",
      "  9.09545351e-01  1.13562077e-01  1.99027485e-01  1.54591053e-01\n",
      " -1.84771240e-01  1.54865612e-01 -8.10678594e-02 -2.57017039e-01\n",
      "  2.36814189e-01 -5.72051705e-02 -2.90813080e-01 -5.15325056e-01\n",
      " -6.59614310e-01 -3.60701153e-01 -1.37654417e-01 -1.30201272e-01\n",
      "  1.77210509e+00 -6.45847225e-01 -1.34820871e-01 -2.59656844e-01\n",
      " -9.08350444e-02 -8.23039654e-02 -1.16641277e-01  5.33299740e-02\n",
      "  6.22574037e-01 -5.41129575e-02  1.25211501e-01 -6.65284781e-01\n",
      " -1.04904231e-01  1.85932827e-01 -3.68456221e-01  8.98041785e-02\n",
      " -3.24440724e-01  3.60431077e-01 -4.77916385e-01 -8.16115047e-02\n",
      "  2.34077565e-02 -4.80972249e-01  1.34737349e+00 -4.82320775e-02\n",
      " -3.04939138e-01  4.24304799e-01  8.10258783e-02 -6.61807935e-03\n",
      " -3.28487004e-02  1.38296464e-01 -7.15003545e-01 -3.25528630e-02\n",
      "  1.78726869e-01  1.55991211e+00 -5.64053613e-01  6.69310356e-02\n",
      " -1.57485835e-01 -4.18079603e-01 -3.28487004e-02 -3.79912582e-01\n",
      " -3.82910657e-01 -1.86996230e-01 -7.89572560e-01  9.28610522e-01\n",
      "  1.83538462e-02  5.01804855e-01 -4.76486204e-01 -4.31096665e-02\n",
      "  2.84293263e-01  4.10177059e-02 -6.14411108e-01 -1.47136000e-01\n",
      " -6.74063081e-01  8.59025469e-01 -6.82856998e-01  1.48564081e-01\n",
      "  1.19181466e-01 -1.70860626e-01  1.28946115e+00 -1.62144656e-01\n",
      " -4.06698458e-02  1.98235333e-01  1.40031929e-01 -2.76175719e-01\n",
      " -2.87920982e-01 -4.02096383e-01  3.75586271e-01 -6.19058347e-01\n",
      "  4.42533032e-02  7.16275768e-02 -2.96756456e-01 -3.26008180e-02\n",
      "  2.08543706e-01 -8.08206383e-02 -4.03741248e-01 -5.50316594e-01\n",
      " -7.77463158e-01  9.68569386e-03 -2.55647554e-01 -5.12129911e-01\n",
      " -2.98138607e-01 -4.88020001e-01 -5.47238676e-01  2.14306483e-02\n",
      "  2.26141915e-01 -6.89501250e-02  4.86856447e-01 -4.31463354e-01\n",
      " -1.65275446e-02 -2.81155972e-02 -7.88040856e-02  2.44724605e-01\n",
      " -4.28826800e-01 -4.31096665e-02 -2.23223875e-01 -3.51828727e-01\n",
      " -3.26976954e-01 -2.87997981e-01  6.07094603e-01 -1.44728074e-01\n",
      " -2.59656844e-01  2.72606586e-01 -5.21775433e-01  1.58127882e-01\n",
      "  1.64862246e-02 -3.16266568e-01 -1.88326661e-01 -5.03104389e-01\n",
      "  7.09364013e-02 -4.96056637e-01 -3.38369239e-02 -3.82217850e-01\n",
      " -2.25291174e-01 -1.85109797e-01  5.10523718e-03 -3.34963892e-01\n",
      "  2.44240129e-02 -4.47738056e-01 -2.90101971e-01  8.69648364e-02\n",
      "  5.23191407e-01  8.71580157e-02 -5.78446501e-01 -2.28806765e-01\n",
      " -2.34062574e-01  4.22251959e-01 -4.31096665e-02  1.94007991e-01\n",
      " -2.96408059e-01  3.77511429e-02  1.40031929e-01  4.57493989e-02\n",
      "  8.15121746e-03 -6.35275909e-01 -7.15166620e-02  1.69948568e-01\n",
      "  3.75586271e-01 -4.55131790e-01 -5.55275312e-01  8.67298941e-01\n",
      " -5.21775433e-01  2.67135201e-01 -1.94929670e-01  1.40031929e-01\n",
      " -2.28371499e-01 -2.48120637e-02 -1.11236637e-01 -3.64950670e-01\n",
      " -4.95856466e-01 -3.61226829e-01 -7.44197732e-01 -9.38016986e-02\n",
      " -3.06886354e-01  5.37548804e-01  1.35981403e-01  9.70287480e-02\n",
      " -1.89080361e-02 -3.46852870e-01  6.84755166e-04 -4.41440499e-01\n",
      "  2.77651343e-01 -3.10967845e-01 -3.60701153e-01 -1.94941988e-02\n",
      " -7.47617710e-02 -2.90873545e-01  4.80794024e-02 -2.28909962e-01\n",
      "  7.32961357e-01 -3.30522286e-01 -4.92466868e-01 -2.76729665e-01\n",
      "  1.60630916e-01 -2.18297916e-01 -5.47238676e-01  2.06889848e-02\n",
      " -5.65585963e-02 -3.76527205e-01 -2.03569936e-01 -5.03047254e-03\n",
