Practice Work.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>year</th>\n",
       "      <th>per capita income (US$)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970</td>\n",
       "      <td>3399.299037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1971</td>\n",
       "      <td>3768.297935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1972</td>\n",
       "      <td>4251.175484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1973</td>\n",
       "      <td>4804.463248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1974</td>\n",
       "      <td>5576.514583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1975</td>\n",
       "      <td>5998.144346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1976</td>\n",
       "      <td>7062.131392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1977</td>\n",
       "      <td>7100.126170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1978</td>\n",
       "      <td>7247.967035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1979</td>\n",
       "      <td>7602.912681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1980</td>\n",
       "      <td>8355.968120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1981</td>\n",
       "      <td>9434.390652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1982</td>\n",
       "      <td>9619.438377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1983</td>\n",
       "      <td>10416.536590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1984</td>\n",
       "      <td>10790.328720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1985</td>\n",
       "      <td>11018.955850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1986</td>\n",
       "      <td>11482.891530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1987</td>\n",
       "      <td>12974.806620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1988</td>\n",
       "      <td>15080.283450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1989</td>\n",
       "      <td>16426.725480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1990</td>\n",
       "      <td>16838.673200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1991</td>\n",
       "      <td>17266.097690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1992</td>\n",
       "      <td>16412.083090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1993</td>\n",
       "      <td>15875.586730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1994</td>\n",
       "      <td>15755.820270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1995</td>\n",
       "      <td>16369.317250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1996</td>\n",
