Linear-regression-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bdd8c3bf-a3c6-4268-b4ca-ba174e1dcb78",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sir wasif's lab\n",
    "#importing all libraries required\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5af9ab56-7c43-4dd4-990d-12a92f4dbf07",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Advertising.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c017e01a-8f44-44b5-af70-cf8df88fd831",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  radio  newspaper  sales\n",
       "0    230.1   37.8       69.2   22.1\n",
       "1     44.5   39.3       45.1   10.4\n",
       "2     17.2   45.9       69.3    9.3\n",
       "3    151.5   41.3       58.5   18.5\n",
       "4    180.8   10.8       58.4   12.9\n",
       "..     ...    ...        ...    ...\n",
       "195   38.2    3.7       13.8    7.6\n",
       "196   94.2    4.9        8.1    9.7\n",
       "197  177.0    9.3        6.4   12.8\n",
       "198  283.6   42.0       66.2   25.5\n",
       "199  232.1    8.6        8.7   13.4\n",
       "\n",
       "[200 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4d544ceb-bdc5-4b8e-8668-40c3fb69056f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TV           0\n",
       "radio        0\n",
       "newspaper    0\n",
       "sales        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "202fba28-5a95-452b-a364-0c661c75dc3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   TV         200 non-null    float64\n",
      " 1   radio      200 non-null    float64\n",
      " 2   newspaper  200 non-null    float64\n",
      " 3   sales      200 non-null    float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 6.4 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4982576b-324b-474d-8cf8-e058432a25ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>147.042500</td>\n",
       "      <td>23.264000</td>\n",
       "      <td>30.554000</td>\n",
       "      <td>14.022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>85.854236</td>\n",
       "      <td>14.846809</td>\n",
       "      <td>21.778621</td>\n",
       "      <td>5.217457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>74.375000</td>\n",
       "      <td>9.975000</td>\n",
       "      <td>12.750000</td>\n",
       "      <td>10.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>149.750000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>25.750000</td>\n",
       "      <td>12.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>218.825000</td>\n",
       "      <td>36.525000</td>\n",
       "      <td>45.100000</td>\n",
       "      <td>17.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>296.400000</td>\n",
       "      <td>49.600000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TV       radio   newspaper       sales\n",
       "count  200.000000  200.000000  200.000000  200.000000\n",
       "mean   147.042500   23.264000   30.554000   14.022500\n",
       "std     85.854236   14.846809   21.778621    5.217457\n",
       "min      0.700000    0.000000    0.300000    1.600000\n",
       "25%     74.375000    9.975000   12.750000   10.375000\n",
       "50%    149.750000   22.900000   25.750000   12.900000\n",
       "75%    218.825000   36.525000   45.100000   17.400000\n",
       "max    296.400000   49.600000  114.000000   27.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6e23fa10-f4c9-4f09-951f-f8a62cf9496f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 4)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "11545ac9-ff9c-4b5a-bf9c-f14aa33c8646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#univariate analysis\n",
    "plt.boxplot(data=df, x=df[\"TV\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "56bc95f6-3681-415c-9b38-b620f25dcf54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate analysis\n",
    "#univariate analysis\n",
    "plt.boxplot(data=df, x=df[\"radio\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1726725c-842c-45ee-a4db-1e4518d755c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#univariate analysis\n",
    "plt.boxplot(data=df, x=df[\"newspaper\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0350745-eb51-4ffc-b03c-9af6c543b2fd",
   "metadata": {},
   "source": [
    "BIVARIATE ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e652e866-3ccc-4387-af12-58e81e9018e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import seaborn as sns\n",
    "# sns.pairplot(df, x_vars=[\"TV\", \"radio\", \"newspaper\"], y_vars=[\"sales\"], height=4, aspect=1)\n",
    "# plt.show()\n",
    "\n",
    "plt.scatter(x=df[\"TV\"], y=df[\"sales\"])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2ad38b4e-34c7-4424-9d14-acb667e4289c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=df[\"radio\"], y=df[\"sales\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "21ec67d0-865b-4988-809e-b9deaefb4202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=df[\"newspaper\"], y=df[\"sales\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c13091a0-709a-46c4-bbe7-de26d628e5f5",
   "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>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  radio  newspaper\n",
       "0    230.1   37.8       69.2\n",
       "1     44.5   39.3       45.1\n",
       "2     17.2   45.9       69.3\n",
       "3    151.5   41.3       58.5\n",
       "4    180.8   10.8       58.4\n",
       "..     ...    ...        ...\n",
       "195   38.2    3.7       13.8\n",
       "196   94.2    4.9        8.1\n",
       "197  177.0    9.3        6.4\n",
       "198  283.6   42.0       66.2\n",
       "199  232.1    8.6        8.7\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x= df[[\"TV\", \"radio\", \"newspaper\"]]\n",
    "x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5ff90560-c38f-45d8-95c3-4605493c7f22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.1\n",
       "1      10.4\n",
       "2       9.3\n",
       "3      18.5\n",
       "4      12.9\n",
       "       ... \n",
       "195     7.6\n",
       "196     9.7\n",
       "197    12.8\n",
       "198    25.5\n",
       "199    13.4\n",
       "Name: sales, Length: 200, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y= df[\"sales\"]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1fd9d3d7-d248-49e7-be09-6cbd272cbabc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d775c283-0dfb-473b-b66c-9f0991dcbafc",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x,y, test_size=0.25, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "610f469a-e963-47ed-a47a-f90ee45c3bce",
   "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>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>78.2</td>\n",
       "      <td>46.8</td>\n",
       "      <td>34.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>168.4</td>\n",
       "      <td>7.1</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.7</td>\n",
       "      <td>48.9</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>7.8</td>\n",
       "      <td>38.9</td>\n",
       "      <td>50.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>76.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>25.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>204.1</td>\n",
       "      <td>32.9</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>217.7</td>\n",
       "      <td>33.5</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>165.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>280.2</td>\n",
       "      <td>10.1</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  radio  newspaper\n",
       "114   78.2   46.8       34.5\n",