      " -3.14706323e-01 -1.09018023e-01  3.47844887e-02  2.37727341e-01\n",
      " -5.47238676e-01  2.38037781e+00  4.31886865e-02  5.66848952e-01\n",
      " -4.22243580e-01 -5.51061371e-01 -3.66880780e-03 -2.39574280e-01\n",
      " -5.18930441e-01  1.41731655e-01 -1.33874154e-01  1.58098383e-01\n",
      " -3.64899477e-01  2.67135201e-01  7.40440423e-01]\n",
      "GradientBoosting: [-0.40653817 -0.50008212  0.37254185  0.65050883 -0.61352588  4.01039818\n",
      " -0.29559081  1.47361373 -0.01037915 -0.44805621 -0.07836595 -0.2850284\n",
      " -0.18092169  0.08423084  0.05960106 -0.39404765 -0.6394615  -0.50008212\n",
      "  0.11901596 -0.10878496 -0.35377995  0.3637831   0.52102874  0.98249318\n",
      "  0.07764625 -0.50237529  2.53196469  0.32773609 -0.0747939   0.32439246\n",
      "  0.39660964  0.23648048 -0.5967407   0.2352005   0.19927447  0.78982069\n",
      " -0.2850284   0.14580846  0.55677766 -0.26051569 -0.42806549 -0.13981454\n",
      " -0.35762954  0.26328658  0.00727447  0.1044207   0.31238216 -0.46268268\n",
      "  0.03329935 -0.25669126 -0.48763548 -0.00651147  0.61599165 -0.45550317\n",
      "  0.12529128 -0.13628491  0.1241293   0.58202374  0.16511396  0.12529128\n",
      "  0.15060665  0.25693845 -0.09095819  1.85917667 -0.42041538 -0.24850107\n",
      "  0.64277631  0.19247526 -0.09847029 -0.4290197   0.10073367  0.17050225\n",
      " -0.10878496 -0.12111576  0.07835647  0.69533791 -0.12413326  0.23730472\n",
      " -0.33049145  0.06805346 -0.42041538 -0.16315455  0.24906685 -0.49479243\n",
      "  1.78098682 -0.03322923  0.08535286  1.11953008  0.30600377  0.78340591\n",
      "  0.25325141  0.25929374 -0.13962216  0.24556211  0.08079019 -0.60066035\n",
      "  0.13882752 -0.14678279 -0.16620543 -0.3843905  -0.45434294 -0.42272493\n",
      " -0.16353716 -0.20032319  1.42673044  0.90941793 -0.14790454 -0.47583858\n",
      " -0.04538734 -0.00977177 -0.32919197 -0.06559561  0.58678678 -0.00651147\n",
      "  0.81609815 -0.64905972 -0.22092502 -0.067129   -0.24125451  0.03315286\n",
      " -0.24652275  0.11000413 -0.55489259  2.55127195  0.07147633 -0.45003423\n",
      "  3.33594773 -0.21888891 -0.52880404  0.23395978  0.16511396 -0.21500493\n",
      " -0.12880123  0.17277249 -0.20860666 -0.08792874  0.23131013  0.45992553\n",
      " -0.61352588  0.0208596  -0.1470784  -0.31833954 -0.12880123 -0.46678507\n",
      " -0.43236935 -0.13217133 -0.7191065   0.2931026  -0.04834332  1.94768898\n",
      " -0.21840878 -0.07175115 -0.09758802 -0.16315455 -0.63260535 -0.01839902\n",
      " -0.64285081  0.25580829 -0.15494999  0.07205519  0.20434611 -0.42041538\n",
      " -0.47132457 -0.18157283 -0.20564658  0.24443149  0.12529128 -0.40522364\n",
      "  0.42301697 -0.5232584   0.59571731 -0.64285081  0.22846569  0.07147633\n",
      " -0.51704985 -0.10433058  0.39189806 -0.05188248 -0.50008212 -0.453303\n",
      "  0.05317287 -0.02198806 -0.29975658 -0.38805486 -0.52880404 -0.45003423\n",
      " -0.62278226 -0.12413326  0.10840122 -0.04000395  1.08549154 -0.3212396\n",
      " -0.12111576 -0.22143687 -0.18094116  0.18825494 -0.41740639 -0.07175115\n",
      " -0.02684268 -0.46638698 -0.27108242  0.09443841  0.59789425 -0.49479243\n",
      " -0.47583858  0.39328969 -0.39328542  0.26070224  0.06301948 -0.50008212\n",
      " -0.34141222 -0.43831574  0.1081845  -0.52506587 -0.09392833 -0.10413684\n",
      " -0.51238695  0.10382388 -0.09107463 -0.24836855 -0.14055152 -0.53535369\n",
      " -0.52880404 -0.08406088  0.16258635  0.14924196 -0.57456055 -0.42533685\n",
      " -0.46250872  0.11230279 -0.07175115  0.1368412  -0.45292985 -0.12413326\n",
      "  0.12529128  0.26818566 -0.15445317 -0.69797676 -0.25398365  0.29081283\n",
