honey_production.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Honey Production**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The honeybees are in a precarious state right now. There have been articles about the decline of the honeybee population for various reasons. This project is to investigate this decline and how the trends of the past predict the future for the honeybees."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here is the loaded DataFrame about honey production in the *United States* from [Kaggle](https://www.kaggle.com/). It is called `df` and has the following columns:\n",
    "\n",
    "- state\n",
    "- numcol\n",
    "- yieldpercol\n",
    "- totalprod\n",
    "- stocks\n",
    "- priceperlb\n",
    "- prodvalue\n",
    "- year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>state</th>\n",
       "      <th>numcol</th>\n",
       "      <th>yieldpercol</th>\n",
       "      <th>totalprod</th>\n",
       "      <th>stocks</th>\n",
       "      <th>priceperlb</th>\n",
       "      <th>prodvalue</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1136000.0</td>\n",
       "      <td>159000.0</td>\n",
       "      <td>0.72</td>\n",
       "      <td>818000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AZ</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>60</td>\n",
       "      <td>3300000.0</td>\n",
       "      <td>1485000.0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>2112000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>65</td>\n",
       "      <td>3445000.0</td>\n",
       "      <td>1688000.0</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2033000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>83</td>\n",
       "      <td>37350000.0</td>\n",
       "      <td>12326000.0</td>\n",
       "      <td>0.62</td>\n",
       "      <td>23157000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CO</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>72</td>\n",
       "      <td>1944000.0</td>\n",
       "      <td>1594000.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1361000.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state    numcol  yieldpercol   totalprod      stocks  priceperlb  \\\n",
       "0    AL   16000.0           71   1136000.0    159000.0        0.72   \n",
       "1    AZ   55000.0           60   3300000.0   1485000.0        0.64   \n",
       "2    AR   53000.0           65   3445000.0   1688000.0        0.59   \n",
       "3    CA  450000.0           83  37350000.0  12326000.0        0.62   \n",
       "4    CO   27000.0           72   1944000.0   1594000.0        0.70   \n",
       "\n",
       "    prodvalue  year  \n",
       "0    818000.0  1998  \n",
       "1   2112000.0  1998  \n",
       "2   2033000.0  1998  \n",
       "3  23157000.0  1998  \n",