       "173  168.4    7.1       12.8\n",
       "5      8.7   48.9       75.0\n",
       "126    7.8   38.9       50.6\n",
       "117   76.4    0.8       14.8\n",
       "..     ...    ...        ...\n",
       "106   25.0   11.0       29.7\n",
       "14   204.1   32.9       46.0\n",
       "92   217.7   33.5       59.0\n",
       "179  165.6   10.0       17.6\n",
       "102  280.2   10.1       21.4\n",
       "\n",
       "[150 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b80bbaa5-ae6a-40a0-acd1-762ebc90dd87",
   "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>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>14.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>19.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sales\n",
       "114   14.6\n",
       "173   11.7\n",
       "5      7.2\n",
       "126    6.6\n",
       "117    9.4\n",
       "..     ...\n",
       "106    7.2\n",
       "14    19.0\n",
       "92    19.4\n",
       "179   12.6\n",
       "102   14.8\n",
       "\n",
       "[150 rows x 1 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "71343432-9686-44c6-9d5f-6f9c59a68b23",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6aab08fd-3511-40a3-aa9a-03edbf357904",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "402c59dd-a418-4e07-884f-da7d435f280e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "12ce280e-e59e-413f-b36a-3129d560695c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04543356, 0.19145654, 0.00256809])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "model.coef_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9a5b0471-4645-44e7-b507-f47a34aedc26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16.38348211],\n",
       "       [20.92434957],\n",
       "       [21.61495426],\n",
       "       [10.49069997],\n",
       "       [22.17690456],\n",
       "       [13.02668085],\n",
       "       [21.10309295],\n",
       "       [ 7.31813008],\n",
       "       [13.56732111],\n",
       "       [15.12238649],\n",
       "       [ 8.92494113],\n",
       "       [ 6.49924401],\n",
       "       [14.30119928],\n",
       "       [ 8.77233515],\n",
       "       [ 9.58665483],\n",
       "       [12.09485291],\n",
       "       [ 8.59621605],\n",
       "       [16.25337881],\n",
       "       [10.16948105],\n",
       "       [18.85753401],\n",
       "       [19.5799036 ],\n",
       "       [13.15877029],\n",
       "       [12.25103735],\n",
       "       [21.35141984],\n",
       "       [ 7.69607607],\n",
       "       [ 5.64686906],\n",
       "       [20.79780073],\n",
       "       [11.90951247],\n",
       "       [ 9.06581044],\n",
       "       [ 8.37295611],\n",
       "       [12.40815899],\n",
       "       [ 9.89416076],\n",
       "       [21.42707658],\n",
       "       [12.14236853],\n",
       "       [18.28776857],\n",
       "       [20.18114718],\n",
       "       [13.99303029],\n",
       "       [20.89987736],\n",
       "       [10.9313953 ],\n",
       "       [ 4.38721626],\n",
       "       [ 9.58213448],\n",
       "       [12.6170249 ],\n",
       "       [ 9.93851933],\n",
       "       [ 8.06816257],\n",
       "       [13.45497849],\n",
       "       [ 5.25769423],\n",
       "       [ 9.15399537],\n",
       "       [14.09552838],\n",
       "       [ 8.71029827],\n",
       "       [11.55102817]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fdd85192-e8c8-4773-ab48-d2035146c113",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>16.9</td>\n",
       "      <td>16.383482</td>\n",
       "      <td>0.516518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>22.4</td>\n",
       "      <td>20.924350</td>\n",
       "      <td>1.475650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>21.4</td>\n",
       "      <td>21.614954</td>\n",
       "      <td>-0.214954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>7.3</td>\n",
       "      <td>10.490700</td>\n",
       "      <td>-3.190700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>24.7</td>\n",
       "      <td>22.176905</td>\n",
       "      <td>2.523095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>12.6</td>\n",
       "      <td>13.026681</td>\n",
       "      <td>-0.426681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>22.3</td>\n",
       "      <td>21.103093</td>\n",
       "      <td>1.196907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>8.4</td>\n",
       "      <td>7.318130</td>\n",
       "      <td>1.081870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>11.5</td>\n",
       "      <td>13.567321</td>\n",
       "      <td>-2.067321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>14.9</td>\n",
       "      <td>15.122386</td>\n",
       "      <td>-0.222386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>9.5</td>\n",
       "      <td>8.924941</td>\n",
       "      <td>0.575059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>8.7</td>\n",
       "      <td>6.499244</td>\n",
       "      <td>2.200756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>11.9</td>\n",
       "      <td>14.301199</td>\n",
       "      <td>-2.401199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>5.3</td>\n",
       "      <td>8.772335</td>\n",
       "      <td>-3.472335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>10.3</td>\n",
       "      <td>9.586655</td>\n",
       "      <td>0.713345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>11.7</td>\n",
       "      <td>12.094853</td>\n",
       "      <td>-0.394853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>5.5</td>\n",
       "      <td>8.596216</td>\n",
       "      <td>-3.096216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>16.6</td>\n",
       "      <td>16.253379</td>\n",
       "      <td>0.346621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>11.3</td>\n",
       "      <td>10.169481</td>\n",
       "      <td>1.130519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>18.9</td>\n",
       "      <td>18.857534</td>\n",
       "      <td>0.042466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>19.7</td>\n",
       "      <td>19.579904</td>\n",
       "      <td>0.120096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>12.5</td>\n",
       "      <td>13.158770</td>\n",
       "      <td>-0.658770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>10.9</td>\n",
       "      <td>12.251037</td>\n",
       "      <td>-1.351037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>22.2</td>\n",
       "      <td>21.351420</td>\n",
       "      <td>0.848580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>9.3</td>\n",
       "      <td>7.696076</td>\n",
       "      <td>1.603924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>8.1</td>\n",
       "      <td>5.646869</td>\n",
       "      <td>2.453131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>21.7</td>\n",
       "      <td>20.797801</td>\n",
       "      <td>0.902199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>13.4</td>\n",
       "      <td>11.909512</td>\n",
       "      <td>1.490488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>10.6</td>\n",
       "      <td>9.065810</td>\n",
       "      <td>1.534190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>5.7</td>\n",
       "      <td>8.372956</td>\n",
       "      <td>-2.672956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.6</td>\n",
       "      <td>12.408159</td>\n",
       "      <td>-1.808159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11.3</td>\n",
       "      <td>9.894161</td>\n",