       "      <td>16699.826680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1997</td>\n",
       "      <td>17310.757750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1998</td>\n",
       "      <td>16622.671870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1999</td>\n",
       "      <td>17581.024140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2000</td>\n",
       "      <td>18987.382410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2001</td>\n",
       "      <td>18601.397240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2002</td>\n",
       "      <td>19232.175560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2003</td>\n",
       "      <td>22739.426280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2004</td>\n",
       "      <td>25719.147150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2005</td>\n",
       "      <td>29198.055690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2006</td>\n",
       "      <td>32738.262900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2007</td>\n",
       "      <td>36144.481220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2008</td>\n",
       "      <td>37446.486090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2009</td>\n",
       "      <td>32755.176820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2010</td>\n",
       "      <td>38420.522890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2011</td>\n",
       "      <td>42334.711210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2012</td>\n",
       "      <td>42665.255970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2013</td>\n",
       "      <td>42676.468370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2014</td>\n",
       "      <td>41039.893600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2015</td>\n",
       "      <td>35175.188980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2016</td>\n",
       "      <td>34229.193630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  per capita income (US$)\n",
       "0   1970              3399.299037\n",
       "1   1971              3768.297935\n",
       "2   1972              4251.175484\n",
       "3   1973              4804.463248\n",
       "4   1974              5576.514583\n",
       "5   1975              5998.144346\n",
       "6   1976              7062.131392\n",
       "7   1977              7100.126170\n",
       "8   1978              7247.967035\n",
       "9   1979              7602.912681\n",
       "10  1980              8355.968120\n",
       "11  1981              9434.390652\n",
       "12  1982              9619.438377\n",