      "  0.59571731 -0.59355648 -0.62278226  1.41705222 -0.39328542  0.23677428\n",
      " -0.43875659  0.12529128 -0.31788762 -0.01611998 -0.08749804 -0.50008212\n",
      " -0.58101331 -0.33251924 -0.66123815  0.00727447 -0.30279968  1.2311094\n",
      "  0.10820799 -0.07397529  0.02668367 -0.51538429  0.18598061 -0.52701319\n",
      "  0.43060544 -0.52880404 -0.42272493 -0.0906848  -0.00977177  0.13652935\n",
      " -0.02435137 -0.1698248   2.33596505 -0.47770375 -0.46379317 -0.34097928\n",
      "  0.06934824 -0.43573909 -0.62278226 -0.10410455 -0.2201803  -0.37882702\n",
      " -0.13277804  0.21597472 -0.52880404 -0.35133005 -0.03767804  0.27780902\n",
      " -0.62278226  0.68586531  0.27627742  0.3936859  -0.61623261 -0.43842776\n",
      "  0.10171177 -0.26627    -0.22161871  0.17157377 -0.35377995  0.15913514\n",
      " -0.50760488  0.23677428  0.4369007 ]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\abrao\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\abrao\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\abrao\\anaconda3\\Lib\\site-packages\\sklearn\\ensemble\\_gb.py:437: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "for i in models:\n",
    "    model = i[1].fit(x_train, y_train)\n",
    "    print(f'{i[0]}: {model.predict(x_test)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "1de9e599-3dcb-4041-b86c-7a63c20db4ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\abrao\\AppData\\Local\\Temp\\ipykernel_312\\4286903934.py:5: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  model = i[1].fit(x_train, y_train)\n",
      "C:\\Users\\abrao\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\abrao\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\abrao\\anaconda3\\Lib\\site-packages\\sklearn\\ensemble\\_gb.py:437: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "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>Model</th>\n",
       "      <th>MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNeighbors</td>\n",
       "      <td>0.926872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.975797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.873909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SGD</td>\n",
       "      <td>0.915547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GradientBoosting</td>\n",
       "      <td>0.844686</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Model       MSE\n",
       "0        KNeighbors  0.926872\n",
       "1     Random Forest  0.975797\n",
       "2               SVM  0.873909\n",
       "3               SGD  0.915547\n",
       "4  GradientBoosting  0.844686"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_df = []\n",
    "trained_models = []\n",
    "\n",
    "for i in models:\n",
    "    model = i[1].fit(x_train, y_train)\n",
    "    y_pred = model.predict(x_test)\n",
    "    trained_models.append(model)\n",
    "    to_df.append([i[0], mean_squared_error(y_test, y_pred)])\n",
    "\n",
    "evaluate = pd.DataFrame(to_df, columns=['Model', 'MSE'])\n",
    "evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "df137625-c82e-4d34-8870-23962ae17b56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "old score: 0.9268719962409276, new score: 0.8612605376785506\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "\n",
    "param_grid = {'n_neighbors': range(1, 10), 'weights': ['uniform', 'distance']}\n",
    "\n",
    "grid_search = GridSearchCV(estimator=trained_models[0], param_grid=param_grid, cv=5)\n",
    "model = grid_search.fit(x_train, y_train)\n",
    "new_score = model.predict(x_test)\n",
    "print(f'old score: {evaluate.iloc[0, 1]}, new score: {mean_squared_error(y_test, new_score)}')"
   ]
  }
 ],
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