       "4   1361000.0  1998  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://s3.amazonaws.com/linear_regression/honeyproduction.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
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  {
   "cell_type": "markdown",
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    year     totalprod\n",
      "0   1998  5.105093e+06\n",
      "1   1999  4.706674e+06\n",
      "2   2000  5.106000e+06\n",
      "3   2001  4.221545e+06\n",
      "4   2002  3.892386e+06\n",
      "5   2003  4.122091e+06\n",
      "6   2004  4.456805e+06\n",
      "7   2005  4.243146e+06\n",
      "8   2006  3.761902e+06\n",
      "9   2007  3.600512e+06\n",
      "10  2008  3.974927e+06\n",
      "11  2009  3.626700e+06\n",
      "12  2010  4.382350e+06\n",
      "13  2011  3.680025e+06\n",
      "14  2012  3.522675e+06\n"
     ]
    }
   ],
   "source": [
    "prod_per_year = df.groupby('year').totalprod.mean().reset_index()\n",
    "print(prod_per_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = prod_per_year['year']\n",
    "X = X.values.reshape(-1,1)\n",
    "y = prod_per_year['totalprod']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
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  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Fkp7JY3fl0rbk8rcPZfwpST2lOqsl7cvH6lK8N8vuy7rTT+ytsFH5NMvEOf/mYjGioZ9DRLEdPlbEzTrA8YxAbgL2lp7vAD4QER8EfgSsBZC0mGJJ2guAFcA9krqyzr3AGop10hflcYDrgMGIOA9YD9yRbc0B1gGXAEuAdbk2OllmfUQsAgazDTvZfJpl4nhxIutwTSUQSd3AJ4Fv1mMR8TcRMZRPnwS6c38lsCki3oyI/UA/sETSfOCMiHgiinV0HwCuLtXZmPsPA5fn6GQ5sCMiBiJikCJprchjS7MsWbfelp1MPs0ysc65Ai5/DFb+pNg6eVgHaXYEcifwBeBXb3P8M0D9pPgC4OXSsQMZW5D7jfERdTIpvQbMHaOtucCrpQRWbmsESWsk9UnqO3LkyNj/Svt1Ps1iZm9j3AQi6UrgcETsfpvjXwKGgD+rh0YpFmPEq9QZq62RwYgNEVGLiNq8efNGK2Jj8WkWM3sbzayJfhlwlaTfBd4NnCHp2xHx6ZzUvhK4PE9LQTEaWFiq3w0czHj3KPFynQOSpgFnAgMZ/1hDne8DrwCzJE3LUUi5LTvZvAa0mY1i3BFIRKyNiO6I6KGYHH8sk8cK4BbgqogonyTfCqzKK6t6KSbLd0bEIeB1SZfmHMa1wJZSnfoVVtfkawSwHVgmaXZOni8Dtuexx7MsWbfelpmZTYJmRiBv527gXcCOvBr3yYj4bEQ8J2kz8DzFqa0bImI461wP3A/MoJgzqc+b3Ac8KKmfYuSxCiAiBiTdBuzKcrdGxEDu3wJsknQ7sCfbMDOzSaK3zjxNfbVaLfr6+lrdDTOzjiJpd0TUGuP+S3QzM6vECcSmHt+7y2xSOIHY1OJ7d5lNGicQm1p87y6zSeMEYlOL791lNmmcQGxq8b27zCaNE4hNLb53l9mkcQKxqcX37jKbNCfyl+hm7cn37jKbFB6BmJlZJU4gZmZWiROImZlV4gRiZmaVOIGYmVklTiBmZlaJE4iZmVXSdAKR1CVpj6RH8vkcSTsk7cvt7FLZtZL6Jb0oaXkpfpGkZ/LYXbm0Lbn87UMZf0pST6nO6nyNfbkGez3em2X3Zd3pJ/ZWmJnZ8TieEchNwN7S8y8Cj0bEIuDRfI6kxRRL0l4ArADukdSVde4F1lCsk74ojwNcBwxGxHnAeuCObGsOsA64BFgCrCslqjuA9fn6g9mGmZlNkqYSiKRu4JPAN0vhlcDG3N8IXF2Kb4qINyNiP9APLJE0HzgjIp6IYh3dBxrq1Nt6GLg8RyfLgR0RMRARg8AOYEUeW5plG1/fzMwmQbMjkDuBLwC/KsXeGxGHAHJ7dsYXAC+Xyh3I2ILcb4yPqBMRQ8BrwNwx2poLvJplG9saQdIaSX2S+o4cOdLkP9fMJo1XkOxY4yYQSVcChyNid5NtapRYjBGvUmestkYGIzZERC0iavPmzRutiJm1ileQ7GjNjEAuA66S9BKwCVgq6dvAz/K0FLk9nOUPAAtL9buBgxnvHiU+oo6kacCZwMAYbb0CzMqyjW2ZWafwCpIdbdwEEhFrI6I7InooJscfi4hPA1uB+lVRq4Etub8VWJVXVvVSTJbvzNNcr0u6NOcwrm2oU2/rmnyNALYDyyTNzsnzZcD2PPZ4lm18fTPrFF5BsqOdyO3cvwpslnQd8FPgUwAR8ZykzcDzwBBwQ0QMZ53rgfuBGcC2fADcBzwoqZ9i5LEq2xqQdBuwK8vdGhEDuX8LsEnS7cCebMPMOsnM3uK01bTT34p5BcmOoeKX+VNDrVaLvr6+VnfDzOrqcyBd04uRx/AbxQqSXgSsrUjaHRG1xrj/Et3MWscrSHY0r0hoZq3lFSQ7lkcgZmZWiROImZlV4gRiZmaVOIFY6/gWFmYdzQnEWsO3sDDreE4g1hq+hYVZx3MCsdbwLSzMOp4TiLXGzN7ir47LfAsLs47iBGKtcf7NxS0rhn4OEcV2+FgRN7OO4ARireFbWJh1PN/KxFrHt7Aw62gegZiZWSVOIGZmVokTiJmZVTJuApH0bkk7JT0t6TlJX8n4hZKelPQDSX2SlpTqrJXUL+lFSctL8YskPZPH7sqlbcnlbx/K+FOSekp1Vkval4/VpXhvlt2XdaefnLfEzMya0cwI5E1gaUR8CLgQWCHpUuBrwFci4kLgy/kcSYsplqS9AFgB3COpK9u6F1hDsU76ojwOcB0wGBHnAeuBO7KtOcA64BJgCbAu10Yny6yPiEXAYLZhZmaTZNwEEoWj+fSd+Yh8nJHxM4GDub8S2BQRb0bEfqAfWCJpPnBGRDwRxTq6DwBXl+pszP2HgctzdLIc2BERAxExCOygSGAClmZZsm69LTMzmwRNXcabI4jdwHnAH0fEU5I+B2yX9A2KRPSRLL4AeLJU/UDGfpn7jfF6nZcBImJI0mvA3HK8oc5c4NWIGBqlrca+r6EY9XDuuec28881M7MmNDWJHhHDeaqqm2I08QHgeuDzEbEQ+DxwXxbXaE2MEa9SZ6y2Gvu+ISJqEVGbN2/eaEXMzKyC47oKKyJeBb5PMXexGvhuHvoOxRwFFKOBhaVq3RSntw7kfmN8RB1J0yhOiQ2M0dYrwKws29iWmXmtFZsEzVyFNU/SrNyfAfwO8ALFF/ZHs9hSYF/ubwVW5ZVVvRST5Tsj4hDwuqRLcw7jWmBLqU79CqtrgMdynmQ7sEzS7Jw8XwZsz2OPZ1mybr0ts1Ob11qxSdLMHMh8YGPOg7wD2BwRj0h6FfijHAX8EznPEBHPSdoMPA8MATdExHC2dT1wPzAD2JYPKE5/PSipn2LksSrbGpB0G7Ary90aEQO5fwuwSdLtwB7eOoVmdmorr7UCb233ft23jrGTSsUv86eGWq0WfX19re6G2cTa0luMPFSaKowoblq58iet65d1LEm7I6LWGPdfoptNNV5rxSaJE4jZVOO1VmySOIGYTTVea8UmidcDMZuKvNaKTQKPQMzMrBInEDMzq8QJxMys1Tr0zgFOIGZmrdTBdw5wAjEza6XynQOkYts1vYi3OScQM7NWOrofuk4bGes6DY6+1JLuHA8nEDOzVurgOwc4gZiZtVIH3znACcTMrJU6+M4B/kt0M5uaDm4rJqKP7i9OE51/c/t+KXfonQM8AjGzqaeDL43tJE4gZjb1dPClsZ2kmSVt3y1pp6SnJT0n6SulY38o6cWMf60UXyupP48tL8UvkvRMHrsrl7Yll799KONPSeop1VktaV8+VpfivVl2X9adfuJvh5mNqVP+YrqDL43tJM2MQN4ElkbEh4ALgRW5rvnHgZXAByPiAuAbAJIWUyxJewGwArgnl8MFuJdi6dtF+ViR8euAwYg4D1gP3JFtzQHWAZcAS4B1uTY6WWZ9RCwCBrMNM5sonXRaqIMvje0k4yaQKBzNp+/MR1Csb/7ViHgzyx3OMiuBTRHxZkTsB/qBJZLmA2dExBNRrKP7AHB1qc7G3H8YuDxHJ8uBHRExEBGDwA6KBCZgaZYl69bbMrOJ0EmnhTr40thO0tQciKQuST8ADlN8oT8FvB/47TyN9LeSLs7iC4CXS9UPZGxB7jfGR9SJiCHgNWDuGG3NBV7Nso1tNfZ9jaQ+SX1Hjhxp5p9rZqPppNNCHXxpbCdp6jLeiBgGLpQ0C/iepA9k3dnApcDFwGZJ/xLQaE2MEadCnbHaauz7BmADQK1WG7WMmTVhZm9x2mra6W/F2vm0UIdeGttJjusqrIh4Ffg+xdzFAeC7eYprJ/Ar4KyMLyxV6wYOZrx7lDjlOpKmAWcCA2O09QowK8s2tmVmE8GnhaxBM1dhzcuRB5JmAL8DvAD8JcU8BJLeD0yn+GLfCqzKK6t6KSbLd0bEIeD1nIAXcC2wJV9mK1C/wuoa4LGcJ9kOLJM0OyfPlwHb89jjWZasW2/LzCaCTwtZg2ZOYc0HNuaVVO8ANkfEI3nZ7LckPQscA1bnF/tzkjYDzwNDwA15CgyKiff7gRnAtnwA3Ac8KKmfYuSxCiAiBiTdBuzKcrdGxEDu3wJsknQ7sCfbMLOJ5NNCVqLiO//UUKvVoq+vr9XdMDPrKJJ2R0StMe6/RDczs0qcQMzMpqoJvnOAE4iZ2VQ0CXcOcAIxM5uKJuHOAU4gZmZT0STcOcAJxMxsKpqEG0o6gZiZTUWTcOcAJxAzs6loEu4c4DXRzcymqgm+c4BHIGZmVokTiJmZVeIEYmZmlTiBmJlZJU4gZmZWySl1O3dJR4C/r1j9LIoFszpFJ/W3k/oKndVf93XidFJ/T7Sv74uIeY3BUyqBnAhJfaPdD79ddVJ/O6mv0Fn9dV8nTif1d6L66lNYZmZWiROImZlV4gTSvA2t7sBx6qT+dlJfobP6