       "13  1983             10416.536590\n",
       "14  1984             10790.328720\n",
       "15  1985             11018.955850\n",
       "16  1986             11482.891530\n",
       "17  1987             12974.806620\n",
       "18  1988             15080.283450\n",
       "19  1989             16426.725480\n",
       "20  1990             16838.673200\n",
       "21  1991             17266.097690\n",
       "22  1992             16412.083090\n",
       "23  1993             15875.586730\n",
       "24  1994             15755.820270\n",
       "25  1995             16369.317250\n",
       "26  1996             16699.826680\n",
       "27  1997             17310.757750\n",
       "28  1998             16622.671870\n",
       "29  1999             17581.024140\n",
       "30  2000             18987.382410\n",
       "31  2001             18601.397240\n",
       "32  2002             19232.175560\n",
       "33  2003             22739.426280\n",
       "34  2004             25719.147150\n",
       "35  2005             29198.055690\n",
       "36  2006             32738.262900\n",
       "37  2007             36144.481220\n",
       "38  2008             37446.486090\n",
       "39  2009             32755.176820\n",
       "40  2010             38420.522890\n",
       "41  2011             42334.711210\n",
       "42  2012             42665.255970\n",
       "43  2013             42676.468370\n",
       "44  2014             41039.893600\n",
       "45  2015             35175.188980\n",
       "46  2016             34229.193630"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('canada_per_capita_income.csv', delimiter=',')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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fAq4COshGZXlklplZUo8O95oFkYi4F5haJP2kEvkDmF9i22JgcZH0HuCowZXUzKw9je/sYFORgDGUHe6+Y93MrE3V4+58TwVvZtam6rFGioOImbWlcgtPNcPx6qXWd+c7iJhZ2+lv4alGH6+duE/EzNrOUC885YWsSnMQMbO2M9RDW72QVWkOImbWdoZ6LikvZFWag4iZtZ2hHtrargtZDQV3rJtZ2xnqoa31GCrbqpTdKD58dHd3R09PT6OLYWbWUiStiYjuwnQ3Z5mZWdUcRMzMrGoOImZmVjUHETMzq5qDiJmZVc1BxMzMqlazICJpX0l3S/qZpHWSLkjpkyXdJWmDpOsl7Z3S90mve9P2SXnHOi+lr5d0cl76zJTWK2lBrc7FzMyKq2VN5BngpIj4Y+AYYGZaD/0S4NKImAI8Bpyd8p8NPBYRrwIuTfmQdCQwB3gtMBP4iqQRadndy4FTgCOBM1NeMzOrk5oFkcg8lV6OSo8ATgJuTOlLgNnp+az0mrT9TyUppS+NiGci4pdka7Afnx69EfFwRDwLLE15zcysTmraJ5JqDPcAW4EVwEPAjojYlbL0Abl5A7qAjQBp++PAwfnpBfuUSi9WjnmSeiT1bNu2bShOzczMqPHcWRGxGzhGUifwLeA1xbKlnyqxrVR6sQBYdA6XiFgELIJs2pN+im1mLaJVVxtsJ3WZgDEidkhaBZwIdEoamWobE4DNKVsfMBHokzQSeBmwPS89J3+fUulm1ua82mBzqOXorLGpBoKkDuAtwIPA7cA7U7a5wC3p+bL0mrR9ZWSzQy4D5qTRW5OBKcDdwGpgShrttTdZ5/uyWp2PmTUXrzbYHGpZExkHLEmjqPYCboiIb0t6AFgq6bPAWuDKlP9K4BpJvWQ1kDkAEbFO0g3AA