population_to_co2_ml.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the csv files into a pandas DataFrame\n",
    "data_p = pd.read_csv('data/population_data.csv')\n",
    "data_c = pd.read_csv('data/co2_ppm.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Year</th>\n",
       "      <th>population_millions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1993</td>\n",
       "      <td>5581597.598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1994</td>\n",
       "      <td>5663150.428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1995</td>\n",
       "      <td>5744212.930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1996</td>\n",
       "      <td>5824891.931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1997</td>\n",
       "      <td>5905045.647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Year  population_millions\n",
       "0           0  1993          5581597.598\n",
       "1           1  1994          5663150.428\n",
       "2           2  1995          5744212.930\n",
       "3           3  1996          5824891.931\n",
       "4           4  1997          5905045.647"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview the population dataset\n",
    "data_p.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Year</th>\n",
       "      <th>CO2_concentration_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1993</td>\n",
       "      <td>357.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1994</td>\n",
       "      <td>358.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1995</td>\n",
       "      <td>360.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1996</td>\n",
       "      <td>362.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1997</td>\n",
       "      <td>363.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Year  CO2_concentration_per_million\n",
       "0           0  1993                         357.10\n",
       "1           1  1994                         358.83\n",
       "2           2  1995                         360.82\n",
       "3           3  1996                         362.61\n",
       "4           4  1997                         363.73"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview the CO2 dataset\n",
    "data_c.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0_x</th>\n",
       "      <th>Year</th>\n",
       "      <th>population_millions</th>\n",
       "      <th>Unnamed: 0_y</th>\n",
       "      <th>CO2_concentration_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1993</td>\n",
       "      <td>5581597.598</td>\n",
       "      <td>0</td>\n",
       "      <td>357.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1994</td>\n",
       "      <td>5663150.428</td>\n",
       "      <td>1</td>\n",
       "      <td>358.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1995</td>\n",
       "      <td>5744212.930</td>\n",
       "      <td>2</td>\n",
       "      <td>360.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1996</td>\n",
       "      <td>5824891.931</td>\n",
       "      <td>3</td>\n",
       "      <td>362.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1997</td>\n",
       "      <td>5905045.647</td>\n",
       "      <td>4</td>\n",
       "      <td>363.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1998</td>\n",
       "      <td>5984794.075</td>\n",
       "      <td>5</td>\n",
       "      <td>366.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1999</td>\n",
       "      <td>6064239.033</td>\n",
       "      <td>6</td>\n",
       "      <td>368.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>2000</td>\n",
       "      <td>6143493.806</td>\n",
       "      <td>7</td>\n",
       "      <td>369.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>2001</td>\n",
       "      <td>6222626.531</td>\n",
       "      <td>8</td>\n",
       "      <td>371.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>2002</td>\n",
       "      <td>6301773.172</td>\n",
       "      <td>9</td>\n",
       "      <td>373.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>6381185.141</td>\n",
       "      <td>10</td>\n",
       "      <td>375.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>2004</td>\n",
       "      <td>6461159.391</td>\n",
       "      <td>11</td>\n",
       "      <td>377.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>2005</td>\n",
       "      <td>6541906.956</td>\n",
       "      <td>12</td>\n",
       "      <td>379.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>2006</td>\n",
       "      <td>6623517.917</td>\n",
       "      <td>13</td>\n",
       "      <td>381.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>2007</td>\n",
       "      <td>6705946.643</td>\n",
       "      <td>14</td>\n",
       "      <td>383.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>2008</td>\n",
       "      <td>6789088.672</td>\n",
       "      <td>15</td>\n",
       "      <td>385.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>2009</td>\n",
       "      <td>6872766.988</td>\n",
       "      <td>16</td>\n",
       "      <td>387.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>2010</td>\n",
       "      <td>6956823.588</td>\n",
       "      <td>17</td>\n",
       "      <td>389.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>2011</td>\n",
       "      <td>7041194.168</td>\n",
       "      <td>18</td>\n",