675OnE7q74T01XMgZmZWiUcgZmZWiROImZlVckonEEnfknRY0rOl2IckPSHpGUl/JemMjE+X9KcZf1rSx0p1fj/jP5T015LOmoC+LpT0uKS9kp6TdFPG50jaIWlfbmeX6qyV1C/pRUnLS/GLsr/9ku6SpHbsq6TTJP03SS9kO189mf082f1taHNr+eeqHfuaP9MbJP0o3+Pfa+O+tt1nTNLcLH9U0t0NbbXVZ+zt+nrCn7GIOGUfwL8CPgw8W4rtAj6a+58Bbsv9G4A/zf2zgd0UCXgacBg4K499DfiPE9DX+cCHc/89wI+Axfl6X8z4F4E7cn8x8DTwLqAX+DHQlcd2Ar8JCNgGXNGOfQVOAz6eZaYD//Nk9/Vkv7d5/N8Af17+uWrHvgJfAW7P/XfUf4bbra9t/Bk7Hfgt4LPA3Q1ttdtnbNS+nuhn7KT+B3TiA+hhZAL5R966uGAh8Hzu/zHw6VK5R4ElwDuBI8D78oflT4A1k9DvLcAngBeB+aUfqhdzfy2wtlR+e/5AzwdeKMV/H/gv7djXUdr5I+AP2vW9zf2ZwP/KD/NJTyAnua8vA6dPdB9Pws9sW37GSuX+LSO/lNvuM/Z2fR2lneP6jJ3Sp7DexrPAVbn/KYokAsVvRislTZPUC1wELIyIXwLXA88ABym+OO6byA5K6gF+A3gKeG9EHALI7dlZbAHFF0TdgYwtyP3GeDv2tdzOLOBfUyTuCXMS+nsb8J+AhsWo26uv+X4C3Cbp7yR9R9J727GvbfwZezvt+Blrpp3j/ow5gfy6zwA3SNpNMTQ8lvFvUfwg9AF3Av8HGJL0Toof7t8AzgF+SPGb1ISQNBP4C+BzEfGPYxUdJRZjxE+6k9DXejvTgP8K3BURPzm5vSx14gT7K+lC4LyI+N6EdLDcgRN/b6cB3cD/jogPA08A3zjpHeWkvK/t+hl72yZGibX6MzZeO5U+Y04gDSLihYhYFhEXUbyhP874UER8PiIujIiVwCxgH3BhHv9xFGPAzcBHJqJv+UH6C+DPIuK7Gf6ZpPl5fD7FuWIokt3CUvVuit/eDuR+Y7wd+1q3AdgXEXee7H6e5P7+JnCRpJcoTmO9X9L327Sv/49ilFRPdt+hmA9sx76262fs7bTjZ2w8lT5jTiANJJ2d23cA/4HifGv9aoXTc/8TwFBEPA/8A7BYUv1OlZ8A9k5Av0QxbN8bEf+5dGgrsDr3V1OcC63HV0l6V55yWwTszGHt65IuzTavLdVpq75mW7cDZwKfO5l9nIj+RsS9EXFORPRQTFj+KCI+1qZ9DeCvgHr/Lgeeb8e+0r6fsVG16WdsrLaqf8YmcmKn3R8UI4xDwC8pfmu4DriJ4oqGHwFf5a0J9R6KCaq9wH+nuL1xvZ3PZvyHFB/KuRPQ19+iGAb/EPhBPn4XmEtxznJfbueU6nyJYgT1IqUrK4AaxVzPj4G76//GdusrxW9uke9tvZ1/187vbel4DxNzFdbJ/Dl4H/A/sq1HgXPbuK/t+hl7CRgAjlJ8hyxu48/Yr/X1RD9jvpWJmZlV4lNYZmZWiROImZlV4gRiZmaVOIGYmVklTiBmZlaJE4iZmVXiBGJmZpX8f5LV58tmjeg3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X,y, alpha = 0.8, color = 'orange')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating & Fitting Linear Regression Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Parameters**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slope and Intercept of that fitted line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-88303.18915238192\n",
      "181208083.10732976\n"
     ]
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X,y)\n",
    "print(regr.coef_[0])\n",
    "print(regr.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_predict = regr.predict(X)\n",
    "y_predict = y_predict.reshape(-1,1)\n",
    "plt.figure()\n",
    "plt.scatter(X,y, alpha = 0.8, color = 'orange')\n",
    "plt.plot(X, y_predict)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Prediction of Honey Decline**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, it looks like the production of honey has been in decline, according to this linear model. Let’s predict what the year 2050 may look like in terms of honey production.\n",
    "\n",
    "The known dataset stops at the year 2013, so I've created an array that is the range from 2013 to 2050. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_future = np.array(range(2013,2051))\n",