8AuYH5qJkPSOcByYASwOCLW1fB8zKyJeLXB5lCzIBIR9wJTi6Q/TDayqjD9D8C7ShzrIuCiIum3ArcOurBm1nLGd3awqUjA8GqD9eU71s2sJXm1webglQ3NrCV5tcHm4CBiZi1r9tQuB40Gc3OWmZlVzUHEzMyq5iBiZmZVcxAxM7OqOYiYmVnVHETMzKxqDiJmZlY1BxEzM6uag4iZmVXNQcTMzKrmaU/MrOl5BcPmVVFNRNKrJd0m6f70+nWS/qm2RTMze3EFw007dhK8uILhzWs3NbpoRuXNWV8FzgOegxfWCplTq0KZmeV4BcPmVmkQ2S8i7i5I21VuB0kTJd0u6UFJ6yR9JKV/WtImSfekx6l5+5wnqVfSekkn56XPTGm9khbkpU+WdJekDZKuT8vkmlkb8QqGza3SIPKopP8FBICkdwJb+tlnF/DxiHgNcCIwX9KRadulEXFMetyajnkkWe3mtcBM4CuSRqTldS8HTgGOBM7MO84l6VhTgMeAsys8HzNrEaVWKvQKhs2h0iAyH/gP4AhJm4CPAh8qt0NEbImIn6bnTwIPAuV6wmYBSyPimYj4JdBLtozu8UBvRDwcEc8CS4FZkgScBNyY9l8CzK7wfMysRXgFw+ZWURBJH+BvAcYCR0TEGyLiV5W+iaRJZOut35WSzpF0r6TFkkantC5gY95ufSmtVPrBwI6I2FWQXuz950nqkdSzbdu2SottZk1g9tQuLj79aLo6OxDQ1dnBxacf7dFZTaKiIb6SOoGzgEnAyKwSABHx4Qr2PQC4CfhoRDwh6QrgQrKmsQuBLwLvB1Rk96B4oIsy+fdMjFgELALo7u4umsfMmpdXMGxeld4ncitwJ3Af8HylB5c0iiyAfD0ivgkQEY/kbf8q8O30sg+YmLf7BGBzel4s/VGgU9LIVBvJz29mZnVQaRDZNyL+biAHTn0WVwIPRsSX8tLHRUSuU/4dwP3p+TLgG5K+BIwHpgB3k9U4pkiaDGwi63x/d0SEpNuBd5L1k8wFbhlIGc3MbHAqDSLXSPogWa3hmVxiRGwvs8804L3AfZLuSWmfJBtddQxZ09OvgL9Ox1on6QbgAbKRXfMjYjeApHOA5cAIYHFErEvH+wSwVNJngbVkQcvMzOpEEf13EUiaD1wE7ODFfoeIiFfWsGw10d3dHT09PY0uhplZS5G0JiK6C9MrrYn8HfCqiHh0aItlZmatrNL7RNYBT9eyIGZm1noqrYnsBu5JHdn5fSL9DvE1M7P2VWkQuTk9zMxqwtO9t6aKgkhELEmTG746Ja2PiOdqVywzG05y073nZuvNTfcOOJA0uUrXE5kObCCbCPErwC8kvbGG5TKzYcTTvbeuSpuzvgjMiIj1kC1SBVwHHFergpnZ8OHp3ltXpaOzRuUCCEBE/AIYVZsimdlw4+neW1elQaRH0pWSpqfHV4E1tSyYmQ0fnu69dVXanPUhsjVFPkw2l9UPyfpGzMwGLdd57tFZrafSaU/2B/6QN5fVCGCfiGi5GxA97YmZ2cCVmvak0uas24D8xskO4PtDUTAzM2tdlQaRfSPiqdyL9Hy/2hTJzMxaRaVB5PeSjs29kHQc4LF3ZmbDXKUd6x8F/ktSbuXAccAZtSmSmZm1ikqnPVkt6QjgcLLRWT/3tCdmZlZpcxbA64HXAVPJVic8q1xmSRMl3S7pQUnrJH0kpY+RtELShvRzdEqXpMsk9Uq6t6D5bG7Kv0HS3Lz04yTdl/a5LC3Ja2ZmdVLp3FnXAF8A3kAWTF4P7DHUq8Au4OMR8RrgRGC+pCOBBcBtETGFbNTXgpT/FLJ11acA84Ar0nuPAc4HTgCOB87PBZ6UZ17efjMrOR8zMxsalfaJdANHRiU3lSQRsQXYkp4/KelBoAuYBUxP2ZYAq8jWSp8FXJ3e405JnZLGpbwrcuu5S1oBzJS0CjgoIn6S0q8GZgPfrbSMZmY2OJU2Z90P/FG1byJpElkz2F3Ay1OAyQWaQ1O2LmBj3m59Ka1cel+R9GLvP09Sj6Sebdu2VXsaZmZWoNKayCHAA5Lu5qUrG57W346SDgBuAj4aEU+U6bYotiGqSN8zMWIRsAiyO9b7K7OZ1Y4Xn2ovlQaRT1dzcEmjyALI1yPimyn5EUnjImJLaq7amtL7gIl5u08ANqf06QXpq1L6hCL5zawOqgkGXnyq/VTUnBURPyj2KLdPGil1JfBgRHwpb9MyIDfCai5wS176WWmU1onA46m5azkwQ9Lo1KE+A1ietj0p6cT0XmflHcvMaigXDDbt2EnwYjC4ee2msvt58an2U7YmIulJijcRCYiIOKjM7tOA9wL3SbonpX0SWAjcIOls4DfAu9K2W4FTgV7gaeB9ZG+yXdKFwOqU7zO5Tnay2YWvIpvL67u4U92sLsoFg9lTu0rWUrz4VPspG0Qi4sBqDxwRP6J4vwXAnxbJH2TTzRc71mJgcZH0HuCoastoZtUpFwzKNVmN7+xgU5F9vfhU6xrIzYZmZkD5lQjL1VK8+FT7cRAxswErFwzK1VJmT+3i4tOPpquzAwFdnR1cfPrR7lRvYZWOzjIze0G5lQg/v3x92Sar2VO7HDTaiIOImVWlVDA49+TDX9InAm6yamcOImZW1kDvB/F66cOLg4jZEGr2u7EHWr5qbw50k9Xw4Y51syFS7Q149