       "      <td>391.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>2012</td>\n",
       "      <td>7125827.957</td>\n",
       "      <td>19</td>\n",
       "      <td>393.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>2013</td>\n",
       "      <td>7210582.041</td>\n",
       "      <td>20</td>\n",
       "      <td>396.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>2014</td>\n",
       "      <td>7295290.759</td>\n",
       "      <td>21</td>\n",
       "      <td>398.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>2015</td>\n",
       "      <td>7379796.967</td>\n",
       "      <td>22</td>\n",
       "      <td>400.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0_x  Year  population_millions  Unnamed: 0_y  \\\n",
       "0              0  1993          5581597.598             0   \n",
       "1              1  1994          5663150.428             1   \n",
       "2              2  1995          5744212.930             2   \n",
       "3              3  1996          5824891.931             3   \n",
       "4              4  1997          5905045.647             4   \n",
       "5              5  1998          5984794.075             5   \n",
       "6              6  1999          6064239.033             6   \n",
       "7              7  2000          6143493.806             7   \n",
       "8              8  2001          6222626.531             8   \n",
       "9              9  2002          6301773.172             9   \n",
       "10            10  2003          6381185.141            10   \n",
       "11            11  2004          6461159.391            11   \n",
       "12            12  2005          6541906.956            12   \n",
       "13            13  2006          6623517.917            13   \n",
       "14            14  2007          6705946.643            14   \n",
       "15            15  2008          6789088.672            15   \n",
       "16            16  2009          6872766.988            16   \n",
       "17            17  2010          6956823.588            17   \n",
       "18            18  2011          7041194.168            18   \n",
       "19            19  2012          7125827.957            19   \n",
       "20            20  2013          7210582.041            20   \n",
       "21            21  2014          7295290.759            21   \n",
       "22            22  2015          7379796.967            22   \n",
       "\n",
       "    CO2_concentration_per_million  \n",
       "0                          357.10  \n",
       "1                          358.83  \n",
       "2                          360.82  \n",
       "3                          362.61  \n",
       "4                          363.73  \n",
       "5                          366.70  \n",
       "6                          368.38  \n",
       "7                          369.55  \n",
       "8                          371.14  \n",
       "9                          373.28  \n",
       "10                         375.80  \n",
       "11                         377.52  \n",
       "12                         379.80  \n",
       "13                         381.90  \n",
       "14                         383.79  \n",
       "15                         385.60  \n",
       "16                         387.43  \n",
       "17                         389.90  \n",
       "18                         391.65  \n",
       "19                         393.85  \n",
       "20                         396.52  \n",
       "21                         398.65  \n",
       "22                         400.83  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_table = pd.merge(data_p, data_c, on=\"Year\", how=\"left\")\n",
    "merge_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population_millions</th>\n",
       "      <th>CO2_concentration_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5581597.598</td>\n",
       "      <td>357.10</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5663150.428</td>\n",
       "      <td>358.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5744212.930</td>\n",
       "      <td>360.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5824891.931</td>\n",
       "      <td>362.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5905045.647</td>\n",
       "      <td>363.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5984794.075</td>\n",
       "      <td>366.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6064239.033</td>\n",
       "      <td>368.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6143493.806</td>\n",
       "      <td>369.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6222626.531</td>\n",
       "      <td>371.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6301773.172</td>\n",
       "      <td>373.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6381185.141</td>\n",
       "      <td>375.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6461159.391</td>\n",
       "      <td>377.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6541906.956</td>\n",
       "      <td>379.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6623517.917</td>\n",
       "      <td>381.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6705946.643</td>\n",
       "      <td>383.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6789088.672</td>\n",
       "      <td>385.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6872766.988</td>\n",
       "      <td>387.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6956823.588</td>\n",