    "x_future = x_future.reshape(-1, 1)\n",
    "\n",
    "future_predict = regr.predict(x_future)\n",
    "future_predict = future_predict.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C12l7g52WiORAjguImZUDPgAGOecOknE5qi7QDNgJ/Ddz6Gmmu2ziuZmT3VqnBp0b45yLc87FxcTEnG6IBFn5UiX4e48mjO/bhvAw445X5vP4xOUcOKLmjCKhLEcFxMxKkFE83nLOTQRwzu1yzp1wzp0EXibjHgVknA3UCpheE9jh4zVPEz9ljplFABWB9GzW2gtU8mOzriWFVJuLo5k6sCMPdLqYdxdupWvSTJJXqzmjSKjKyVNYBrwKrHHODQuIVw8YdhOw0m9PBnr5J6vqkHGzfIFzbidwyMza+DXvBiYFzMl8wqon8IW/TzIN6Gpmlf0lsq7ANL/vSz8WPzdzLSnESpUI5/FrLuOj/u2pXKYk949L5eG3F7NXzRlFQo6drdmdmXUAZgErgMznLZ8Afk3G5SsHbAIe8EUCM/sTcC8ZT3ANcs5N9fE44A2gNDAVeMQ558ysFPAmGfdX0oFezrmNfs69/vMA/uGce93HLwbGA1HAEuA3zrlsf8rExcW51NTUsx4UCQ0/nzjJi19t4Lkv0igbGc6QGxrRvdmF+Ke/RaSAmNki51zcL+LFqVuqCkjhtH7XIf74wXKWbNnPlQ1i+MdNTbiwUulgpyVSbJypgOg30SXk1a9WngkPtuOv1zdk3sZ0uial8L95m9WcUSTIVECkUAgPM+7tUIfpg+NpVqsSf/5oJb1ense3as4oEjQqIFKo1Ioqw5t9WvHsLZezZudBug1P4cWZGzh+Qu1QRAqaCogUOmbGbS1rMSOxE50uieGZqWvpMWoOq3ccDHZqIsWKCogUWtUqlOKlu1ow6s7mfHfgJ258fjb/nb6Oo8fVDkWkIKiASKFmZlzbpDrJgzvRvVkNnvsijetGzmbR5n3BTk2kyFMBkSKhctmS/Pe2poy9txVHjp2g54tf89THq/jh6PGzTxaRXFEBkSKl0yUxTBscz91tLuL1OZtIGJ7CrPVqoimSH1RApMgpFxnBU90b8/6DbSkZEcZdry7gjxOWceBHNWcUyUsqIFJktYyNYsqAjjz0q7p8sHg7nZNm8tnK74KdlkiRoQIiRVqpEuH8sdulTOrfnphykTz4v0U89NYidh/6KdipiRR6KiBSLDSuUZFJD7fnDwkNmLFmN12GpfDBom0Up15wInlNBUSKjRLhYfS/sh5TBnSkftVy/O79ZfR+fSHb9v0Y7NRECiUVECl26lUtx3sPtOWpGxuRuimdhKQUxs3dpOaMIudIBUSKpbAwo3e7WKYPjqdFbBR/nbSK216ay4Y9h4OdmkihoQIixVrNymUYe09L/nNrU9bvPsw1I2bxwpdp/KzmjCJnpQIixZ6Z0bNFTZIT4+l8WVX+PW0dPV6Yw8rtB4KdmkhIUwER8aqWL8WoO1vw4m+as/vQUbq/MIdnP1vLTz+rOaPI6aiAiGTRrXF1ZgzuxM1X1GDUVxu4duQsUjelBzstkZCjAiJyGhXLlODftzZl3L2tOHb8JLe+NJchk1ZyWM0ZRf6PCohINuIviWHaoHh6t41l3LzNJCSlMPMbNWcUgRwUEDOrZWZfmtkaM1tlZgN9PMrMks1svX+vHDDncTNLM7N1ZpYQEG9hZiv8vpFmZj4eaWbv+vh8M4sNmNPbf8Z6M+sdEK/jx673c0vmzSEROVXZyAievLEREx5sS6kSYfR+bQG/e28Z+388FuzURIIqJ2cgx4HfOecuA9oA/c2sIfAY8Llzrj7wuf8av68X0AjoBowys3C/1migL1Dfv7r5eB9gn3OuHpAEDPVrRQFDgNZAK2BIQKEaCiT5z9/n1xDJNy0uiuLTAR15+Mp6TFq6nc7DUpi6Ymew0xIJmrMWEOfcTufcYr99CFgD1AC6A2P9sLFAD7/dHRjvnDvqnPsWSANamVl1oIJzbq7LaEA0LsuczLUmAFf7s5MEINk5l+6c2wckA938vqv82KyfL5JvSpUI5/cJDZj0cHsuqBhJv7cW8+Cbi9h9UM0Zpfg5p3sg/tLSFcB8oJpzbidkFBmgqh9WA9gaMG2bj9Xw21njp8xxzh0HDgDR2awVDez3Y7OulTXnvmaWamape/bo2rXkjUYXVuSjh9rz2DWX8uW63XQeNpP3U7eqOaMUKzkuIGZWDvgAGOScO5jd0NPEXDbx3MzJbq1Tg86Ncc7FOefiYmJiTjdEJFciwsN4sFNdpg7syKUXVOAPE5Zz92sL2Jqu5oxSPOSogJhZCTKKx1vOuYk+vMtflsK/7/bxbUCtgOk1gR0+XvM08VPmmFkEUBFIz2atvUAlPzbrWiIF6uKYcozv24anezRm8eZ9JAxP4fU533JCzRmliMvJU1gGvAqscc4NC9g1Gch8Kqo3MCkg3ss/WVWHjJvlC/xlrkNm1saveXeWOZlr9QS+8PdJpgFdzayyv3neFZjm933px2b9fJECFxZm3NXmIqYndqJVnSie+ng1t700l7Tdh4Kdmki+sbNdszWzDsAsYAWQ2WHuCTLug7wH1Aa2ALc659L9nD8B95LxBNcg59xUH48D3gBKA1OBR5xzzsxKAW+ScX8lHejlnNvo59zrPw/gH8651338YmA8EAUsAX7jnDua3fcSFxfnUlNTz35URM6Dc46Plm7nqY9X8+PREwy4uh4PdKpLiXD92pUUTma2yDkX94t4cbrppwIiBWnv4aMMmbyKT5fv5NILyvPvnk1pUrNisNMSOWdnKiD6K5FIPqlSLpIX7mjOS3e1IP2HY/QYNYdnpqo5oxQdKiAi+Syh0QUkJ3aiZ/OavDhzA9eMmMX8jd8HOy2R86YCIlIAKpYuwdCel/PWfa05fvIkt4+Zx58/WsGhn34OdmoiuaYCIlKA2terwrRB8fTpUIe35m8hISmFL9fuPvtEkRCkAiJSwMqUjOAv1zfkg37tKBsZwT1vLGTwu0tJ/0HNGaVwUQERCZLmtSvzyYAODLi6Ph8v20GXYTP5eNkOtUORQkMFRCSIIiPCSexyCR8/0oEalUvzyDtLuH/cInapOaMUAiogIiHgsuoVmNivHX+69jJmrd9D52EzGb9gi85GJKSpgIiEiIjwMO6Pv5hpg+JpdGEFHpu4gjtfmc+W79WcUUKTCohIiImtUpa372vDP29qwvJtB+g6fCavzNqo5owSclRAREJQWJhxR+vaJCfG065uFf7+6RpuGf013+xSc0YJHSogIiGsesXSvNo7jhG9mrEl/UeuGzmLETPWc+z4ybNPFslnKiAiIc7M6N6sBsmD47mmcXWSZnzDjc/PZtnW/cFOTYo5FRCRQiK6XCQjf30Fr9wdx74fj3HTqDn8c8oajhxTc0YJDhUQkUKmc8NqJCd24vaWtRmTspFuI1KYu0HNGaXgqYCIFEIVSpXgXzc34e37WwPw65fn8fjEFRxUc0YpQCogIoVYu7pV+GxgPPd3rMO7C7fQdVgKn6/ZFey0pJhQAREp5EqXDOdP1zVk4kPtqVi6BH3GpjLgnSV8fzjbf+FZ5LypgIgUEc1qVeLjRzowuPMlTF25k87DZjJp6Xa1Q5F8owIiUoSUjAhjYOf6fDqgI7WjyzJw/FLuG5vKzgNHgp2aFEEqICJF0CXVyjOxXzv+fN1lzNmwly7DUnhr/mZOqh2K5KGzFhAze83MdpvZyoDYk2a23cyW+te1AfseN7M0M1tnZgkB8RZmtsLvG2lm5uORZvauj883s9iAOb3NbL1/9Q6I1/Fj1/u5Jc//UIgULeFhxn0dM5ozXl6zIn/6cCV3vDKPTXt/CHZqUkTk5AzkDaDbaeJJzrlm/jUFwMwaAr2ARn7OKDML9+NHA32B+v6VuWYfYJ9zrh6QBAz1a0UBQ4DWQCtgiJlV9nOG+s+vD+zza4jIaVwUXZa37mvNMzc3YdX2gyQMT2FMygaOn1A7FDk/Zy0gzrkUID2H63UHxjvnjjrnvgXSgFZmVh2o4Jyb6zLu6I0DegTMGeu3JwBX+7OTBCDZOZfunNsHJAPd/L6r/Fj83My1ROQ0zIxerWqTnNiJjvVj+OeUtdwy+mvWfncw2KlJIXY+90AeNrPl/hJX5plBDWBrwJhtPlbDb2eNnzLHOXccOABEZ7NWNLDfj8261i+YWV8zSzWz1D179pz7dylShFxQsRQv392C5359Bdv2HeH6kbMZlvwNR4+rHYqcu9wWkNFAXaAZsBP4r4/baca6bOK5mZPdWr/c4dwY51yccy4uJibmTMNEig0z44amF5Kc2Ikbml7IyM/Xc8Nzs1myZV+wU5NCJlcFxDm3yzl3wjl3EniZjHsUkHE2UCtgaE1gh4/XPE38lDlmFgFUJOOS2ZnW2gtU8mOzriUiORRVtiRJtzfjtd/Gcein49w8+mue/mQ1Px47fvbJIuSygPh7GpluAjKf0JoM9PJPVtUh42b5AufcTuCQmbXx9zDuBiYFzMl8wqon8IW/TzIN6Gpmlf0lsq7ANL/vSz8WPzdzLRE5R1ddWo3pg+O5s3VtXp39LQnDU5iTtjfYaUkhkJPHeN8B5gINzGybmfUBnvWP5C4HrgQGAzjnVgHvAauBz4D+zrnMi6v9gFfIuLG+AZjq468C0WaWBiQCj/m10oGngYX+9TcfA3gUSPRzov0aIpJL5UuV4O89mvBu3zZEhIVx5yvzeXTCcg4cUXNGOTMrTm0O4uLiXGpqarDTEAlpP/18gqQZ3/ByykaqlIvk7z0a07XRBcFOS4LIzBY55+KyxvWb6CJyilIlwnn8msv4qH97osqWpO+bi3j47cXsVXNGyUIFRERO6/KaGc0Zf9flEqav2kXnYTP5cMk2NWeU/6MCIiJnVCI8jEeurs+nAzpQp0pZBr+7jHvfWMiO/WrOKCogIpID9auVZ8KD7fjr9Q2ZtzGdrkkpvDlPzRmLOxUQEcmR8DDj3g51mD44nma1KvGXj1bS6+V5bNxzONipSZCogIjIOakVVYY3+7Ti2VsuZ+3Og1wzYhYvzlRzxuJIBUREzpmZcVvLWsxI7MSvGsTwzNS19Bg1h9U71JyxOFEBEZFcq1qhFC/+pgWj7mzOdwd+4sbnZ/Pf6evUnLGYUAERkfNiZlzbpDrJgztxY7MLee6LNK4dMYtFm3P6r0BIYaUCIiJ5onLZkgy7rRlv3NOSn34+Sc8X5/Lk5FX8cFTNGYsqFRARyVO/alCVaYPjuavNRbzx9SYShqcwa73+LZ6iSAVERPJcucgI/ta9Me8/2JaSEWHc9eoC/vD+Mg78qOaMRYkKiIjkm5axUUwZ0JGHflWXiUu20zlpJp+t3BnstCSPqICISL4qVSKcP3a7lEn92xNTLpIH/7eYfv9bxO5DPwU7NTlPKiAiUiAa16jIpIfb84eEBny+djddhqUwYZGaMxZmKiAiUmBKhIfR/8p6TBnQkfpVy/H