       "      <td>1.405839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>23.7</td>\n",
       "      <td>21.427077</td>\n",
       "      <td>2.272923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>8.7</td>\n",
       "      <td>12.142369</td>\n",
       "      <td>-3.442369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>16.1</td>\n",
       "      <td>18.287769</td>\n",
       "      <td>-2.187769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>20.7</td>\n",
       "      <td>20.181147</td>\n",
       "      <td>0.518853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>11.6</td>\n",
       "      <td>13.993030</td>\n",
       "      <td>-2.393030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>20.8</td>\n",
       "      <td>20.899877</td>\n",
       "      <td>-0.099877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>11.9</td>\n",
       "      <td>10.931395</td>\n",
       "      <td>0.968605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>6.9</td>\n",
       "      <td>4.387216</td>\n",
       "      <td>2.512784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>11.0</td>\n",
       "      <td>9.582134</td>\n",
       "      <td>1.417866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>12.8</td>\n",
       "      <td>12.617025</td>\n",
       "      <td>0.182975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>10.1</td>\n",
       "      <td>9.938519</td>\n",
       "      <td>0.161481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>9.7</td>\n",
       "      <td>8.068163</td>\n",
       "      <td>1.631837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>11.6</td>\n",
       "      <td>13.454978</td>\n",
       "      <td>-1.854978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>7.6</td>\n",
       "      <td>5.257694</td>\n",
       "      <td>2.342306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>9.153995</td>\n",
       "      <td>1.346005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>14.6</td>\n",
       "      <td>14.095528</td>\n",
       "      <td>0.504472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>10.4</td>\n",
       "      <td>8.710298</td>\n",
       "      <td>1.689702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>12.0</td>\n",
       "      <td>11.551028</td>\n",
       "      <td>0.448972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual  Predicted  Difference\n",
       "95     16.9  16.383482    0.516518\n",
       "15     22.4  20.924350    1.475650\n",
       "30     21.4  21.614954   -0.214954\n",
       "158     7.3  10.490700   -3.190700\n",
       "128    24.7  22.176905    2.523095\n",
       "115    12.6  13.026681   -0.426681\n",
       "69     22.3  21.103093    1.196907\n",
       "170     8.4   7.318130    1.081870\n",
       "174    11.5  13.567321   -2.067321\n",
       "45     14.9  15.122386   -0.222386\n",
       "66      9.5   8.924941    0.575059\n",
       "182     8.7   6.499244    2.200756\n",
       "165    11.9  14.301199   -2.401199\n",
       "78      5.3   8.772335   -3.472335\n",
       "186    10.3   9.586655    0.713345\n",
       "177    11.7  12.094853   -0.394853\n",
       "56      5.5   8.596216   -3.096216\n",
       "152    16.6  16.253379    0.346621\n",
       "82     11.3  10.169481    1.130519\n",
       "68     18.9  18.857534    0.042466\n",
       "124    19.7  19.579904    0.120096\n",
       "16     12.5  13.158770   -0.658770\n",
       "148    10.9  12.251037   -1.351037\n",
       "93     22.2  21.351420    0.848580\n",
       "65      9.3   7.696076    1.603924\n",
       "60      8.1   5.646869    2.453131\n",
       "84     21.7  20.797801    0.902199\n",
       "67     13.4  11.909512    1.490488\n",
       "125    10.6   9.065810    1.534190\n",
       "132     5.7   8.372956   -2.672956\n",
       "9      10.6  12.408159   -1.808159\n",
       "18     11.3   9.894161    1.405839\n",
       "55     23.7  21.427077    2.272923\n",
       "75      8.7  12.142369   -3.442369\n",
       "150    16.1  18.287769   -2.187769\n",
       "104    20.7  20.181147    0.518853\n",
       "135    11.6  13.993030   -2.393030\n",
       "137    20.8  20.899877   -0.099877\n",
       "164    11.9  10.931395    0.968605\n",
       "76      6.9   4.387216    2.512784\n",
       "79     11.0   9.582134    1.417866\n",
       "197    12.8  12.617025    0.182975\n",
       "38     10.1   9.938519    0.161481\n",
       "24      9.7   8.068163    1.631837\n",
       "122    11.6  13.454978   -1.854978\n",
       "195     7.6   5.257694    2.342306\n",
       "29     10.5   9.153995    1.346005\n",
       "19     14.6  14.095528    0.504472\n",
       "143    10.4   8.710298    1.689702\n",
       "86     12.0  11.551028    0.448972"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.DataFrame({\"Actual\": y_test,\"Predicted\": predictions, \"Difference\": y_test - predictions})\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c05256b4-65eb-4063-9053-8638185d6006",
   "metadata": {},
   "outputs": [
    {
     "data": {
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pnNqyM336dKxZswabNm2Cn5+ffgS3QqGAl5cXtFotxo4di8OHD+OXX36BTqfTlwkMDIRUKgUATJ48GS1atNCvE/D2228jMTERsbGxUCqVWLJkCbKzs/H5558750YJIYQ0KDxQihvKmmYbnqubrKjUNd1bjWEMuFDYeDl3RrueW8apyc6yZcsAAAMGDDA4v3LlSkydOhWXL1/G5s2bAQDdunUzKLN79259vby8PPB3ZaslJSV47rnnUFhYCIVCgYSEBOzZswf33Xef3e6FEEKIZfy8RGjZXIrLN9SQigTwnHC7hafmg1kwo7FGq7NPjMS9OTXZYY2k6q1atWq0DACkpaUZPP7444/x8ccfWxMaIYQQB4oKlkEm4ZB/XQ1fqQZKlVT/nMTETyoOQDNf+8TnKlxh6rk7cokByoQQQkhogBQhCglKK3U4ls9wrbSmZUcqAeRSoFptvD4DkNCmaXfTcLx1XVGcZ+Y6lOwQQghxrkoVw/E8oOBWTZdVsL8InaOBVsHAuUKGm+VAoAK4er3ha3AcEOgHxLVwXNzEfVCyQwghxGmyLjCkH68ZXFw7aOFiEfDXWaBHG2BARw46oWadndxChl8O1szOqi3LcTV1m/sD4+7nIBY19ZYdGqBsCUp2CCGEOMWxSwy7j9U9XztU89D5mmQmuSMHsQhoH8khMpjh6EXg7BUGjQ5Q+ABdYzi0CYNnbBLK8zWHNfU9ECU7hBBCHE6rY9hzovFyh84BCa0Z/LxqEhkfOYek9k1/pWRiW56Z4hFCCHGq84WAStN4OXDA8bzGi3kKjuOsPjwRtewQQghxuBtKw20iGsSAm8rGr6cs1+KPg6W4UayBt1yE3t38EBUht0msroSmnluGkh1CCCEOJ+LvDDJujLHPZ62W4esfCvDLrlvQ6Rh4EcAEYOWPhejawQevPROJoGYSm8TsCmiAsmXMTvG2bdtmsDP5559/jm7duuGJJ55AcXGxTYMjhBDSNEUEmrgFBICWDWyJyBjDoi/zsWnnTWh1DAyATnentehYTgVS3j+PEmUjO4iSJs/sZOef//wnlMqaNsVjx47h1VdfxYgRI3DhwgWkpKTYPEBCCCFNT2RzIMCndkOIholFQPuW9T+XeawMew6WNpg0CQJws0SD1ZuuWRWrS+H4OzOyLDk8dFVBs+86NzcX8fHxAIDU1FQ8/PDD+OCDD7B06VL8+uuvNg+QEEJI08NxHIZ1v70isJFyQ7oBUnH9JX75/WajM6kFAdj5ZzGqqpvIplm3u7EsPUDdWKaRSqWorKwEAOzcuRNDhgwBULMLeW2LDyGEENKYiEAO4/rWrHwM1CQ9tZOF/LyAR3oB7Vs2/OF84lwlBKHx11GpGS5dUVkfMHFbZg9Qvv/++5GSkoK+ffvir7/+wvr16wEAZ86cQcuWDbQ1EkIIIfWICOQweSBDQTFw9dad1ZCjQwC+kWnSzIRER1/WlAFCboDjeHBWdEVZU9edmX3Xn332GcRiMX788UcsW7YMLVrUbETy66+/YtiwYTYPkBBCSNPGcRwiAjn0bMuhVyyHmFCu0UQHAGIi5Sb1yoh4oEWYzAaRuoDarihrDg9kdstOVFQUfvnllzrnP/74Y5sERAghhJjioQeCcPJcpdEyPA/066WAvy+ttOLJLGrPOn/+PN566y1MmDABRUVFAGqmpJ84YcLa34QQQogN3N/TH+3beDU4SJnnAbmUxxOjQh0bmB3VLipozeGJzL7r9PR0dO7cGQcOHMCGDRtQXl4OADh69Cjmzp1r8wAJIYR4DsYYbharUXRDBY3G+KAciZjHuzNj0C3eF0BNdxXH1fwJAEEBEix8ozVaNpUuLMCqmVjWLkjozsxu13vjjTfw3nvvISUlBX5+fvrzAwcOxCeffGLT4AghhHgGtUbA5u3XkLq1AAXXamZOecl5PDQoFONGhiMkqP6ExcdbhPdSYnA+rwq//1mMG8UaeMlFSErwR68ufhCJPPPDnRgyO9k5duwY1qxZU+d8cHAwbt68aZOgCCGEuC+NluFkrgZllQK8ZDziYyTwkjWcdFSrdJj13ikcO11msEBgVbWADb8WYHv6dXzybkfERHo3eI02UV5oE+Vly9twTRxn3cKAtBGoaQICAlBQUICYmBiD81lZWfqZWYQQQjyPwBi2ZVRjx4FqVKruZC0SMdCvmwxjBnhDUs8CgZ+vuojj9yQ6+msKQHmlFq+/dwprPk+AWOyZY05q0d5YljH7f80TTzyB119/HYWFheA4DoIg4M8//8Rrr72GyZMn2yNGQgghLo4xhm+3VmDTniqDRAcANFpg9yEVlqwvg0Zr+FxpmQa/7rpudPdzQQCKbqqx9yDtv2jVVhG1hwcy+67ff/99REVFoUWLFigvL0d8fDySk5PRp08fvPXWW/aIkRBCiIs7ek6DjGPqBp9nDDibr8XuQ9UG5/dlFkOra3zBP54Hdv95w+o4iWcyuxtLIpFg9erVeOedd5CVlQVBEJCQkIDY2Fh7xEcIIcQN7