       "      <td>389.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7041194.168</td>\n",
       "      <td>391.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7125827.957</td>\n",
       "      <td>393.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7210582.041</td>\n",
       "      <td>396.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7295290.759</td>\n",
       "      <td>398.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7379796.967</td>\n",
       "      <td>400.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    population_millions  CO2_concentration_per_million\n",
       "0           5581597.598                         357.10\n",
       "1           5663150.428                         358.83\n",
       "2           5744212.930                         360.82\n",
       "3           5824891.931                         362.61\n",
       "4           5905045.647                         363.73\n",
       "5           5984794.075                         366.70\n",
       "6           6064239.033                         368.38\n",
       "7           6143493.806                         369.55\n",
       "8           6222626.531                         371.14\n",
       "9           6301773.172                         373.28\n",
       "10          6381185.141                         375.80\n",
       "11          6461159.391                         377.52\n",
       "12          6541906.956                         379.80\n",
       "13          6623517.917                         381.90\n",
       "14          6705946.643                         383.79\n",
       "15          6789088.672                         385.60\n",
       "16          6872766.988                         387.43\n",
       "17          6956823.588                         389.90\n",
       "18          7041194.168                         391.65\n",
       "19          7125827.957                         393.85\n",
       "20          7210582.041                         396.52\n",
       "21          7295290.759                         398.65\n",
       "22          7379796.967                         400.83"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df = merge_table[['population_millions', 'CO2_concentration_per_million']]\n",
    "data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape:  (23, 1) (23, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[5581597.598],\n",
       "       [5663150.428],\n",
       "       [5744212.93 ],\n",
       "       [5824891.931],\n",
       "       [5905045.647],\n",
       "       [5984794.075],\n",
       "       [6064239.033],\n",
       "       [6143493.806],\n",
       "       [6222626.531],\n",
       "       [6301773.172],\n",
       "       [6381185.141],\n",
       "       [6461159.391],\n",
       "       [6541906.956],\n",
       "       [6623517.917],\n",
       "       [6705946.643],\n",
       "       [6789088.672],\n",
       "       [6872766.988],\n",
       "       [6956823.588],\n",
       "       [7041194.168],\n",
       "       [7125827.957],\n",
       "       [7210582.041],\n",
       "       [7295290.759],\n",
       "       [7379796.967]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data_df.population_millions.values.reshape(-1, 1)\n",
    "y = data_df.CO2_concentration_per_million.values.reshape(-1, 1)\n",
    "\n",
    "print(\"Shape: \", X.shape, y.shape)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'CO2 Parts per Million')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(X, y) \n",
    "plt.xlabel(\"Population Millions\")\n",
    "plt.ylabel(\"CO2 Parts per Million\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weight coefficients:  [[2.43860039e-05]]\n",
      "y-axis intercept:  [220.33437679]\n"
     ]
    }
   ],
   "source": [
    "print('Weight coefficients: ', model.coef_)\n",
    "print('y-axis intercept: ', model.intercept_) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min X Value: [[5581597.598]]\n",
      "Max X Value: [[7379796.967]]\n"
     ]
    }
   ],
   "source": [
    "x_min = np.array([[X.min()]])\n",
    "x_max = np.array([[X.max()]])\n",
    "print(f\"Min X Value: {x_min}\")\n",
    "print(f\"Max X Value: {x_max}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the predicted y_min and y_max\n",
    "y_min = model.predict(x_min)\n",
    "y_max = model.predict(x_max)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "# Plot the original X and y data using plt.scatter\n",
    "# Plot the model fit line using [x_min[0], x_max[0]], [y_min[0], y_max[0]]\n",
    "plt.scatter(X, y, c='blue')\n",
    "plt.plot([x_min[0], x_max[0]], [y_min[0], y_max[0]], c='red')\n",
    "plt.savefig('images/population_to_co2.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True output: [357.1]\n",
      "Predicted output: [356.44723785]\n",
      "Prediction Error: [-0.65276215]\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X)\n",
    "print(f\"True output: {y[0]}\")\n",
    "print(f\"Predicted output: {predictions[0]}\")\n",
    "print(f\"Prediction Error: {predictions[0]-y[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.32296105901039446\n",
      "R2: 0.9971831758912286\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "mse = mean_squared_error(y_test, predicted)\n",
    "r2 = r2_score(y_test, predicted)\n",
    "print(f\"Mean Squared Error: {mse}\")\n",
    "print(f\"R2: {r2}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}