VJN+XxzoPXHQcRsgG5eu4lpC1cyecF3mLZw5Qsfws3+gVtN+XxzoPXHzVlmA1CueadZPnCruVu81D6+OdD64yBiVkKxD9Zy3+bLfeDWq6+kmrvFX9YxquQ+Hmll/XFzllkRpfoPin0IQ/ZtvtQNeG8+YuyQ95VU06RWqnwSZefB8s2BVo5rImZFlPowHiGxu8gCn+M7O0oObe1vssKBqrZJrVT5Pnb9PSX3AY+0svIcRMyKKPVhvDuCjlEjSjbvFPvA7e9DeqBNXdU2qZUqX393mJuV4+YssyJKfYDmmnMG0rxTbrLC/obdFmu2KlfbqGaCQ0+KaIPhmohZEeU6lAfavFPuWP0Nuy3WbNW53ygee/q5Pd6nXJOa7zC3WlEUad9tZ93d3dHT09PoYlgLGMoRVaWONXnBd0qu+laqaaqzYxTP7Hp+j6DkDm+rJUlrIqJ7j/RaBRFJi4G3A1sj4qiU9mngg8C2lO2TEXFr2nYecDawG/hwRCxP6TOBfwFGAP8ZEQtT+mRgKTAG+Cnw3oh4tr9yOYhYvkZPUzJt4cqigaKrs4PNqYmrkIBLzzjGNQerq1JBpJZ9IlcBM4ukXxoRx6RHLoAcCcwBXpv2+YqkEZJGAJcDpwBHAmemvACXpGNNAR4jC0BmFWuGaUrK9UeU60uZPbWLOxacxC8Xvo07FpzkAGINU7MgEhE/BLb3mzEzC1gaEc9ExC/J1lk/Pj16I+LhVMtYCsySJOAk4Ma0/xJg9pCegLW9ZpimpNx9GO7wtlbQiI71cySdBfQAH4+Ix4Au4M68PH0pDWBjQfoJwMHAjojYVSS/WUWaZZqSUh317vC2VlDvIHIFcCEQ6ecXgfeTNfMWCorXlKJM/qIkzQPmARx22GEDK7G1rVaYF8o3+lmzq+t9IhHxSETsjojnga+SNVdBVpOYmJd1ArC5TPqjQKekkQXppd53UUR0R0T32LFjh+ZkrOW5uchs8OoaRCSNy3v5DuD+9HwZMEfSPmnU1RTgbmA1MEXSZEl7k3W+L4tsSNntwDvT/nOBW+pxDtY+PC+U2eDVrDlL0nXAdOAQSX3A+cB0SceQNT39CvhrgIhYJ+kG4AFgFzA/Inan45wDLCcb4rs4Italt/gEsFTSZ4G1wJW1OhdrX24uMhsc32xoZmb9asR9ImZm1uYcRMzMrGqegNHaXqOnNjFrZw4i1tbKLeDkQGI2eG7OsrbWDFObmLUzBxFra80ytYlZu3IQsbZWbiZcMxs8BxFra57axKy23LFubc0z4ZrVloOItY1SQ3k9tYlZ7TiIWFvwUF6zxnCfiLUFD+U1awzXRKyllGqy8lBes8ZwELGmVCxYACWbrFphlUKzduQgYk2nVP/GvqP2Ktlkde7Jh79kH/BQXrN6cBCxhinVNFWqf6MwLWfzjp0eymvWIA4i1hDlRlMNtB8j12Tlobxm9Vez0VmSFkvaKun+vLQxklZI2pB+jk7pknSZpF5J90o6Nm+fuSn/Bklz89KPk3Rf2ucySarVudjQKzeaqlQ/RmfHKN99btZkajnE9ypgZkHaAuC2iJgC3JZeA5wCTEmPecAVkAUdsrXZTwCOB87PBZ6UZ17efoXvZU2s3GiqUlOVfPq013Lx6UfT1dmBgK7ODi4+/WjXPswaqGbNWRHxQ0mTCpJnAdPT8yXAKuATKf3qyBZ8v1NSp6RxKe+KiNgOIGkFMFPSKuCgiPhJSr8amA18t1bnY9Up1e9RbjRVf/0bDhpmzaPefSIvj4gtABGxRdKhKb0L2JiXry+llUvvK5JelKR5ZLUWDjvssEGeghUz0CG5/Y2mcv+GWWtolo71Yv0ZUUV6URGxCFgE0N3dXTKfVaeaIbl3LDgJ8Ggqs1ZX7yDyiKRxqRYyDtia0vuAiXn5JgCbU/r0gvRVKX1CkfzWANUMyQXXNszaQb3nzloG5EZYzQVuyUs/K43SOhF4PDV7LQdmSBqdOtRnAMvTticlnZhGZZ2Vdyyrs2qH5JpZ66tZTUTSdWS1iEMk9ZGNsloI3CDpbOA3wLtS9luBU4Fe4GngfQARsV3ShcDqlO8zuU524ENkI8A6yDrU3aneIKU6yTs7RvHMrud9F7lZG1M2IGr46O7ujp6enkYXoyWVGmlV2CcCWbC4+PSjAfd7mLUDSWsiorswvVk61q3JVbJeh4fkmg0/DiJWkXJ3mHv1QLPhy4tSWUW8XoeZFeOaiO2hWN+H1+sws2JcE7GXyPV9bNqxk+DFvo83HzHWkx+a2R5cE2kD5UZNlersHuhaHrf/fBsXn360R1qZ2Us4iLS4UqOmen69nZvWbCo6mgpKz2lVru/DnedmVshBpMWVqjlcd9dGdhfcA5QbTZV7Xmyb+z7MbCDcJ9LiStUcCgNIfv5q1vJw34eZFeMg0uJK1RBGlFjocXxnR8l9cmt5eOEnM6uUm7NaXKl1Of78uK6X9Ink0out81G4zX0fZlYpB5EWUWo0VbkpR7pfMabsaCqPtDKzwfIEjC2g3ASH/uA3s3rwBIwtYKD3buTmrTVCUnEAAAkISURBVDIzaxQHkSZRbpZcz1tlZs3Ko7OaRLnaRrnRVGZmjdSQICLpV5Luk3SPpJ6UNkbSCkkb0s/RKV2SLpPUK+leScfmHWduyr9B0txS79cKfO+GmbWiRjZnvTkiHs17vQC