MqsBs/BaAsNQ00Lz4P3yfWLBpaVa8HzaHTbB0EASsto93KO48BZMe7GmrruzOJVltq0aYM2bdrYMhZCCCFuiDGGnDxtg7uP3+2WUsDNUgHBASIAQIC/xKT9rXgeCAyQWBlpE8BZ2RXlodtFmJ3sPPXUU0af//rrry0OhhBCiPsRGExKdGrp7tqAvE/PZpBKOKg1xi8gCMCg+5tbGCHxdGYnO8XFhgPENBoNjh8/jpKSEjzwwAM2C4wQQoh7EPEcAvw4lJQ1nvGIRECA753WBV8fMUYOCUPq1oIGEyaeB8JD5Ejs3sxWIbstmo1lGbOTnZ9++qnOOUEQ8I9//AOtW7e2SVCEEELcS/8EOTb/UWW0hYfngN7xUsjvWXPn+SejcLmgCvsPl4DjDFuJeA4IDJDiw7c60AKBQE03lFXr7HhmN5ZN7prnecycOZM2AyWEEA+VnCCDvw/X4KbaHAeIxcDQxLoL/0kkPN57vT3eeLEN2rby0Z9vppBgyriW+GpRF7QIk9srdOIBbLYN7Pnz56HV0kh5QgjxRL5ePF59wh+frC/DzVJB30LDoWYWlpeMw4tjfREWJKq3vljEYdiAEAwbEAKVWoBOx+Al5z129lCDeA4NZpSm1vdAZic7KSkpBo8ZYygoKMCWLVswZcoUmwVGCCHEvYQGivDOcwpknVFj/3E1SsoE+Mg59IyX4r54GeRS0z5oZVLP7GoxBcfx4KzoirKmrjszO9nJysoyeMzzPIKDg/Gf//yn0ZlahBBCmjaxiEOvDjL06tB0dhon7s/sZGf37t32iIMQQgghjaFuLIvYbMwOIYQQQuyL43lwViwqaE1dd2bSXSckJKB79+4mHeaYP38+evXqBT8/P4SEhGD06NHIyckxKMMYw7x58xAREQEvLy8MGDAAJ06caPTaqampiI+Ph0wmQ3x8fL1T5gkhhBC3wnHWHx7IpJad0aNH2+XF09PTMX36dPTq1QtarRb/+te/MGTIEJw8eRI+PjXTDz/88EN89NFHWLVqFdq1a4f33nsPgwcPRk5ODvz8/Oq9bkZGBsaPH493330Xjz76KH766SeMGzcOe/fuRe/eve1yL4QQQghxTRxj5izybV/Xr19HSEgI0tPTkZycDMYYIiIi8Morr+D1118HAKhUKoSGhmLhwoV4/vnn673O+PHjoVQq8euvv+rPDRs2DM2aNcPatWvrlFepVFCpVPrHSqUSkZGRKC0thb+/v43vkhBC3I9aI6BaJcDHS0SL+91DqVRCoVDY9TOj9jUKlr4Bfy/L1xxSVlUj/B8LPO7zzaU670pLSwEAgYGBAIDc3FwUFhZiyJAh+jIymQz9+/fHvn37GrxORkaGQR0AGDp0aIN15s+fD4VCoT8iIyOtvRVCCGkSMo+W4vUPTmPYxIMY9dQhjJh8EP+3/AIuXq50dmieibqxLGJ2sqPT6bBo0SLcd999CAsLQ2BgoMFhKcYYUlJScP/996NTp04AgMLCQgBAaGioQdnQ0FD9c/UpLCw0q87s2bNRWlqqP/Lz8y2+D0IIaSq++eEy/vneaWQeLUVtF4Baw/Bb2nU8O+s4Mg4XG61PiKswO9l5++238dFHH2HcuHEoLS1FSkoKxowZA57nMW/ePIsDefHFF3H06NF6u5nuXUGTMdboqprm1JHJZPD39zc4CCHEk6Xvv4lVP1wBULPj+N10AqDTMsxddBZXr1U7ITrPVTsby5rDE5l916tXr8aXX36J1157DWKxGBMmTMBXX32Ff//739i/f79FQbz00kvYvHkzdu/ejZYtW+rPh4WFAUCdFpmioqI6LTd3CwsLM7sOIYSQO9ZsLDDa48EA6ASGzduLHBYTwZ2NQK05zGDKrOl7paWlgeO4Osfp06etuXOrmJ3sFBYWonPnzgAAX19f/Tibhx9+GFu2bDHrWowxvPjii9iwYQN27dqFmJgYg+djYmIQFhaGHTt26M+p1Wqkp6ejT58+DV43KSnJoA4AbN++3WgdQgghNQqKqnHmQoXRHcyBmhaf7XuuOyYo4hS1s6b379+PHTt2QKvVYsiQIaioqGi0bk5ODgoKCvRHbGysAyKun9mLCrZs2RIFBQWIiopC27ZtsX37dnTv3h0HDx6ETGbe8uDTp0/HmjVrsGnTJvj5+elbYxQKBby8vMBxHF555RV88MEHiI2NRWxsLD744AN4e3vjiSee0F9n8uTJaNGiBebPnw8AePnll5GcnIyFCxdi1KhR2LRpE3bu3Im9e/eae7uEEOJxSpSmb+qsLNfZMRJSB2flCspmDlDetm2bweOVK1ciJCQEhw4dQnJystG6ISEhCAgIMDdCuzA72Xn00Ufx+++/o3fv3nj55ZcxYcIErFixAnl5eZg5c6ZZ11q2bBkAYMCAAQbnV65cialTpwIAZs2ahaqqKvzjH/9AcXExevfuje3btxussZOXlwf+rn7IPn36YN26dXjrrbcwZ84ctGnTBuvXr6c1dgghxAT+vqZ/NPj61L+LObEPW20EqlQqDc7LZDKTGizunTVtTEJCAqqrqxEfH4+33noLAwcOtCBi27B6nZ0DBw7gzz//RNu2bTFy5EhbxeVUjlgzgRBCXBVjDM/MOobcvCqjXVkiHnh0WBimT412XHAuyJHr7Fz7ah78va1YZ6eyGqHPzKtzfu7cuY1OMmKMYdSoUSguLsYff/zRYLmcnBzs2bMHPXr0gEqlwnfffYfly5cjLS2t0dYgezG7ZaeyshLe3t76x71796YWE0IIaUI4jsOEURF4f8n5xgpi1FCa+OFQNtoIND8/3yAxM6VVp3bWdGNDQuLi4hAXF6d/nJSUhPz8fCxatMhpyY7ZbWEhISF48skn8dtvv0G4dz4iIYSQJmFQ3yA8PjIcAHDvbGURX3PMebktWoZb3spALGCj2Vj3LrfSWLLT0KxpUyUmJuLs2bMW3bItmJ3sfPvtt1CpVHj00UcRERGBl19+GQcPHrRHbIQQQpyE4zg8/2QU3v1nLDq3vzNGUiTiMKBPEJZ+0An9Ey1fSJZYyMErKDc2a9pUWVlZCA8Pt6iuLZjdjTVmzBiMGTMGZWVl+PHHH7F27Vr06dMHMTExePLJJ/Hvf//bHnESQghxgvt7BeL+XoEor9SiqkoHP18x5DIalOwpGps1DdTsQnDlyhV8++23AIDFixejVatW6NixI9RqNb7//nukpqYiNTXVafdh8ZBuPz8/TJs2Ddu3b8eRI0fg4+ODt99+25axEUIIcRG+3mIEB8ko0XE2nrf+MMOyZctQWlqKAQMGIDw8XH+sX79eX6agoAB5eXn6x2q1Gq+99hq6dOmCfv36Ye/evdiyZQvGjBljs38Gc5ndslOruroamzdvxpo1a7Bt2zaEhITgtddes2VshBBCbOjGLRWKbqggk/KIjvSBmHYvdz8WrIJcp74ZTJmwvWrVKoPHs2bNwqxZs8x6HXszO9nZvn07Vq9ejY0bN0IkEmHs2LH47bff0L9/f3vERwghxErHT5di1bo87D90S38uqJkUYx9pgcdHt4RE4pn7JRHPYXayM3r0aDz00EP45ptv8NBDD0EikdgjLkIIITawJ+MG5iw4CQbDb+g3i9X44rtcHMwuxqJ5nSGlhMc92GjquacxO9kpLCykhfYIIcQN3Lilwtz/OwmBsXoXB2QMyDpWgpVrL+L5ya0dHyAxH8dZ2Y3lmcmO2f9ilOgQQoh7+Pm3Aui09Sc6tRgDNmy5CpWK9rgiTRe1WxJCSBO1+88bEEzYEKiiUoejJ0vtHxCxnoPX2WkqLJ6NRQghxLVVVJq+e3lFJbXsuAULpo/Xqe+BPPOuCSHEA4Q0l5n8Rb55kNS+wRDiRJTsEEJIE/XQg2FGx+vUigiVI74djcd0C9SNZRGTurHMWfVww4YNFgdDCCHEdh5MDsGKNRdxq1gNnZF9m6c+Hg3eQ6ckux0HLyrYVJiU7CgUCnvHQQghxMbkchEWv9sVM/51BLdK1AatPCIe0AnAtAnRGPFgmPOCJObhrByzQ8lOw1auXGnvOAghhNhBdKQ3vlvaE1t2FGLTr1dx7boKUimPPr2C8NjDEejUnr7MkqaPZmMRQkgT5+8rwYRHIzHh0Uhnh0KsZe24GxqzY7off/wR//vf/5CXlwe1Wm3w3OHDh20SGCGEEELuQWN2LGL2XS9ZsgTTpk1DSEgIsrKycN999yEoKAgXLlzA8OHD7REjIYQQQojFzE52li5dii+++AKfffYZpFIpZs2ahR07dmDGjBkoLaUVOAkhhBC7oannFjE72cnLy0OfPn0AAF5eXigrKwMATJo0CWvXrrVtdIQQQgi5o3YFZWsOD2T2XYeFheHmzZsAgOjoaOzfvx8AkJubC2bK6lWEEEIIIQ5kdrLzwAMP4OeffwYAPP3005g5cyYGDx6M8ePH49FHH7V5gIQQQgipwTjO6sMTmT0b64svvoAg1CzF+cILLyAwMBB79+7FI488ghdeeMHmARJCCCHkNo6zcjYWJTsm4Xke/F19fuPGjcO4ceNsGhQhhBBCiK2Ynezs2bPH6PPJyckWB0MIIYQQI2idHYuYnewMGDCgzjnurmYxnU5nVUCEEEIIqZ+14248dcyO2SlecXGxwVFUVIRt27ahV69e2L59uz1iJIQQQghwp2XHmsMDmd2yU98O6IMHD4ZMJsPMmTNx6NAhmwRGCCGEEGILNtsINDg4GDk5Oba6HCGEEELuRRuBWsTsZOfo0aMGjxljKCgowIIFC9C1a1ebBUYIIYSQe1i7CjKtoGyabt26ISEhAd26ddP/fcSIEVCr1VixYoVZ19qzZw8eeeQRREREgOM4bNy40eB5juPqPf7v//6vwWuuWrWq3jrV1dXm3iohhBBCmgCzW3Zyc3MNHvM8j+DgYMjlcrNfvKKiAl27dsW0adPw2GOP1Xm+oKDA4PGvv/6Kp59+ut6yd/P396/TpWZJfIQQQogrodlYljE72UlPT8f48eMhk8kMzqvVaqxbtw6TJ082+VrDhw/H8OHDG3w+LCzM4PGmTZswcOBAtG7d2uh1OY6rU5cQQghxe7TOjkXMvutp06ahtLS0zvmysjJMmzbNJkHV59q1a9iyZQuefvrpRsuWl5cjOjoaLVu2xMMPP4ysrCyj5VUqFZRKpcFBCCGEkKbB7GSHMWawiGCty5cv1zst3Va++eYb+Pn5YcyYMUbLtW/fHqtWrcLmzZuxdu1ayOVy9O3bF2fPnm2wzvz586FQKPRHZGSkrcMnhBBCrMY43urDE5ncjZWQkKAf7Dto0CCIxXeq6nQ65ObmYtiwYXYJEgC+/vprTJw4sdGxN4mJiUhMTNQ/7tu3L7p3745PP/0US5YsqbfO7NmzkZKSon+sVCop4SGEEOJ6aOq5RUxOdkaPHg0AyM7OxtChQ+Hr66t/TiqVolWrVo0OHLbUH3/8gZycHKxfv97sujzPo1evXkZbdmQyWZ0xSIQQQoirYbCudYaZ36HTJJic7MydOxcA0KpVKzz++OMOTQ5WrFiBHj16WLSOD2MM2dnZ6Ny5sx0iI4QQQoirM3s2Vnx8PLKzs9G7d2+D8wcOHIBIJELPnj1NvlZ5eTnOnTunf5ybm4vs7GwEBgYiKioKQE2X0g8//ID//Oc/9V5j8uTJaNGiBebPnw8AePvtt5GYmIjY2FgolUosWbIE2dnZ+Pzzz829VUIIwfViHf46qYayQoCXnEOPOCkiQ222+Dwh5qFuLIuY/Y6dPn06Zs2aVSfZuXLlChYuXIgDBw6YfK3MzEwMHDhQ/7h23MyUKVOwatUqAMC6devAGMOECRPqvUZeXh74u1aELCkpwXPPPYfCwkIoFAokJCRgz549uO+++0yOixBCqlQM32wpR9YZDfjbny+MAb/uq0bblmI8O8oXAX6e2SVAnIjjrJx67pnJDscYY+ZU8PX1xdGjR+usdZObm4suXbqgrKzMpgE6g1KphEKhQGlpKfz9/Z0dDiHEwTRahv+sUeJigQ71/YbkOSDQn8fsqf7w9aKEx9M54jOj9jUu7/4R/r4+ll+nvAItB471uM83s9+lMpkM165dq3O+oKDAYIYWIYS4q/3HVci9Wn+iAwACA24pBew4QNvQEMeqXUHZmsMTmZ3sDB48GLNnzzZYWLCkpARvvvkmBg8ebNPgCCHEGXYfUqGxjwSBAXuyVdDqzGocJ8Q6tSsoW3N4ILObYv7zn/8gOTkZ0dHRSEhIAFAzHT00NBTfffedzQMkhBBHEgSGK9d10Gp00Ol0AACxWASRWFSnbGU1wy2lgJBmdZ8jhLgOs5OdFi1a4OjRo1i9ejWOHDkCLy8vTJs2DRMmTIBEIrFHjIQQ4jAnL1RDWVwOnVYwOC+WiODtK6836SHEURg4sEbbHY3X90QWDbLx8fHBc889Z+tYCCHEqbJPV2LBiiIIQt3ntBodlCUV8A/w0Sc8XjIOzWhGFnEga7d8oO0izHTy5Enk5eVBrVYbnB85cqTVQRFCiKOpNQI++f5Gg4OSAQAMKFdWwb+ZD0Q8h37dZJCIPfObMiHuxOxk58KFC3j00Udx7NgxcByH2pnrtZuD1vZxE0KIO9l/pBIVVfU06dxD0AkQdDoE+kswpLfxvfoIsTlrBxmbWXf+/PnYsGEDTp8+DS8vL/Tp0wcLFy5EXFyc0Xrp6elISUnBiRMnEBERgVmzZuGFF16wPG4rmf0v9vLLLyMmJgbXrl2Dt7c3Tpw4gT179qBnz55IS0uzQ4iEEGJ/R89WgTfxN6KPVMCsSf7w8/bMLgHiPI6eep6eno7p06dj//792LFjB7RaLYYMGYKKiooG6+Tm5mLEiBHo168fsrKy8Oabb2LGjBlITU219vYtZnbLTkZGBnbt2oXg4GDwPA+e53H//fdj/vz5mDFjBrKysuwRJyGE2JVGw4x3Yd0m4oGkzjIE+tNAZeJ4jh6zs23bNoPHK1euREhICA4dOoTk5OR66yxfvhxRUVFYvHgxAKBDhw7IzMzEokWL7LZheGPM/hfT6XT6Hc+bN2+Oq1evAgCio6ORk5Nj2+gIIcRBwoMlJq2krxOAsOY085S4N6VSaXCoVCqT6tWusRcYGNhgmYyMDAwZMsTg3NChQ5GZmQmNRmN50FYwO9np1KkTjh49CgDo3bs3PvzwQ/z5559455136mwhQQgh7mJAL996Z2HdSy7lkNjF2/4BEVKf2o1ArTkAREZGQqFQ6I/azbSNYYwhJSUF999/Pzp16tRgucLCQoSGhhqcCw0NhVarxY0bN6y7fwuZ3Y311ltv6fvq3nvvPTz88MPo168fgoKCsH79epsHSAghjhDWXIIH7vPF7oPlRruzHhscALnM+WN1mFYNbdEl6EquA0wA7+ULcWgMeB+Fs0Mj9mRlN1btAOX8/HyDvbFkMlmjVV988UUcPXoUe/fubfxl7mkmvXcyk6OZnewMHTpU//fWrVvj5MmTuHXrFpo1a+a0myCEEFt45rEgqLUMew9XgOehb+mp/fujgxQYOdD5mydqr+dBffYQwO40RQklRdAWnIeoeSSksT3A8TSmiDTM39/frI1AX3rpJWzevBl79uxBy5YtjZYNCwtDYWGhwbmioiKIxWIEBQVZFK+1bLJzp7G+O0IIcRdiMYcZE4Mxop8/dmQokXtZA54H2reWY0iSHyJCnD9WR3vrKtRnDtbzTM03Z92NfKjBIIvr7djAiEM4egVlxhheeukl/PTTT0hLS0NMTEyjdZKSkvDzzz8bnNu+fTt69uzptJ0WaJtyQgi5R9soGdpGBTs7jDoYY9DkHm20nO7GZQgt2oH3beaAqIgjOXo21vTp07FmzRps2rQJfn5++hYbhUIBLy8vAMDs2bNx5coVfPvttwCAF154AZ999hlSUlLw7LPPIiMjAytWrMDatWstjttazu94JoQQYhJBeQOsuuH1Te7goC3MtXs8pOlbtmwZSktLMWDAAISHh+uPu8foFhQUIC8vT/84JiYGW7duRVpaGrp164Z3330XS5Yscdq0c4BadgghxG0IVWUmlmQQKkvtGgtxEg4waY0EY/XNwExYfGrVqlV1zvXv3x+HDx8278XsiJIdQghxE2ZNAvHQDR+bOgYezIpOGWvqujOz7/qbb77Bli1b9I9nzZqFgIAA9OnTB5cuXbJpcIQQQu7g/ZqbWJIDr3C9MUeEOIvZyc4HH3ygH5SUkZGBzz77DB9++CGaN2+OmTNn2jxAQgghNXhvP/D+zdF4XwSDOLSVAyIijubovbGaCrO7sfLz89G2bVsAwMaNGzF27Fg899xz6Nu3LwYMGGDr+AghhNxF2qY7qo/uAnQ61E43v5ckpgt4Ga3y3BQ5ejZWU2H2Xfv6+uLmzZsAaubNP/jggwAAuVyOqqoq20ZHCCHEAO/tB3mXgeD97p5WfvvbukQGadsekETEOiU2Yn+16+xYc3gis1t2Bg8ejGeeeQYJCQk4c+YMHnroIQDAiRMn0KpVK1vHRwgh5B68tz/kXQZCqCiFrrRmuwhO7gtRszBwvGd+cyfEGLPfFZ9//jmSkpJw/fp1pKam6pd+PnToECZMmGDzAAkhhNSP91FAEtEWkhbtIA6KoETHA9R2Y1lzuDqtVguxWIzjx4/b7Jpmt+wEBATgs88+q3P+7bfftklAhBBCCKmftYOM3WGAslgsRnR0NHQ6nc2uaXaKFxMTgzlz5iAnJ8dmQRBCCCGE1Hrrrbcwe/Zs3Lp1yybXM7tl56WXXsLatWvx/vvvIyEhAZMmTcL48eMRHh5uk4AIIYQQUj9HbwTqLEuWLMG5c+cQERGB6Oho+Pj4GDxv7urMZic7KSkpSElJwZkzZ7B69WosW7YM//znPzFw4EA8+eSTmDx5srmXJIQQQogJPGXq+ejRo216PY6ZsvFFI/bv34+///3vOHr0qE372JxFqVRCoVCgtLQU/v7+zg6HEEKIC3PEZ0bta5w4fAB+vr4WX6esvBwdu/f2uM83q/bG+uuvv7BmzRqsX78epaWlGDt2rK3iIoQQQsg9PKUbq9ahQ4dw6tQpcByH+Ph4JCQkWHQds5Od2u6rNWvW4OLFixg4cCAWLFiAMWPGwM/Pz6IgCCGEENI4Biu7sdxkI9CioiI8/vjjSEtLQ0BAABhjKC0txcCBA7Fu3ToEB5u395vZd92+fXv8+uuvmD59OvLz87F9+3ZMmTKFEh1CCCGE2MRLL70EpVKJEydO4NatWyguLsbx48ehVCoxY8YMs69ndrJz+vRp/PXXX3jllVcQFhZm9gvebc+ePXjkkUcQEREBjuOwceNGg+enTp0KjuMMjsTExEavm5qaivj4eMhkMsTHx+Onn36yKk5CiGvRCQz7Divx5n9y8beXTuJvL53Evz7KRUaWEjrB6mGIhLgsT9kuYtu2bVi2bBk6dOigPxcfH4/PP/8cv/76q9nXMzvZadeundkv0pCKigp07dq13kUKaw0bNgwFBQX6Y+vWrUavmZGRgfHjx2PSpEk4cuQIJk2ahHHjxuHAgQM2i5sQ4jwqtYC3l1zC+0vzcCynApVVAiqrBBw9XYH3Ps/DO59eglojODtMQuyiZlFBa1ZQdo9kRxAESCSSOuclEgkEwfz3t1M774YPH4733nsPY8aMabCMTCZDWFiY/ggMDDR6zcWLF2Pw4MGYPXs22rdvj9mzZ2PQoEFYvHixjaMnhDjDp99eweET5QCAu3/n1f790PFyfPbdVSdERoj9eUrLzgMPPICXX34ZV6/eeS9fuXIFM2fOxKBBg8y+nsuPVEpLS0NISAjatWuHZ599FkVFRUbLZ2RkYMiQIQbnhg4din379jVYR6VSQalUGhyEENdz7YYau/eXwtiCGYwBuzJKUHRT7bjACCE29dlnn6GsrAytWrVCmzZt0LZtW8TExKCsrAyffvqp2dezauq5vQ0fPhx/+9vfEB0djdzcXMyZMwcPPPAADh06BJlMVm+dwsJChIaGGpwLDQ1FYWFhg68zf/582tuLEDewK6MEPG/YolMfjgN27y/B+IdCHBMYIQ7iCXtjAUBkZCQOHz6MHTt24PTp02CMIT4+Hg8++KBF13PpZGf8+PH6v3fq1Ak9e/ZEdHQ0tmzZYrTri7vnh8kYq3PubrNnz0ZKSor+sVKpRGRkpBWRE0LuVlYpYN9RFU5d1ECrZQhvLkK/BDmiQs37FXSjWANTflfzHHCzRGthtIS4LsY4MGZFsmNFXUfRarWQy+XIzs7G4MGDMXjwYKuvaXayo9PpsGrVKvz+++8oKiqqM1Bo165dVgfVkPDwcERHR+Ps2bMNlgkLC6vTilNUVFSnteduMpmswZYiQoh19h2txne/VkCnA2p7n87ma5F2WIWeHaR46hFfSMSm/QL2kvN3LmIEY4Bc5vK99ISQerjErucvv/wyXn75Zeh0OnTq1Aldu3Y1OOzp5s2byM/PN7rpaFJSEnbs2GFwbvv27ejTp49dYyOE1HX4tAorf6mAVmeYo9TODj90Wo0Vm8tNvl5iN3/oTJiIoRNqyhLS9PA1CwtaeLjBUF0ALrDr+bp16/C///0PI0aMsPrFy8vLce7cOf3j3NxcZGdnIzAwEIGBgZg3bx4ee+wxhIeH4+LFi3jzzTfRvHlzPProo/o6kydPRosWLTB//nwANclYcnIyFi5ciFGjRmHTpk3YuXMn9u7da3W8hBDTCYzhh98rjZZhrCbhuVSgRXR447+OOsZ6IypChsuFqgbH7fA8EBUhQ4c2XpaETYhL85TtIpy+67lUKkXbtm3NrVavzMxMDBw4UP+4dtzMlClTsGzZMhw7dgzffvstSkpKEB4ejoEDB2L9+vUGqzXn5eWB5+9kqn369MG6devw1ltvYc6cOWjTpg3Wr1+P3r172yRmQohpzlzS4kZp480wPA/syarGpPDGNzfkOA7/+kcU/rngAsordXUSHp4HfL1F+Nffo4yO0yOEuDZb73pudrLz6quv4pNPPsFnn31m9S+TAQMGwNim67/99luj10hLS6tzbuzYsbQpKSFOVnjTtP52QQCu3DC9b75lmAyfzGmD1ZuLkLa/FFpdze8QsYjDwEQFnhgZgpAgqUUxE+LqPKFlR6utmVzw1FNP2WyykNnJzt69e7F79278+uuv6NixY50VDjds2GCTwAgh7o03Y2iA2MxhBCFBUsyc1hLPjAtHfoEKQE3Xla+3yLwLEeJmPCHZEYvFWLRoEaZMmWK7a5pbISAgwGDMDCGE1KddVN2l3uvDcUD7VqaVvZefjwjxbb0tqksIcV2DBg1CWloapk6dapPrmZ3srFy50iYvTAhp2sKCRIiLFuNsnhbG9ubkOKBfN7njAiPEjXlCyw5Qs6jw7Nmzcfz4cfTo0aPOAOWRI0eadT2LFxW8fv06cnJywHEc2rVrh+DgYEsvRQhpoiYN98UHq0pRrWINJjwTBvtA4ese02EJcTZPWFQQAP7+978DAD766KM6z3EcZ/YaPGb/hqmoqMBTTz2F8PBwJCcno1+/foiIiMDTTz+Nykrj00wJIZ4lNFCE2VMUaNvyzveq2nkNAX4cnh7piwE9qFWHEFN5ykaggiA0eFiy2KDZyU5KSgrS09Px888/o6SkBCUlJdi0aRPS09Px6quvmh0AIaRpCwsS4Z+TFHjnOQUmDvXBuEHeeHm8HxZOb4bETrRyOSHkjhEjRqC0tFT/+P3330dJSYn+8c2bNxEfH2/2dTlmbO53PZo3b44ff/wRAwYMMDi/e/dujBs3DtevXzc7CFejVCqhUChQWloKf39ahZUQQkjDHPGZUfsaGYfPwPeutebMVV5WhqTu7Vz2800kEqGgoAAhITWb+Pr7+yM7OxutW7cGAFy7dg0RERFmt+6YPWansrKy3n2mQkJCqBuLEEIIsaOmPkD53vYXM9tjGmR2N1ZSUhLmzp2L6upq/bmqqiq8/fbbSEpKsklQhBBCCCG2YnbLzieffIJhw4ahZcuW6Nq1KziOQ3Z2NuRyuUkrHhNCCCHEMgxWzsZy8ZYdjuPq7M5gi61fzE52OnXqhLNnz+L777/H6dOnwRjD448/jokTJ8LLizbeI4QQQuxFAAfBioTFmrqOwBjD1KlTIZPVTF6orq7GCy+8oF9nR6VSWXRdi9bZ8fLywrPPPmvRCxJCCCGE1OfeLSKefPLJOmUmT55s9nVNSnY2b96M4cOHQyKRYPPmzUbLmruqISGEEEJM09QHKNtrlwaTkp3Ro0ejsLAQISEhRrddt2RVQ0IIIYSYxlNWULY1k5IdQRDq/TshhBBCiKsze+r5t99+W+8AIbVajW+//dYmQRFCGqYTGK6VqHE0twx/5ZTi4JlS5FyuQGmF1mZrUhBCXBODtVtGeCazk51p06YZLOVcq6ysDNOmTbNJUISQ+qm1Ao5fLEduYRUqVQIEBugEoLhci1P5FcgtrKKEh5AmrLYby5rDE5md7DDG6p3zfvnyZSgUCpsERQipizGG0/kVqFI33JVcVKrBlZuWTc0khLg+Z2wEumfPHjzyyCOIiIgAx3HYuHGj0fJpaWn69XLuPk6fPm3hXVvP5KnnCQkJ+oAHDRoEsfhOVZ1Oh9zcXAwbNswuQRJCgNJKLSpVjY+ZK7ilQkSgDDzvmd/gCCG2VVFRga5du2LatGl47LHHTK6Xk5NjsP9WcHCwPcIzicnJTu0srOzsbAwdOhS+vr7656RSKVq1amXWPwIhxDzXSzQmlavt1gryl9g5IkKIozljNtbw4cMxfPhws+uFhIQgICDA7Hr2YHKyM3fuXABAq1at8Pjjj+tXNySEOIZaa/pMSHPKEkLcBwNgzbu7dkSfUqk0OC+TyWz+uZ6QkIDq6mrEx8fjrbfewsCBA216fXOYPWYnPj4e2dnZdc4fOHAAmZmZtoiJEFIPkcj0b2Qi6sIihBgRGRkJhUKhP+bPn2+za4eHh+OLL75AamoqNmzYgLi4OAwaNAh79uyx2WuYy+ztIqZPn45Zs2ahd+/eBuevXLmChQsX4sCBAzYLjhByR6CvBCXlWpPKBvhatBMMIcTF2aobKz8/32A8jS1bdeLi4hAXF6d/nJSUhPz8fCxatAjJyck2ex1zmN2yc/LkSXTv3r3O+YSEBJw8edImQRFC6gryl5jUYhPkJ4ZUbPZbmxDiBmw1G8vf39/gsPfQlMTERJw9e9aur2GM2b8RZTIZrl27Vud8QUGBwQwtQohtiXgOcS29YSzf8ZLyiAnzdlxQhBBigqysLISHhzvt9c3OTgYPHozZs2dj06ZN+nV1SkpK8Oabb2Lw4ME2D5AQcoe/txidWvni8o1q3Cq706Ul4oHQABkigmQQGxnbIwgMWSfKkHWyHFotQ4swGQYmBsDXh76oEOIOnDEbq7y8HOfOndM/zs3NRXZ2NgIDAxEVFYXZs2fjypUr+l0UFi9ejFatWqFjx45Qq9X4/vvvkZqaitTUVIvjtpbZv+H+85//IDk5GdHR0UhISABQMx09NDQU3333nc0DJIQY8paJ0K6FDzRaAdUaATzHwUvKN7quzqlzFViw/BIKr6shEgEcaqap/3fNFTz+SCgmjgqtd8FQQojrcMau55mZmQYzqVJSUgAAU6ZMwapVq1BQUIC8vDz982q1Gq+99hquXLkCLy8vdOzYEVu2bMGIESMsjttaHLNgbfmKigqsXr0aR44cgZeXF7p06YIJEyZAImka63oolUooFAqUlpYaDOAixF2dza3EzPfPQqdlEBp4x/9tRAieGR/h2MAIaQIc8ZlR+xrbDlyFj6/lr1FRrsSw3hEe9/lmUdu1j48PnnvuOVvHQgixk8+/vwydruFEBwB+2FqEocmBiAyXQ6UWUFnN4C3nIJPSYGdCXIXAYPR9bEp9T2RSsrN582YMHz4cEokEmzdvNlp25MiRNgmMEGIbuflVOHWustFyPA98t7EIXn6+yD6jAmMAxwHd4mQY0dcXca2kDoiWEGKMM7qxmgKTkp3Ro0ejsLAQISEh+m0j6sNxHHQ6na1iI4TYwJncxhMdABAE4M/DSgRHSFDbuc1xHE7lCTh1SYkhiV54bKA3jeshxImcMUC5KTAp2REEod6/E0Jcn7mj8gQB4HkOvgovyORifXKzPwc4faUKY5JliI8W2SFSQgixD+qMJ6SJa9vKy+SyEqkEPM+hWbCvQaJTq6Sc4eutKhw+Y9pKzoQQ22LM+sMTmdSys2TJEpMvOGPGDJPL7tmzB//3f/+HQ4cOoaCgAD/99JO+m0yj0eCtt97C1q1bceHCBSgUCjz44INYsGABIiIanjGyatUqTJs2rc75qqoqyOVyk2MjpKloG+2Ntq28cOFSVaODE719veGn8ALPcw10V9WcW79bjXaRIvh6eWaTOCHOIoCDYMW4G2vqujOTkp2PP/7Y4PH169dRWVmp37q9pKQE3t7eCAkJMSvZqaioQNeuXTFt2jQ89thjBs9VVlbi8OHDmDNnDrp27Yri4mK88sorGDlyZKMbjvr7+yMnJ8fgHCU6xJP948mWmDX/HJjAGvxm5+XrBamXBNJ6WnTuJQjAX6e1eCChaSw3QQhp2kxKdnJzc/V/X7NmDZYuXYoVK1boN/rKycnBs88+i+eff96sFx8+fDiGDx9e73MKhQI7duwwOPfpp5/ivvvuQ15eHqKiohq8LsdxCAsLMzkOlUoFlUqlf6xUKk2uS4g76Bjrg/f/2RoLll5CsVILkQjA7SmsHAfIfb3hq/CFVNp4ooOaqjiTr6NkhxAHowHKljF7nZ05c+bgxx9/NNjRNC4uDh9//DHGjh2LiRMn2jTAu5WWloLjOH2LUkPKy8sRHR0NnU6Hbt264d1339Wv9lyf+fPn4+2337ZxtIS4lm4d/PD9xx2RkVWKI6fKodEIiAiTYXDfQHz/axkOn1aB4zgwxkxKeNQ0bIcQh7N23A2N2TFRQUEBNBpNnfM6na7eDUJtpbq6Gm+88QaeeOIJo6s+tm/fHqtWrULnzp2hVCrxySefoG/fvjhy5AhiY2PrrTN79mz98tdATctOZGSkze+BEGcTizn06xWAfr0CDM4P7+uLzJMqaHWCSYkOzwHBCs/8hkgIcT9mz8YaNGgQnn32WWRmZqJ2p4nMzEw8//zzePDBB20eIFAzWPnxxx+HIAhYunSp0bKJiYl48skn0bVrV/Tr1w//+9//0K5dO3z66acN1pHJZHW2uyfEk8RGSTFtpAIalRY6nYDGdpERGJAYT5uHEuJotYsKWnN4IrN/W3399deYMmUK7rvvPv1eWFqtFkOHDsVXX31l8wA1Gg3GjRuH3Nxc7Nq1y+xEhOd59OrVC2fPnrV5bITYC2MMp3I1yDhahZJyAT5yHj3iZegeJ4PIyK7m1hjYyxstQ8X4cXcVblY2PBaH44DYFjxahdHKFYQ4Gm0XYRmzk53g4GBs3boVZ86cwenTp8EYQ4cOHdCuXTubB1eb6Jw9exa7d+9GUFCQ2ddgjCE7OxudO3e2eXyE2EOxUofFa0pwqVALnr+9yB8H7D9ejUB/HjOfCEBkmH0GBsdGSTF7ihRp2RpsydAA3J0+fp6r+UXZNoLH5KEyWkmZEOI2LG6HbtWqFRhjaNOmDcRiyy5TXl6Oc+fO6R/n5uYiOzsbgYGBiIiIwNixY3H48GH88ssv0Ol0KCwsBAAEBgZCKq3Zp2fy5Mlo0aIF5s+fDwB4++23kZiYiNjYWCiVSixZsgTZ2dn4/PPPLb1VQqwiCMC5QuDYJaCotOZcc3+gSzTQNhwQ3dVAUqUSsGBVMa6X6PR1gTvfxkrKap6f93wQgpvZbxXjAd0k6NJahP0ntThzWQetDghpxiMpXoy2LXirEx2h+Bq0uccgFJwHdFpA5g1xdEeIWnUEJ/ex0V0Q0gRZORsLNBvLNJWVlXjppZfwzTffAADOnDmD1q1bY8aMGYiIiMAbb7xh8rUyMzMxcOBA/ePaQcJTpkzBvHnz9JuOduvWzaDe7t27MWDAAABAXl4eeP7Op0VJSQmee+45FBYWQqFQICEhAXv27MF9991n7q0SYjWNDtiSCVy5VbMcX20L8vVSYOdR4EQ+8HBPQHr7nbjncBWKbunQUEuzwIAqNcPWPysw5WH7ji0L9OcxIlGKETa+rvZcFrQn/qzpD6ttNqqugDbnILTnsyHtMwp8s1AbvyohTQPNxrKM2cnO7NmzceTIEaSlpWHYsGH68w8++CDmzp1rVrIzYMAAowMhGxskCQBpaWkGjz/++OM6iyAS4iy7jwFXb9X8/e7/zbV/LywGfj8KDO9e8/j3g1UNJjq1BAH4M7sKjw/xg0zqXt/SdAUXahIdoJ7fugzQaqDO2AzZoCfByUzf5oIQT0ErKFvG7GRn48aNWL9+PRITEw2asuPj43H+/HmbBkeIO1NWAmcLjJdhAC5cA0oqAH8vhqJbOpOurdYCN0t1iAiueQsry3XYf6wKJWUCvOUcesZ7Iay5682W0uYchGEb170YoFFDl3cS4tgeDoyMENKUmf3b8Pr16wgJCalzvqKiggYsEnKXM1eNf6zX4jgg5yrQq41hz05jRDyg0TJ890sJ0g5W3t6tvKara+2vSnRtJ8Pzf2sGha9r7FAulBeDlV43oSSD7hIlO4TUh7qxLGP23NFevXphy5Yt+se1Cc6XX36JpKQk20VGiJurVNUkL43hAFRWAzzPoW1LiUl1/H14BCp4fLL6Fnb9VQmdUJNU6YQ7v8yOnVPh7eXXUVElWHMbdei0GihvXsP1vPMounQOtwryoaqqqLfbWahQQnMyA6o/foT6r60mvwarrrRlyIQ0GbXbRVhzeCKzW3bmz5+PYcOG4eTJk9Bqtfjkk09w4sQJZGRkID093R4xEuKWJOLGW3WAmjLS2zPJH+ztjbP5pUbLcxwwqJcXDp9SIet0dYPlBAG4fkuHTWlleGK4wvTAjagouYmSoqsG5zSqKlSVlUDm7YvAiCjwvAiMMejOHoL29IG6zVWmNF+Jac8tQojtmN2y06dPH+zbtw+VlZVo06YNtm/fjtDQUGRkZKBHD2p2JqRW61DTmowZqykLAL3iZUiIkzU4hJDngJYhYgxN8sb2feXgG/mSJjBg918V0Gitb7uuLCupk+jcTVVRhuvnT0JboYQu91hNogPUTXQaw3EQtah/axdCPF3tooLWHJ7IrJYdjUaD5557DnPmzNFPPSeE1C80AAhRANeVDSc9HAcE+gJhATWPeZ7D9HEK/G9HGXYdrIJWB/3CghwH9Ooow5SH/SGX8TiXrzbpF1dlNUPhDa1VCxEyxqC8Xmi8EMdBy4CyM4fgdfVMo2VvX7jep0UxnSyIkpCmj8bsWMasZEcikeCnn37CnDlz7BUPIU3K0ARgQwZQqa77S4bjALmkZtr53Q0eYhGHJ4b5Y1R/Xxw+rUJpec0Mq4Q4GZr53xlsbM43NGu/zamrKqDT1t0AuA7GoOEl8NKZsCV6ff8gjEHSbRB432aWBUoIIfUwe8zOo48+io0bNxrsEk5IU6ITGIpKdSi/PbDXz4tHsEKEChVwsQhQaWrG40Q1BwJ9jXfL+HsBf+sLHDoPnLoMaG/PLBfzQPuWQM82gI+8/ro+Xjz6JTS81kzLUDHyC7WNflOTiIGQQOtmZGnVKtMKchwEkQQMaHw1j3vG7nDNW0LcrgdEzVtaGiYhTZ61m3nSRqAmatu2Ld59913s27cPPXr0gI+P4dLuM2bMsFlwhDhaQbEW5wo00Al3PqwZdDh9RYPiCgmqNSL9fPKTl4Hmfgz3twe8ZQ3/AvGRAcnxQFI7oOT2JCOF951Vky01JMkXX20oMVqG54F+3b3hJbNy007OxPpmtbFz4EOjIW5/HziZNzgvX4vDI8RTCLByI1CbReJezP51+9VXXyEgIACHDh3CoUOHDJ7jOI6SHeK2Cm5pkXP1TleNwYrHDGjmo0FxBVCludNKcrMM2HEUGNaNQSYx/o1JIgaCbbjDQ99u3tixvwL5hRr9Hlp347maJGzkAD+rX0vmbfp+VWKd2sTvjgx8eAz4gLrrdhFCiC2Znezk5ubaIw5CnEqrYzhb0PCYlNoeF4WXBlUaHrXtPgw16+mcugJ0a+WQUPWkEg6znwrCJ6tv4VSuGqLbCwpyXM2A5ubNRHh1chCCm1m/krJYIoXM2xeqyvKGC91u0REzDQSJDJxGZTzpEUsgatHO6tgI8SQ0QNkyVv0WrF1EjFZOJu6uqFTXaNMwx9UcXhLBoHWHoWZX885RDKLG5oLbmJ+PCG89F4zz+WrszapESZkO3nIevTp6oUs7GXgbxhMQ2gLX885B0GlRZ0QOq8myZNWl4JkAXWA4xNfzASY08NuVgyThQXC0ng4hZqFkxzIWdeSvWLECnTp1glwuh1wuR6dOnfDVV1/ZOjZC7IoxhltlOhQUa1FUqgVnwhKAjAESUd0+I7W2poXHWdpESjFlZABenhiEZx9rhm7t5TZNdICa1p3gqLaQSuV1fmNyTAd5VTGk2ioAAJNIIe41HFxgRJ3rcH6BkCQ+AlF4a5vGR4gnEBhn9eGJzG7ZmTNnDj7++GO89NJL+u0hMjIyMHPmTFy8eBHvvfeezYMkxJYYY7h6S4tL1zXQ1Nl3s/ZD3DN/ITRGLJEiuFU7VF49j6obV8HAgRe0EAkag38xeWR7SIIjgdBoCOXFEG4VAoyB9w8EFxBKrcGEEIcyO9lZtmwZvvzyS0yYMEF/buTIkejSpQteeuklSnaIy8u9pkH+zXrWgTHYtbP+ydMcB2h0dRtEJSLAW2bLKC1XqWK4ekuHSjUDzwFBfjxCFbxNW3q8I9pA4qOAuugSdGW39OfF/s0hDY2G2C9Qf473bUbr5hBiI9SNZRmzkx2dToeePXvWOd+jRw9otSYsJEaIE5VW6upPdG4ztm1T7S+ZmgHKd9UBEBsOh4/XuZcgMJy6rMXVYkGft3EACooF5IiAztESBPlZOQX9LhJFc0gUzcG0GjCdBpxYAk5EY3AIsSdKdixj9m++J598EsuWLatz/osvvsDEiRNtEhQh9nLlprbRDqo7PSx3fivcHn+L0ioJ7m7x4VDTotO+hY0DNRNjDMfzaxId4E7ktX9qdEBWrgYlFbZfZYMTS8DLvCnRIYS4LItmY61YsQLbt29HYmIiAGD//v3Iz8/H5MmTDVZW/uijj2wTJSE2UlyhM2kncu6uLi12+3HtooJ3b+sU6Af0aw/IG1ljx95KKxmulRhPZBgDzlzV4r5YqYOiIoTYGrNyM09PbdkxO9k5fvw4unfvDgA4f/48ACA4OBjBwcE4fvy4vhwNQCSuyJw3epCfCFIJB//b20WUVwOXrgPVmprVjyODgCA/1/h/fvmmznDIUQNKKxnKqwT4etmuO4sQ4jiMcWBWzKiypq47MzvZ2b17tz3iIMRmKlU6lFVqwBggk/BQ+ErA306+5VIOFdWmZTyxERLIJHeSAoU30CXaLiFbTVnJGkh0GLz4ash5NRiAakEGZZWIkh1CiEexfmlVQlxElUqHi4UVqKg2nE8uFnFoGeyF5goZIppJcLZA3ei1An15g0TH1dXXkOojqkSw9Bak/J3NQjkO0JZKUO0VBrmXt2ODJIRYjQYoW4aSHdIkVKt1OJ2nhO72sBWdwEGtE0Mr1CQsZfkatFIxRDSX4fJNDlXqht/xHIDoEPcabNvMp6bFqvaufEQViJBd1z9/dzLEdBpcu5KP0BaRlPAQ4mYEK8fsWFPXnbnPV1dC7sEY029ZcqmwEjqhdmq4BEq1HNU6MbRMBC0ToVonxukC4EKhFl2ipQ3uUs5zQKcoGfy9RPU+76paBonuWg5RQJjsRs3fjXTP37hWoP/3I4SQpoxadohbERiDsqwSxaXlUKlrNu6USiWoquYAiFGtlaJaV1+rTM2n/sXrAiRiDj3byHGzTIfCEi2qNQxinkNzfxHCAsQQi9xvAJ+vF4/oYBEuXdfBT1wJEdd4EqPTalFdWQkvH9N3NCeEOBd1Y1mGkh3iNnQ6AfkF11GtMtydXK3WQC4CtIIWJTqvRq9zvlCHlkEiNPcXg4MIJeUMYhEQquDcMtGpFRsugkQElBVX69cFakx1NSU7hLgTSnYsY1Kys3nzZpMvOHLkSIuDIcSYq0W36iQ6dxNxAprJynBLpTB6HYEBxy7qcPC0gEvX7rzz5VLgvvYiDOwmglTsfkkPx3GICRXjOuNRWW5iJU/9zUeIm6IxO5YxKdkZPXq0SRfjOA46XZ2dFQmxmkqtQUVltdEyHAd4i9UoVeugY8bH3GSf1yGvyPBctRr445gOF64KeHq4BFInLxRoKalManKyI5G6yIZehBBiRyYNUBYEwaSDEh1iL8qySpPLeotVRp8XhJpvN/U1ajAGXLnJsOOQ+/5f9vUz3rJVi+M4ePv62TkaQogt1XZjWXOYa8+ePXjkkUcQEREBjuOwcePGRuukp6ejR48ekMvlaN26NZYvX27+C9sQjdkhbkFrRiIt4oxvm8DzQElZw88LAsPvB8pw5FAJLl2ugljEoWu8Hx4eFIyIUNdvCRGJxfBvFghl8S2j5QKCgsHzNCGTEHciCDWHNfXNVVFRga5du2LatGl47LHHGi2fm5uLESNG4Nlnn8X333+PP//8E//4xz8QHBxsUn17sCjZqaioQHp6OvLy8qBWGy7QNmPGDJsERsjdzPlQNvZeZgxQaYBbygbq6gScO5GPkutl4Pk7vxjOXarED1uuYdq4CDwxKtz0wJ0kILA5wABlSf0JT0BQc/gpAhwbFCHELQ0fPhzDhw83ufzy5csRFRWFxYsXAwA6dOiAzMxMLFq0yH2SnaysLIwYMQKVlZWoqKhAYGAgbty4AW9vb4SEhFCyQ+zCz8cLxaWND0ThOKBKK8Pt7TvrPM8AnMptuP6Fk5dRcr2m2efub0C1f1/5v6vw8xHjkQeDTQ/eCTiOQ7PmwfBTBKBMWQK1qqZrTyaXw9c/AGIxNeoS4o5sNRtLqTT8xieTySCT2ablOiMjA0OGDDE4N3ToUKxYsQIajQYSieMXbTW7DXvmzJl45JFHcOvWLXh5eWH//v24dOkSevTogUWLFtkjRkLgJZdCJm38A9pLLkXnVt7wktZNdHzkHK7fAJQV9detLKvGraIGmnzusvKHK9BorWhHdiCxRIJmQcEIjWiJ0IiWCAhsTokOIW7MVmN2IiMjoVAo9Mf8+fNtFmNhYSFCQ0MNzoWGhkKr1eLGjRs2ex1zmJ3sZGdn49VXX4VIJIJIJIJKpUJkZCQ+/PBDvPnmm2Zdq7FBT4wxzJs3DxEREfDy8sKAAQNw4sSJRq+bmpqK+Ph4yGQyxMfH46effjIrLuJ6OI5Di7DmEIka/i8rFosQERqEID8efdtL0aONBO0iRIiLEKFXWwmS2knQKUbc4Lei61eL62sMqqOsXIf9h0stvBNCCHG+/Px8lJaW6o/Zs2fb9PrcPQt91a7Wfu95RzE72ZFIJPpgQ0NDkZeXBwBQKBT6v5uqdtDTZ599Vu/zH374IT766CN89tlnOHjwIMLCwjB48GCUlTU8ujQjIwPjx4/HpEmTcOTIEUyaNAnjxo3DgQMHzIqNuB6pRIyYlqFopvA1eMPwHIdAhS9atQyBRFwz5ZzjOAT68ogOFiMqWIwAHx4cx6FTKx6B/jXbQtyrukqFBrYONyDigSvXjM/4IoQQexBwZ60di47b1/H39zc4bNWFBQBhYWEoLCw0OFdUVASxWIygoCCbvY45zG7PTkhIQGZmJtq1a4eBAwfi3//+N27cuIHvvvsOnTt3NutaxgY9McawePFi/Otf/8KYMWMAAN988w1CQ0OxZs0aPP/88/XWW7x4MQYPHqzPUmfPno309HQsXrwYa9euNSs+4nrEYhFCmwcgONAfGm3NDC2JWAy+vuylvvoiDk8NlWLFNjWK78mZTR0ELTC45aKDhBD3d/eegJbWt7ekpCT8/PPPBue2b9+Onj17OmW8DmBBy84HH3yA8PCa2SjvvvsugoKC8Pe//x1FRUX44osvbBZYbm4uCgsLDQY5yWQy9O/fH/v27WuwXkMDo4zVUalUUCqVBgexP8YYyitVuFRQjNMXi5BzsQj5hSWoqFI3+obkeR4yqQQyqcTkRKdWMz8OM0ZLMbqvGBFBHLykgJ830LmDr4lxAwmd/M16TUIIcVfl5eXIzs5GdnY2gJrP5+zsbH1vzuzZszF58mR9+RdeeAGXLl1CSkoKTp06ha+//horVqzAa6+95ozwAVjQstOzZ0/934ODg7F161abBlSrtgmsvkFOly5dMlqvvjr3Nqndbf78+Xj77betiJaYSxAY8q+VoKzCsDuotLwapeXVCPCTo0WIwm79u1IJh15xIvSKu7PSckVlCP46UIhqVcODj3keiG/rg5jIxvfgIoQQW3PG3liZmZkYOHCg/nFKSgoAYMqUKVi1ahUKCgoMhrHExMRg69atmDlzJj7//HNERERgyZIlTpt2DrjBooL1DXJq7APQ3DqzZ8/W//CAmil5kZGRFkRLTHWlqLROonO3krJqiEQ8wps7rgXFx1uE1//eCu98cgFA3V8KPA/4eInw6vOtHBYTIYTcjVm5qCCzoO6AAQOMtravWrWqzrn+/fvj8OHD5r+YnZid7MTExBhNHC5cuGBVQLXCwsIA1LTU1HabATWDnO5tubm3Xn0Do4zVseX6AqRxKo0WpeXG97kCgFsllQhu5guxkRlYtZQVAk5d0kKtYQjw49EhWmzRDub392qGBW/EYvn3l5GbX2XwXI/O/nhxSpRbrKJMCGmaaNdzy5id7LzyyisGjzUaDbKysrBt2zb885//tFVciImJQVhYGHbs2IGEhAQAgFqtRnp6OhYuXNhgvaSkJOzYsQMzZ87Un9u+fTv69Oljs9iIdUqUVY0XQs3EqNKyKgQF+DRYpqJKwA+7q3E4R2Owm6+PF4dhvWUYkCA1uyuseyd//Hd+B5y5UIlLV6ohFnHoEOuD8BBKcgghxB2Zney8/PLL9Z7//PPPkZmZada1ysvLce7cOf3j2kFPgYGBiIqKwiuvvIIPPvgAsbGxiI2NxQcffABvb2888cQT+jqTJ09GixYt9Asivfzyy0hOTsbChQsxatQobNq0CTt37sTevXvNvVViJ7WzqBrDNVK2sprho/UVuF4sGCQ6AFBRxZCaVo3ScgGjk80fX8NxHOLa+CCuTcOJFiGEOFrtFHJr6nsim+0COHz4cKSmpppVJzMzEwkJCfqWm5SUFCQkJODf//43AGDWrFl45ZVX8I9//AM9e/bElStXsH37dvj53dmpOS8vDwUFBfrHffr0wbp167By5Up06dIFq1atwvr169G7d28b3CWxBVNbWlgjZbdmVNeb6NxtZ6YaFwu0ZkZICCGuyRm7njcFNhug/OOPPyIwMNCsOo0NeuI4DvPmzcO8efMaLJOWllbn3NixYzF27FizYiH2Ua3W4mZpFUrLqqETGMQiHj5y0//b+fnU33Wk0jBkHFc3+i2F54A92Wq0Cnf5sfiEEELsxKJFBe/+ts0YQ2FhIa5fv46lS5faNDji3oqV1ci/ZrhmkUYroKRcVe8KxveSS8XwktW/ANXlIh1UmsavITDg1CVq2SGENA1MYGBW9EVZU9edmZ3sjBo1ynCpfp5HcHAwBgwYgPbt29s0OOK+KqrUdRKdOzgIDOC5ht90Ip5DZFhAg91YJg77AQDodJ755iaEND00ZscyZic7xrqUCKlVdKuykRI1CY9MwtcZhOzvI0NYcz9IJQ3/9wwOMG24GccBoYGixgsSQghpssxOdkQiEQoKChASEmJw/ubNmwgJCYFOZ8ZXbtIkabUCyirVJpTkoBWAdtHBUKk1ADjIZWL9Zp7GBPrXrKWTk6c1+k2FMaBfV6nJsRNCiCujdXYsY3ay09CAYpVKBamUPlQIoDEj4dXpGCRiHlKJ3OzXeaiPDGfyteBY/ZuV8xwQFsQjoZ1zNp4jhBBbEwQGwYq+KGvqujOTk50lS5YAqJkh9dVXX8HX986miTqdDnv27KExOwSA6buHAzXdTJbuf9UqXIznR3njq18qob5rsDLP1fRLtwjm8fdHfSChHcoJIcSjmZzsfPzxxwBqWnaWL18OkehOV4NUKkWrVq2wfPly20dI3I5UzEMmEUGlabyFR+Fr3arE8TESvP+cP/46qcaRcxqoNECQP4fETlK0jxaDt9NGooQQ4gzUjWUZk5Od3NxcAMDAgQOxYcMGNGvWzG5BEffGcRyaN/PGlaKyRss2D/C2+vW8ZBz6J8jQP4G2cyCENG2U7FjG7DE7u3fvtkccpIkJ9JejokqNkrKGdzYPb+4LbzmNpyGEEFMJjEGwImOxpq47MzvZGTt2LHr27Ik33njD4Pz//d//4a+//sIPP/xgs+CI/THGIBQXQld6HWAMvLcfRMFR4ETWrTjMcRwiQ/3hLa/C9eJKaLSC/jkvmRghgT5Wd2ERQ1V5V1Dw41aort+ExN8PoaOHwK9DrLPDIoQQpzP7Ey09PR1z586tc37YsGFYtGiRTYIijqErvQ51zl9gqsqakcJATRvn+WxIojtB3CLW4sHDwO3urABvBCm8UK3WQRAEiEU8ZFLausGWtOUVOD7j3yhM/RXgOXA8DwgM5+Z/hsDk3uj69SLIQpo7O0xCiA0woeawpr4nMnsj0PLy8nqnmEskEiiVDa2YS1yNrvQGVMfSaxIdwLAjWNBBk3sE2vzTNnktjuPgJRPDx0tKiY6NCWo1Mh97DoUbttX8/HQCmEYLdnv6f/GfmTgwZCI0xaVOjpQQYgsMDIxZcdS7UEfTZ3ay06lTJ6xfv77O+XXr1iE+Pt4mQRH7YoxBfe5QoyPVNJeOQ1BVOSgqYomr639GScZhQKj/6xrT6VB56TJyP13p4MgIIcR1mP01e86cOXjsscdw/vx5PPDAAwCA33//HWvXrqXxOm5CKLsFVmlKKxwHbeEFSKM72j0mYplL//0e4PkGkx0AgE5A/tfr0Xb2dPASGhBOiDtjgvG3uyn1PZHZyc7IkSOxceNGfPDBB/jxxx/h5eWFLl26YOfOnejfv789YiQ2JpTdMrEkg1B2s/FSDChSAuXVgIgHwhSAnBbTtjum06HsqGldjZpbJajKuwqfNtF2jooQYk+13VHW1PdEFg2geOihh/DQQw/VOZ+dnY1u3bpZGxOxOzP+szdSNLcIOJwLlN3V28VzQEwI0KstQDPL7cfsX1oe+kuOEELMHrNzr9LSUixduhTdu3dHjx49bBETsTPex9QFITnwvg2XPXUFSD9pmOgANVs1XLgGbD0MqDT11yXW48Vi+MS1vjOTzgixvy+8IiMcEBUhxJ4EZv3hiSxOdnbt2oWJEyciPDwcn376KUaMGIHMzExbxkbshFc0Byf3bbwgGMThret9pqwKOHDWWM2aMpnnLQqRmCj6uYmNluFEIrScOg68jPoWCXF3TGBWH57IrGTn8uXLeO+999C6dWtMmDABgYGB0Gg0SE1NxXvvvYeEhAR7xUlsiOM4SNs2/rMSt4wDL/ep97mcq0Bj7QkMwPlr1LpjTy0mPgq/zu3B3bVX3d04kQjS0OaImTHNwZERQojrMDnZGTFiBOLj43Hy5El8+umnuHr1Kj799FN7xkZMwLQaCMpbEMpLwMwYZi9qFgZpfF9AfHtQDcdBn75wHMSRHSBp1bnB+vk3TRv5IzCgiJZ4sRuRtxd6bf4agcm9AQCcWAROJAInrkl+fONj0fu31bSoICFNRO2SaNYcnsjkAcrbt2/HjBkz8Pe//x2xsbQEvbMJ5SXQncmE7tJJQLi9u7jcB+I2XSFq2x2cuPGRweKgCIh6PwLdjct3bRfhD3FINDip3GhdnRnTF80pS8wnDQxAr00rUHY8B1f/9zNU125AEuCPsNFDEZDY3apVsAkhrkUQGAQruqKsqevOTE52/vjjD3z99dfo2bMn2rdvj0mTJmH8+PH2jI00QCgpgjr9B0CnMUzTqyugPZEB3dXzkPYbC07S+BgNjhdBHBINcYh5U5L9vYCKatNad/y8zLo0sZBfpzjEdYpzdhiEEDuiqeeWMbkbKykpCV9++SUKCgrw/PPPY926dWjRogUEQcCOHTtQVlZmzzjJbUynhfrPjXUTnTslwEqKoDli393p4yJMS3Sa+QCBpoyFJoQQQuzE7NlY3t7eeOqpp7B3714cO3YMr776KhYsWICQkBCMHDnSHjGSuwhXzwHVFcY7XhmDkHcarLrSbnFEBtUkMY11kHSPMWlmNCGEEBPUbgRqzeGJrFpnJy4uDh9++CEuX76MtWvX2iomYoTu8lk0nmIAYAJ0BRfsFgfPA0O63Gm1uTsiDjUJTt84IJLGxRJCiM0IjFl9eCKbbEEtEokwevRojB492haXI0YwTTVM6kDiOECjsmsscinwUA/gyk0gp6BmXR0RX9Pq0y4C8JHZ9eUJIYQQk9gk2SGOw8l9wTiu8fmDjIGTe9s9Hp6rab2hFhxCCLE/GqBsGUp23Iwoqj2EfBM2fxSJwYe3sX9AhBBCHIamnluGkh0XxgQBwo18MOVNgOPAKYLBhUSD8w8CK7tltHVH1KabSVPPCSGEkKaOkh0Xpbt2EZqTfwKqqjvTmRgD5L6QxCdCe/QPsErlPbU4AAx8i1iIO/Z1dMiEEELszNpVkD20F4uSHVekKzhvuE6OwcKB5dAc+wOShIFgymLozmfrkx6ueQTEbbqBbxFLq+YSQkgTxJh1m3nSmB3iEphOC83xvY2VguZUBmQDJkDcrgeYINR0c1GCQwghhNRh1To7jtCqVStwtz/I7z6mT59eb/m0tLR6y58+bcKgXhegK7hQszpyY1RVEK7nAwA4nqdEhxBCPACzco0datlxUQcPHoROp9M/Pn78OAYPHoy//e1vRuvl5OTA399f/zg4ONhuMdoSK7lWM0ansf+QHAeh+BpEZu5pRexDW1KMyrNnAEEHeasYSEPDnR0SIaQJYoKV3Vg0G8s13ZukLFiwAG3atEH//v2N1gsJCUFAQIAdI7MTk7NuExIiYnfqa4UoWPlflO7dc2f3eQC+3XsifNrz8GoT68ToCCFNDSU7lnH5bqy7qdVqfP/993jqqaca7bZJSEhAeHg4Bg0ahN27jW+KqVKpoFQqDQ5n4fyamZbEMAG8XzP7B0QapCq4grOvvFAn0QGA8uzDOJcyHRUnjjkpOkIIIbXcKtnZuHEjSkpKMHXq1AbLhIeH44svvkBqaio2bNiAuLg4DBo0CHv27Gmwzvz586FQKPRHZGSkHaI3jSgiFuBM+LGIJODDWts/INKg/P97H7oyZZ1EBwAgCGA6LS69/28IGhPGYBFCiAkEZv3hiTjmRqOVhg4dCqlUip9//tmseo888gg4jsPmzZvrfV6lUkGlurOPlFKpRGRkJEpLSw3G/TiK9nw2tGczjZYRx/eFOKqDgyIi96o6fxZnX3rWpLJRb8xFQPJAO0dECHEWpVIJhUJh18+M2teYNu8ipHLLX0NdrcTKea2c9vnmLG7TsnPp0iXs3LkTzzzzjNl1ExMTcfbs2Qafl8lk8Pf3NzicSdS6K8SxPW4vJljbXXf7T46HuEMSJTpOVpb5V83W743hRSjL3G//gAghxI6WLl2KmJgYyOVy9OjRA3/88UeDZV1xVrTLD1CutXLlSoSEhOChhx4yu25WVhbCw91ndgzHcRC3SYCoZRx0l89AKLsJAOAVIRC1iAUnlTs5QiKoqm93NwrGCzIBQrV9d58nhHgOZ2wEun79erzyyitYunQp+vbti//+978YPnw4Tp48iaioqAbrudKsaLdIdgRBwMqVKzFlyhSIxYYhz549G1euXMG3334LAFi8eDFatWqFjh076gc0p6amIjU11aExM1UldIWXAK0GnI8f+JBocKa0BNyFk3lD3KabfQIkVpGGhgG6esbq3Ivna8oSQogNCIJ1m3kKt7+f3TsRRyaTQSaT1Vvno48+wtNPP63vWVm8eDF+++03LFu2DPPnz2/wtVxpVrRbJDs7d+5EXl4ennrqqTrPFRQUIC8vT/9YrVbjtddew5UrV+Dl5YWOHTtiy5YtGDFihENiZepqaA7/Dl3ucYDd9a3fyxeSzv0gbtvNIXEQ+1L0G4Aryz4BU6uNF9Tp0GzwcMcERQghJrp3Is7cuXMxb968OuXUajUOHTqEN954w+D8kCFDsG/fPqOvkZCQgOrqasTHx+Ott97CwIHOG7voFsnOkCFDGmx6W7VqlcHjWbNmYdasWQ6Iqi6mroZqx3c1u5TfG29VOTR//QpWVQ5J5/udEh+xHZG3D4LHTkDRmm8aLsTzUPRJhjyKFn4khNiGrbqx8vPzDbqYGmrVuXHjBnQ6HUJDQw3Oh4aGorCwsN46tbOie/ToAZVKhe+++w6DBg1CWloakpOTLY7dGm6R7LgLzbE/6k907qI99gdELWPBNwttsAxxD6FPTIG2tAS3tmwCRKI73Vq8CBB08E3oiciUN4xfhBBCzGCrRQXNnYxz79p2jLEG17uLi4tDXFyc/nFSUhLy8/OxaNEiSnbcHdOqoTt/xIRtHnhozxyCtLdjutWI/XA8j5bTZyJw0FDc2LIJFceyAcYgbx2L5o+Mhm+3HmaP0yKEEFfSvHlziESiOq04RUVFdVp7jElMTMT3339v6/BMRsmOjQi3CgGtCYvHMQG6q+ftHxBxGO/28YhqH+/sMAghHsDR20VIpVL06NEDO3bswKOPPqo/v2PHDowaNcrk6zh7VjQlO7ai05petr4VdwkhhJBGCKjZvdya+uZKSUnBpEmT0LNnTyQlJeGLL75AXl4eXnjhBQCuOyv6bpTs2Ahn8j5VHDi/QLvGQgghpGlyxkag48ePx82bN/HOO++goKAAnTp1wtatWxEdXTP5wtVmRdfHrbaLcBRLl/5W7fwewvXLjY7bkSQ9DHFMZ2vDJIQQ4gIcuV3E47POQCrzs/g6alUZ1n3YjraLIJYTd+lvvADHgVM0h4i2eiCEEGKB2qnn1hyeiJIdGxKFREJ6/6M1U49x15S829PzOEVzyB6YAE5EvYeEEELMxwQGwYrDmi4wd0afujYmioyDfPSL0F44Ct3lM7e3i1BA3KYr+Ig2NBWZEEIIcTBKduyAk3tDEp8ISXyis0MhhBDShDhjgHJTQMkOIYQQ4iacset5U0B9KoQQQghp0qhlhxBCCHETTBDABMGq+p6Ikh1CCCHETdTOqrKmvieibixCCCGENGnUskMIIYS4CRqgbBlKdgghhBA3QVPPLUPJDiGEEOImKNmxDCU7xCUxnQ7a3BPQXbkACDrwgaGQxHUHJ/d2dmiEEELcDCU7xOVoL+Wgesc6sKpyoHZ7DYFB9ecvkPYaDGnPB8BxnPGLEEJIEyRAgMAsnz4ugKaeE+J02vxzqPp5BVDb0nr3mhA6HdT7twE6LWSJQ50SHyGEOBMTrOuKsiJPcms09Zy4DMYYqtNSbyc6Db+Z1Qd3Qn01z2FxEUIIcW/UskNchu5qLljJjUbLMcZwY/EcaCT+aDb8Ufj1HUjdWoQQj0ADlC1DLTvEZQhFlwETkhaO4yDxlUOVew6Fn3+Ia18t8di1IwghnqV2nR1rDk9EyQ5xO/o36+0/lbt+RenOrU6MiBBCiCujbiziMviQlvoEpjGa8ir93xmAa99+BVFUB/jFtbZTdIQQ4nyCIECwYjNPa+q6M2rZIS5DFBEDLiAYQONdWZXXbun/zgHgtFXY33cUsqfNgk6ltl+QhBDiRLVjdqw5PBElO8RlcBwH+cDHbo/baTjhKc8vgqDW1jkvkvK4suZnZE/5p8f2SxNCCKmLkh3iUsQt28Br5NPgvH0A3B6MJwj6P5WXClB+uajeujqVDhAEFKZuQ/G+w44MmxBCHIIxwerDE9GYHeJyxFHt4DPtLWhzT6IifSuqz+VAW1WNquslYLq6b1TGGNRKDdRKDQCAE4uQ9+U6BPbt4ejQCSHErmjquWUo2SEuieNFkLTpDN/ACFyf+TSYWtXg4GWO41B8ukT/mGl1KM066aBICSHEgawdd+OhyQ51YxGXJm4WhBaz3gEnkdZZg6f2DX/rVAnK8isMnuN4+q9NCCGkhkt/IsybNw8cxxkcYWFhRuukp6ejR48ekMvlaN26NZYvX+6gaIm9eMd3QfSHy+Hb50EImppuLMYYKgorcWVPAW6eKDYoz4lFCOzXyxmhEkKIXQlMsPrwRC7fjdWxY0fs3LlT/1gkEjVYNjc3FyNGjMCzzz6L77//Hn/++Sf+8Y9/IDg4GI899pgjwiV2Ig0NR8SLr+Ly9tO4+es+CBptg9tnMa0O0c9PcGyAhBDiADRmxzIun+yIxeJGW3NqLV++HFFRUVi8eDEAoEOHDsjMzMSiRYso2Wki4he9iT/7/A1MVwmm09VbpvVrz8CvY6yDIyOEEOKqXLobCwDOnj2LiIgIxMTE4PHHH8eFCxcaLJuRkYEhQ4YYnBs6dCgyMzOh0WgarKdSqaBUKg0O4pp828Wgzx/r4delPQCAE4nASWpydpGPF+LefxXtP3jNmSESQojdMCbULMdh6UHdWK6nd+/e+Pbbb9GuXTtcu3YN7733Hvr06YMTJ04gKCioTvnCwkKEhoYanAsNDYVWq8WNGzcQHh5e