795fR+/WFbNv3Y7BTk1xQARGRAlevajnee6AtT93YiNRNGc0Zx369Sc0ZCxkVEBEJirAwo3e7WKYPjicuNoohk1dx20tz2aDmjIWGCoiIBFXNymUYe09L/nNrU9bvPsw1I2bxwpdp/KzmjCFPBUREgs7M6NmiJsmJ8XS+rCr/nraOHi/MYeX2A8FOTbKhAiIiIaNq+VKMurMFL/6mObsOHqX7C3N49rO1/PSzmjOGIhUQEQk53RpX5/PETtx8RQ1GfbWBa0fOYuEmNWcMNWctIGb2mpntNrOVAbEoM0s2s/X+vXLAvsfNLM3M1plZQkC8hZmt8PtGmpn5eKSZvevj880sNmBOb/8Z682sd0C8jh+73s8tef6HQkRCScUyJfj3rU15s08rjh0/ya0vzuWvk1ZyWM0ZQ0ZOzkDeALpliT0GfO6cqw987r/GzBoCvYBGfs4oMwv3c0YDfYH6/pW5Zh9gn3OuHpAEDPVrRQFDgNZAK2BIQKEaCiT5z9/n1xCRIqhj/RimDYrnnvaxvDlvMwlJKXy1bnew0xJyUECccylA1nPH7sBYvz0W6BEQH++cO+qc+xZIA1qZWXWggnNursv4tdNxWeZkrjUBuNqfnSQAyc65dOfcPiAZ6Ob3XeXHZv18ESmCykZGMOSGRkx4sB2lS4bz29cXkvjeUvb9cCzYqRVrub0HUs05txPAv1f18RrA1oBx23ysht/OGj9ljnPuOHAAiM5mrejrkmoAAA3nSURBVGhgvx+bda1fMLO+ZpZqZql79qiltEhh1uKiynw6oAMDrqrH5KU76JI0k0+X71Q7lCDJ65vodpqYyyaemznZrfXLHc6Ncc7FOefiYmJizjRMRAqJyIhwErs2YPLDHahesTT9317MA28uYvdBNWcsaLktILv8ZSn8e+YFyW1ArYBxNYEdPl7zNPFT5phZBFCRjEtmZ1prL1DJj826logUEw0vrMCHD7Xj8WsuZeY3e7h62EzeW7hVZyMFKLcFZDKQ+VRUb2BSQLyXf7KqDhk3yxf4y1yHzKyNv4dxd5Y5mWv1BL7w90mmAV3NrLK/ed4VmOb3fenHZv18ESlGIsLDeKBTXaYO7Mhl1Svwxw+Wc9erC9iaruaMBcHOVq3N7B3gV0AVYBcZT0Z9BLwH1Aa2ALc659L9+D8B9wLHgUHOuak+HkfGE12lganAI845Z2algDeBK8g48+jlnNvo59wLPOFT+Ydz7nUfvxgYD0QBS4DfOOeOnu2bjYuLc6mpqWc9KCJS+Jw86Xh7wRaembqWEycdf0hoQO92sYSHne6qt5wLM1vknIv7Rbw4ne6pgIgUfTv2H+GJD1fw1bo9XFG7Es/ecjn1q5UPdlqF2pkKiH4TXUSKlAsrleb137Zk+O3N+HbvD1w3cjbPfb5ezRnzgQqIiBQ5ZkaPK2owI7ETXRtV47/J33DDc7NZvm1/sFMrUlRARKTIqlIukufvaM6Yu1qQ/sMxerwwh39NWaPmjHlEBUREiryujS4gObETt8XV4qWUjXQbnsK8jd8HO61CTwVERIqFiqVL8Mwtl/PWfa054Ry9xszjTx+u4NBPPwc7tUJLBUREipX29aowbVA8fTrU4e0FW+ialMIXa3cFO61CSQVERIqdMiUj+Mv1DfmgXzvKRUZw7xupDBq/hHQ1ZzwnKiAiUmw1r12ZTwZ0YMDV9flk+U46D5vJ5GU71A4lh1RARKRYi4wIJ7HLJXz8SAdqVi7NgHeWcP+4RXx3QM0Zz0YFREQEuKx6BSb2a8cT117KrPV76DJsJu8s2KKzkWyogIiIeBHhYfSNr8u0QfE0vLACj09cwR0vz2fz9z8EO7WQpAIiIpJFbJWyvHN/G/5xU2NWbD9AwvAUXpm1kRMndTYSSAVEROQ0wsKMO1tfRHJiPO3qVuHvn67h5tFfs+67Q8FOLWSogIiIZKN6xdK82juOEb2asTX9R65/bhbDZ3zDseNqzqgCIiJyFmZG92Y1SB4cz7VNqjN8xnpueG42S7cW7+aMKiAiIjkUXS6SEb2u4NXecRw48jM3j5rDPz5dzZFjxbM5owqIiMg5uvqyakxPjKdXq9q8POtbEoan8PWGvcFOq8CpgIiI5EKFUiX4501NeOf+NpjBHS/P5/GJKzhYjJozqoCIiJyHtnWj+WxgPH3jL+bdhVvoMmwmM1YXj+aMKiAiIuepdMlwnrj2Mj58qD2Vy5TkvnGpDHhnCd8fPhrs1PKVCoiISB5pWqsSkx/uwODOlzB1ZUZzxklLtxfZdijnVUDMbJOZrTCzpWaW6mNRZpZsZuv9e+WA8Y+bWZqZrTOzhIB4C79OmpmNNDPz8Ugze9fH55tZbMCc3v4z1ptZ7/P5PkRE8krJiDAGdq7PpwM6clF0WQaOX8p9Y1PZeeBIsFPLc3lxBnKlc66Zcy7Of/0Y8Llzrj7wuf8aM2sI9AIaAd2AUWYW7ueMBvoC9f2rm4/3AfY55+oBScBQv1YUMARoDbQChgQWKhGRYLukWnk+6NeOP193GV9v+J4uw1J4a/5mThahdij5cQmrOzDWb48FegTExzvnjjrnvgXSgFZmVh2o4Jyb6zLO88ZlmZO51gTgan92kgAkO+fSnXP7gGT+f9EREQkJ4WHGfR0vZtqgeJrWqsifPlzJHa/MY9PeotGc8XwLiAOmm9kiM+vrY9WcczsB/HtVH68BbA2Yu83HavjtrPFT5jjnjgMHgOhs1voFM+trZqlmlrpnz55cfZMiIuejdnQZ/tenNUNvacKqHQdJGJ7CmJQNHD9RuNuhnG8Bae+caw5cA/Q3s/hsxtppYi6beG7nnBp0boxzLs45FxcTE5NNeiIi+cfMuL1lbWYkdiL+khj+OWUtN4/+mjU7DwY7tVw7rwLinNvh33cDH5JxP2KXvyyFf9/th28DagVMrwns8PGap4mfMsfMIoCKQHo2a4mIhLRqFUox5q4WvHBHc3bsP8INz81m2PR1HD1e+Nqh5LqAmFlZMyufuQ10BVYCk4HMp6J6A5P89mSgl3+yqg4ZN8sX+Mtch8ysjb+/cXeWOZlr9QS+8PdJpgFdzayyv3ne1cdEREKemXHd5dVJHtyJG5teyMgv0rh+5GwWb9kX7NTOyfmcgVQDZpvZMmAB8Klz7jPgGaCLma0Huvivcc6tAt4DVgOfAf2dc5kltx/wChk31jcAU338VSDazNKARPwTXc65dOBpYKF//c3HREQKjcplSzLs9ma8fk9Lfjh6nFtGf83Tn6zmx2PHg51ajlhR/QWX04mLi3OpqanBTkNE5BcOHz3Os5+tZdzczdSKKs0zN19O+3pVgp0WAGa2KOBXNf6PfhNdRCQElIuM4G/dG/PeA22JCAvjzlfm8+iE5Rw4ErrNGVVARERCSKs6UUwd2JF+v6rLhMXb6DJsJtNXfRfstE5LBUREJMSUKhHOo90u5aOH2hNdLpK+by6i/9uL2XMotJozqoCIiISoJjUrMvnh9vwhoQHJq3bRJWkmExdvC5nmjCogIiIhrER4GP2vrMeUgR24uEpZEt9bxj1vLGT7/uA3Z1QBEREpBOpVLc/7D7bjyRsasuDbdLoOm8mbczcFtTmjCoiISCERHmb8tn0dpg2Kp/lFlfnLpFX0GjOPjXsOByUfFRARkUKmVlQZxt3bin/3vJy13x2k24hZjP6q4JszqoCIiBRCZsatcbWY8btOXNWgKkM/W0uPUXNYvaPgmjOqgIiIFGJVy5fixbtaMPrO5nx34Cg3Pj+b/0xbx08/539zRhUQEZEi4Jom1ZmRGE+PK2rw/JdpXDdyFos252+LQBUQEZEiolKZkvzn1qaMu7cVP/18kp4vzuXJyav44Wj+NGdUARERKWLiL4lh+uB4ereNZezcTXRNSmHdd4fy/HNUQEREiqCykRE8eWMj3n+gLXWrlqNm5dJ5/hkReb6iiIiEjLjYKMbd2ypf1tYZiIiI5IoKiIiI5IoKiIiI5IoKiIiI5IoKiIiI5IoKiIiI5IoKiIiI5IoKiIiI5IqFyr+tWxDMbA+wOUgfXwXYG6TPPheFIU/lmDeUY94oDjle5JyLyRosVgUkmMws1TkXF+w8zqYw5Kkc84ZyzBvFOUddwhIRkVxRARERkVxRASk4Y4KdQA4VhjyVY95Qjnmj2OaoeyAiIpIrOgMREZFcUQEREZFcUQHJJTOrZWZfmtkaM1tlZgN9PMrMks1svX+v7OPRfvxhM3s+y1pfmdk6M1vqX1WDmGcXM1tkZiv8+1UBa7Xw8TQzG2lmFoI55suxzEWOrQJyWGZmNwWsFSrHMbscQ+I4Bsyr7f/f+X1ALCSO41lyDInjaGaxZnYkII8XA9bK/XF0zumVixdQHWjut8sD3wANgWeBx3z8MWCo3y4LdAAeBJ7PstZXQFyI5HkFcKHfbgxsD1hrAdAWMGAqcE0I5pgvxzIXOZYBIgLm7g74OlSOY3Y5hsRxDJj3AfA+8PtQ++/xLDmGxHEEYoGVZ1gr18cxT7+p4vwCJgFdgHVA9YA/5HVZxv2WAiwguc3Txw34Hoj0Y9YG7Ps18FIo5ViQx/Icc6wD7CLjn5AO1eP4fzmG2nEEegD/Bp7M/OEcasfxdDmG0nHkDAXkfI+jLmHlATOLJeNvxfOBas65nQD+PaenrK/7U8u/5NWpeB7keQuwxDl3FKgBbAvYt83HQinHTPl6LHOao5m1NrNVwArgQefccULsOJ4hx0xBP45mVhZ4FHgqy/SQOY7Z5Jgp6MfRq2NmS8xsppl19LHzOo4qIOfJzMqRceo6yDl3MJfL3OmcawJ09K+78iq/TOeap5k1AoYCD2SGTjMsT58Bz4McIZ+P5bnk6Jyb75xrBLQEHjezUoTYcTxDjhA6x/EpIMk5dzjrEqcZG6zjeKYcIXSO406gtnPuCiAReNvMKnCex1EF5DyYWQky/vDecs5N9OFdZlbd78+8rpwt59x2/34IeBtoFcw8zawm8CFwt3Nugw9vA2oGLFsT2BFiOebrscztn7dzbg3wAxn3a0LqOJ4hx1A6jq2BZ81sEzAIeMLMHia0juOZcgyZ4+icO+qc+95vLwI2AJdwnsdRBSSX/Knoq8Aa59ywgF2Tgd5+uzcZ1yazWyfCzKr47RLA9cDKYOVpZpWAT4HHnXNzMgf70+FDZtbGr3k3Z/neCjrH/DyWucixjplF+O2LgAbAphA7jqfNMZSOo3Ouo3Mu1jkXCwwH/umcez6UjuOZcgyl42hmMWYW7rcvBuoDG8/7OOb3zZ2i+iLjiSoHLAeW+te1QDTwObDev0cFzNkEpAOHyaj8Dcl4OmuRX2cVMAIID1aewJ/J+Jvo0oBXVb8vjoz/ATYAz+M7GYRKjvl5LHOR410+h6XAYqBHwFqhchxPm2MoHccsc5/k1BvUIXEcz5RjKB1HMu4VrgKW+T/rG/LiOKqViYiI5IouYYmISK6ogIiISK6ogIiISK6ogIiISK6ogIiISK6ogIiISK6ogIiISK78P0pKB/T0HQ2bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(x_future, future_predict)\n",
    "plt.show()"
   ]
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