7+vMnz8fb7/9tl3ugdieX4c26PfXBpQcPIobv++DrloF75hIhI8dBrGPt7PDI4QQu6FuLMu4dLIzfPhw/d87d+6MpKQktGnTBt988w1SUlLqrcPdO2Pn9nTle8/fbfbs2QbXUyqViIyMtCZ04gABvbogoFcXZ4dBCCHExbl0snMvHx8fdO7cGWfPnq33+bCwMBQWFhqcKyoqglgsrrclqJZMJoNMJrNprIQQQoitWbsKsqd2Y7n8mJ27qVQqnDp1qsHuqKSkJOzYscPg3Pbt29GzZ09IJBJHhEgIIYTYjSAAgsCsOJx9B87h0snOa6+9hvT0dOTm5uLAgQMYO3YslEolpkyZAqCm+2ny5Mn68i+88AIuXbqElJQUnDp1Cl9//TVWrFiB116jAauEEEKIp3LpbqzLly9jwoQJuHHjBoKDg5GYmIj9+/cjOjoaAFBQUIC8vDx9+ZiYGGzduhUzZ87E559/joiICCxZsoSmnRNCCGkSamdVWVPfE7l0srNu3Tqjz69atarOuf79++PwYdrxmhBCSNNDs7Es49LJDiGEEELuoAHKlnHpMTuEEEIIIdailh1CCCHETVA3lmUo2SGEEELcBA1QtgwlO/WoXXWZ9sgihBDSmNrPitrPDnvSaSucWt9dUbJTj7KyMgCgLSMIIYSYrKysDAqFwi7XlkqlCAsLQ+bv46y+VlhYGKRSqQ2ich8cc0Qq6mYEQcDVq1fh5+dndE8te6jdlys/Px/+/v4OfW1bair3AdC9uKqmci9N5T4Az70XxhjKysoQEREBnrffvJ/q6mqo1WqrryOVSiGXy20Qkfuglp168DyPli1bOjUGf39/t/9lATSd+wDoXlxVU7mXpnIfgGfei71adO4ml8s9LkmxFZp6TgghhJAmjZIdQgghhDRplOy4GJlMhrlz50Imkzk7FKs0lfsA6F5cVVO5l6ZyHwDdC3FdNECZEEIIIU0atewQQgghpEmjZIcQQgghTRolO4QQQghp0ijZIYQQQkiTRsmOA82bNw8cxxkcYWFhRuukp6ejR48ekMvlaN26NZYvX+6gaBvWqlWrOvfBcRymT59eb/m0tLR6y58+fdrBkQN79uzBI488goiICHAch40bNxo8zxjDvHnzEBERAS8vLwwYMAAnTpxo9LqpqamIj4+HTCZDfHw8fvrpJzvdwR3G7kWj0eD1119H586d4ePjg4iICEyePBlXr141es1Vq1bV+7Oqrq522r0AwNSpU+vElJiY2Oh1Hf1zaew+6vu35TgO//d//9fgNZ3xM5k/fz569eoFPz8/hISEYPTo0cjJyTEo4y7vlcbuxd3eK8QylOw4WMeOHVFQUKA/jh071mDZ3NxcjBgxAv369UNWVhbefPNNzJgxA6mpqQ6MuK6DBw8a3MOOHTsAAH/729+M1svJyTGoFxsb64hwDVRUVKBr16747LPP6n3+ww8/xEcffYTPPvsMBw8eRFhYGAYPHqzfL60+GRkZGD9+PCZNmoQjR45g0qRJGDduHA4cOGCv2wBg/F4qKytx+PBhzJkzB4cPH8aGDRtw5swZjBw5stHr+vv7G/ycCgoK7L5qa2M/FwAYNmyYQUxbt241ek1n/Fwau497/12//vprcByHxx57zOh1Hf0zSU9Px/Tp07F//37s2LEDWq0WQ4YMQUXFnU0k3eW90ti9uNt7hViIEYeZO3cu69q1q8nlZ82axdq3b29w7vnnn2eJiYk2jsw6L7/8MmvTpg0TBKHe53fv3s0AsOLiYscG1ggA7KefftI/FgSBhYWFsQULFujPVVdXM4VCwZYvX97gdcaNG8eGDRtmcG7o0KHs8ccft3nMDbn3Xurz119/MQDs0qVLDZZZuXIlUygUtg3OTPXdy5QpU9ioUaPMuo6zfy6m/ExGjRrFHnjgAaNlXOFnUlRUxACw9PR0xph7v1fuvZf6uMt7hZiOWnYc7OzZs4iIiEBMTAwef/xxXLhwocGyGRkZGDJkiMG5oUOHIjMzExqNxt6hmkStVuP777/HU0891eimqQkJCQgPD8egQYOwe/duB0VoutzcXBQWFhr8m8tkMvTv3x/79u1rsF5DPydjdZyhtLQUHMchICDAaLny8nJER0ejZcuWePjhh5GVleWYABuRlpaGkJAQtGvXDs8++yyKioqMlnf1n8u1a9ewZcsWPP30042WdfbPpLS0FAAQGBgIwL3fK/feS0Nl3Pm9QuqiZMeBevfujW+//Ra//fYbvvzySxQWFqJPnz64efNmveULCwsRGhpqcC40NBRarRY3btxwRMiN2rhxI0pKSjB16tQGy4SHh+OLL75AamoqNmzYgLi4OAwaNAh79uxxXKAmKCwsBIB6/81rn2uonrl1HK26uhpvvPEGnnjiCaObGrZv3x6rVq3C5s2bsXbtWsjlcvTt2xdnz551YLR1DR8+HKtXr8auXbvwn//8BwcPHsQDDzwAlUrVYB1X/7l888038PPzw5gxY4yWc/bPhDGGlJQU3H///ejUqRMA932v1Hcv93L39wqpH+167kDDhw/X/71z585ISkpCmzZt8M033yAlJaXeOve2lrDbC1431oriKCtWrMDw4cMRERHRYJm4uDjExcXpHyclJSE/Px+LFi1CcnKyI8I0S33/5o39e1tSx1E0Gg0ef/xxCIKApUuXGi2bmJhoMPC3b9++6N69Oz799FMsWbLE3qE2aPz48fq/d+rUCT179kR0dDS2bNliNFlw5Z/L119/jYkTJzY6xsPZP5MXX3wRR48exd69e+s8527vFWP3AjSN9wqpH7XsOJGPjw86d+7c4DeBsLCwOt94ioqKIBaLERQU5IgQjbp06RJ27tyJZ555xuy6iYmJLvcNqHZmXH3/5vd+G723nrl1HEWj0WDcuHHIzc3Fjh07jH5TrQ/P8+jVq5fL/azCw8MRHR1tNC5X/rn88ccfyMnJsei948ifyUsvvYTNmzdj9+7daNmypf68O75XGrqXWk31vUJqULLjRCqVCqdOnUJ4eHi9zyclJelnOtXavn07evbsCYlE4ogQjVq5ciVCQkLw0EMPmV03Kyurwft2lpiYGISFhRn8m6vVaqSnp6NPnz4N1mvo52SsjiPU/vI+e/Ysdu7caVGCzBhDdna2y/2sbt68ifz8fKNxuerPBahpEe3Rowe6du1qdl1H/EwYY3jxxRexYcMG7Nq1CzExMQbPu9N7pbF7AZr2e4Xc5pRh0R7q1VdfZWlpaezChQts//797OGHH2Z+fn7s4sWLjDHG3njjDTZp0iR9+QsXLjBvb282c+ZMdvLkSbZixQomkUjYjz/+6Kxb0NPpdCwqKoq9/vrrdZ679z4+/vhj9tNPP7EzZ86w48ePszfeeIMBYKmpqY4MmTHGWFlZGcvKymJZWVkMAPvoo49YVlaWftbFggULmEKhYBs2bGDHjh1jEyZMYOHh4UypVOqvMWnSJPbGG2/oH//5559MJBKxBQsWsFOnTrEFCxYwsVjM9u/f77R70Wg0bOTIkaxly5YsOzubFRQU6A+VStXgvcybN49t27aNnT9/nmVlZbFp06YxsVjMDhw44LR7KSsrY6+++irbt28fy83NZbt372ZJSUmsRYsWLvdzaez/F2OMlZaWMm9vb7Zs2bJ6r+EKP5O///3vTKFQsLS0NIP/O5WVlfoy7vJeaexe3O29QixDyY4DjR8/noWHhzOJRMIiIiLYmDFj2IkTJ/TPT5kyhfXv39+gTlpaGktISGBSqZS1atWqwV+Qjvbbb78xACwnJ6fOc/fex8KFC1mbNm2YXC5nzZo1Y/fffz/bsmWLA6O9o3Ya/L3HlClTGGM1U2rnzp3LwsLCmEwmY8nJyezYsWMG1+jfv7++fK0ffviBxcXFMYlEwtq3b++QRM7YveTm5tb7HAC2e/fuBu/llVdeYVFRUUwqlbLg4GA2ZMgQtm/fPqfeS2VlJRsyZAgLDg5mEomERUVFsSlTprC8vDyDa7jCz6Wx/1+MMfbf//6XeXl5sZKSknqv4Qo/k4b+76xcuVJfxl3eK43di7u9V4hlOMZuj3glhBBCCGmCaMwOIYQQQpo0SnYIIYQQ0qRRskMIIYSQJo2SHUIIIYQ0aZTsEEIIIaRJo2SHEEIIIU0aJTuEEEIIadIo2SGEEEJIk0bJDiFuat68eejWrZv+8dSpUzF69GiHx3Hx4kVwHIfs7Gy7vg7Hcdi4caNdX4MQ0jRRskOIDU2dOhUcx4HjOEgkErRu3RqvvfYaKioq7P7an3zyCVatWmVSWUclKIQQ4grEzg6AkKZm2LBhWLlyJTQaDf744w8888wzqKiowLJly+qU1Wg0NtvBXqFQ2OQ6hBDS1FDLDiE2JpPJEBYWhsjISDzxxBOYOHGivvultuvp66+/RuvWrSGTycAYQ2lpKZ577jmEhITA398fDzzwAI4cOWJw3QULFiA0NBR+fn54+umnUV1dbfD8vd1YgiBg4cKFaNu2LWQyGaKiovD+++8DAGJiYgAACQkJ4DgOAwYM0NdbuXIlOnToALlcjvbt22Pp0qUGr/PXX38hISEBcrkcPXv2RFZWltF/j9mzZyMxMbHO+S5dumDu3LkAgIMHD2Lw4MFo3rw5FAoF+vfvj8OHDzd4zbS0NHAch5KSEv257OxscByHixcv6s/t27cPycnJ8PLyQmRkJGbMmGHQyrZ06VLExsZCLpcjNDQUY8eONXovhBD3RMkOIXbm5eUFjUajf3zu3Dn873//Q2pqqr4b6aGHHkJhYSG2bt2KQ4cOoXv37hg0aBBu3boFAPjf//6HuXPn4v3330dmZibCw8PrJCH3mj17NhYuXIg5c+bg5MmTWLNmDUJDQwHUJCwAsHPnThQUFGDDhg0AgC+//BL/+te/8P777+PUqVP44IMPMGfOHHzzzTcAgIqKCjz88MOIi4vDoUOHMG/ePLz22mtG45g4cSIOHDiA8+fP68+dOHECx44dw8SJEwEAZWVlmDJlCv744w/s378fsbGxGDFiBMrKykz9Z67j2LFjGDp0KMaMGYOjR49i/fr12Lt3L1588UUAQGZmJmbMmIF33nkHOTk52LZtG5KTky1+PUKIC3PyruuENClTpkxho0aN0j8+cOAACwoKYuPGjWOMMTZ37lwmkUhYUVGRvszvv//O/P39WXV1tcG12rRpw/773/8yxhhLSkpiL7zwgsHzvXv3Zl27dq33tZVKJZPJZOzLL7+sN87c3FwGgGVlZRmcj4yMZGvWrDE49+6777KkpCTGGGP//e9/WWBgIKuoqNA/v2zZsnqvdbcuXbqwd955R/949uzZrFevXg2W12q1zM/Pj/3888/6cwDYTz/9xBhjbPfu3QwAKy4u1j+flZXFALDc3FzGGGOTJk1izz33nMF1//jjD8bzPKuqqmKpqanM39+fKZXKBuMghDQN1LJDiI398ssv8PX1hVwuR1JSEpKTk/Hpp5/qn4+OjkZwcLD+8aFDh1BeXo6goCD4+vrqj9zcXH1ryKlTp5CUlGTwOvc+vtupU6egUqkwaNAgk+O+fv068vPz8fTTTxvE8d577xnE0bVrV3h7e5sUR62JEydi9erVAADGGNauXatv1QGAoqIivPDCC2jXrh0UCgUUCgXKy8uRl5dncvz3OnToEFatWmVwL0OHDoUgCMjNzcXgwYMRHR2N1q1bY9KkSVi9ejUqKystfj1CiOuiAcqE2NjAgQOxbNkySCQSRERE1BmA7OPjY/BYEASEh4cjLS2tzrUCAgIsisHLy8vsOoIgAKjpyurdu7fBcyKRCEBNomKJJ554Am+88QYOHz6Mqqoq5Ofn4/HHH9c/P3XqVFy/fh2LFy9GdHQ0ZDIZkpKSoFar670ez/N14rm7q7D2fp5//nnMmDGjTv2oqChIpVIcPnwYaWlp2L59O/79739j3rx5OHjwoMX/7oQQ10TJDiE25uPjg7Zt25pcvnv37igsLIRYLEarVq3qLdOhQwfs378fkydP1p/bv39/g9eMjY2Fl5cXfv/9dzzzzDN1npdKpQAAnU6nPxcaGooWLVrgwoULBq0ud4uPj8d3332HqqoqfUJlLI5aLVu2RHJyMlavXo2qqio8+OCD+vFDAPDHH39g6dKlGDFiBAAgPz8fN27caPB6tS1jBQUFaNasGQDUmUbfvXt3nDhxwujPQiwW48EHH8SDDz6IuXPnIiAgALt27cKYMWMavSdCiPugZIcQJ3vwwQeRlJSE0aNHY+HChYiLi8PVq1exdetWjB49Gj179sTLL7+MKVOmoGfPnrj//vuxevVqnDhxAq1bt673mnK5HK+//jpmzZoFqVSKvn374vr16zhx4gSefvpphISEwMvLC9u2bUPLli0hl8uhUCgwb948zJgxA/7+/hg+fDhUKhUyMzNRXFyMlJQUPPHEE/jXv/6Fp59+Gm+99RYuXryIRYsWmXSfEydOxLx586BWq/Hxxx8bPNe2bVt899136NmzJ5RKJf75z38abZ1q27YtIiMjMW/ePLz33ns4e/Ys/vOf/xiUef3115GYmIjp06fj2WefhY+PD06dOoUdO3bg008/xS+//IILFy4gOTkZzZo1w9atWyEIAuLi4ky6H0KIG3HymCFCmpR7Byjfa+7cuQaDimsplUr20ksvsYiICCaRSFhkZCSbOHEiy8vL05d5//33WfPmzZmvry+bMmUKmzVrVoMDlBljTKfTsffee49FR0cziUTCoqKi2AcffKB//ssvv2SRkZGM53nWv39//fnVq1ezbt26MalUypo1a8aSk5PZhg0b9M9nZGSwrl27MqlUyrp168ZSU1MbHaDMGGPFxcVMJpMxb29vVlZWZvDc4cOHWc+ePZlMJmOxsbHshx9+YNHR0ezjjz/Wl8FdA5QZY2zv3r2sc+fOTC6Xs379+rEffvjBYIAyY4z99ddfbPDgwczX15f5+PiwLl26sPfff58xVjNYuX///qxZs2bMy8uLdenSha1fv97oPRBC3BPHmIWd8IQQQgghboBmYxFCCCGkSaNkhxBCCCFNGiU7hBBCCGnSKNkhhBBCSJNGyQ4hhBBCmjRKdgghhBDSpFGyQwghhJAmjZIdQgghhDRplOwQQgghpEmjZIcQQgghTRolO4QQQghp0v4f/zYpUhE5mTsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "error = np.abs(predictions- y_test)\n",
    "plt.scatter(predictions, y_test, c= error, cmap = \"coolwarm\", s= 50)\n",
    "plt.xlabel(\"Predicted values\")\n",
    "plt.ylabel(\"Actual values\")\n",
    "plt.ylabel(\"Actual prediction vs actual values\")\n",
    "plt.colorbar(label =\"Error\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebbe22b5-d75b-472b-8370-bbb2c8b6bafa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}