4LSIWSlqQXn8COAWYkh4nAFcAJ0gaQ7bkbjcQwBpJyyLisXqeRDUGOktuf4s+mZk1SjP1icwiW5MdYAmwiiyIzAKujmwY2Z2SOiWNS3lX5NZcl7QCmAlcV99iD0ypvo/+7uvwvRtm1owa1ScSwP+TtEbSvJT28ojYApB+HprSu4CNefv2pbRS6XuQNE9Sj6Sebdu2DeFpDFx/s+T6TnEzayWNqolMi4jNkg4FVkj6eZm8xebviDLpeyZGLAIWQXafyEALW41Sw3U9S66ZtZOGBJGI2Jx+bpX0LeB44BFJ4yJiS2qu2pqy9wET83afAGxO6dML0lfVuOgVKTdc17Pkmlk7qXtzlqT9JR2Yew7MAO4HlgG5EVZzgVvS82XAWWmU1onA46m5azkwQ9LoNJJrRkqrq5vXbmLawpVMXvAdpi1c+UINpNRwXY+0MrN20oiayMuBbymbZXYk8I2I+J6k1cANks4GfgO8K+W/FTgV6AWeBt4HEBHbJV0IrE75PpPrZK+XUjWOwgCSk2uyAo+0MrP24LmzKlCqf2PawpVFm6ZGSEXX8+jq7OCOBSdVXXYzs0bx3FlVqmY6kt0RdIwaUXK4rplZu/C0J/2oZjqS3PBcD9c1s3bnmkg/yg3JvfSMY0ou7uThumY2HLgm0g8vJWtmVpprIv0otfyspyMxM3MQ6ZeH5JqZleYgUgHXNszMinOfiJmZVc1BxMzMquYgYmZmVXMQMTOzqjmImJlZ1YbdBIyStgG/rnL3Q4BH+83V/nwdMr4OGV+HF7XztXhFRIwtTBx2QWQwJPUUm8VyuPF1yPg6ZHwdXjQcr4Wbs8zMrGoOImZmVjUHkYFZ1OgCNAlfh4yvQ8bX4UXD7lq4T8TMzKrmmoiZmVXNQcTMzKo2rIOIpMWStkq6Py/tjyX9RNJ9kv5b0kEp/T2S7sl7PC/pmLTtuJS/V9JlktSoc6rWAK/FKElLUvqDks7L22empPXpWixoxLkMxgCvw96SvpbSfyZpet4+Lf03IWmipNvT73edpI+k9DGSVkjakH6OTulK59kr6V5Jx+Yda27Kv0HS3EadUzWquA5HpL+VZyT9fcGxWvp/o6SIGLYP4I3AscD9eWmrgTel5+8HLiyy39HAw3mv7wb+BBDwXeCURp9bLa8F8G5gaXq+H/ArYBIwAngIeCWwN/Az4MhGn1sNr8N84Gvp+aHAGmCvdvibAMYBx6bnBwK/AI4EPgcsSOkLgEvS81PTeQo4EbgrpY8BHk4/R6fnoxt9fjW8DocCrwcuAv4+7zgt/79R6jGsayIR8UNge0Hy4cAP0/MVwJ8X2fVM4DoASeOAgyLiJ5H9tVwNzK5NiWtngNcigP0ljQQ6gGeBJ4Djgd6IeDgingWWArNqXfahNMDrcCRwW9pvK7AD6G6Hv4mI2BIRP03PnwQeBLrIfp9LUrYlvHhes4CrI3Mn0Jmuw8nAiojYHhGPkV2/mXU8lUEZ6HWIiK0RsRp4ruBQLf+/UcqwDiIl3A+clp6/C5hYJM8ZpCBC9gfVl7etL6W1g1LX4kbg98AW4DfAFyJiO9l5b8zbv12uRanr8DNglqSRkiYDx6VtbfU3IWkSMBW4C3h5RGyB7AOW7Js3lP7dt83fRIXXoZS2uQ6FHET29H5gvqQ1ZNXXZ/M3SjoBeDoicm3mxdq622XcdKlrcTywGxgPTAY+LumVtO+1KHUdFpN9GPQAXwZ+DOyija6DpAOAm4CPRsQT5bIWSYsy6S1lANeh5CGKpLXcdSjGy+MWiIifAzMAJL0aeFtBljm8WAuB7ENkQt7rCcDmWpaxXspci3cD34uI54Ctku4Ausm+aeXX3NriWpS6DhGxC/hYLp+kHwMbgMdog78JSaPIPji/HhHfTMmPSBoXEVtSc9XWlN5H8d99HzC9IH1VLcs91AZ4HUopdX1anmsiBSQdmn7uBfwT8O952/Yia85YmktLVdknJZ2YRuCcBdxS10LXSJlr8RvgpDQiZ3+yjtSfk3VAT5E0WdLeZAF3Wf1LPrRKXQdJ+6XzR9JbgV0R8UA7/E2kcl8JPBgRX8rbtAzIjbCay4vntQw4K/1NnAg8nq7DcmCGpNFpBNOMlNYSqrgOpbTl/wYw7EdnXUfWrv8c2TeFs4GPkI3A+AWwkHRXf8o/HbizyHG6ydrNHwL+LX+fVnkM5FoABwD/BawDHgDOzTvOqSn/Q8A/Nvq8anwdJgHryTpbv082VXZb/E0AbyBrbrkXuCc9TgUOJhtMsCH9HJPyC7g8ne99QHfesd4P9KbH+xp9bjW+Dn+U/m6eIBto0Uc2yKLl/zdKPTztiZmZVc3NWWZmVjUHETMzq5qDiJmZVc1BxMzMquYgYmZmVXMQMRtC6T6JH0k6JS/tLyR9r5HlMqsVD/E1G2KSjiK7j2Yq2eyt9wAzI+KhQRxzZGR3yJs1FQcRsxqQ9DmySSr3B56MiAvTWhrzyaYC/zFwTkQ8L2kR2fTzHcD1EfGZdIw+4D/IZr39MtlUGR8kuxHyvoj4yzqfltkePHeWWW1cAPyUbLLG7lQ7eQfwvyNiVwocc4BvkK1LsT1NrX+7pBsj4oF0nN9HxDQASVvI7op/VlJn3c/IrAgHEbMaiIjfS7oeeCoinpH0FrLFinrSIocdvDg1+JmSzib7fxxPtk5JLohcn3fYdcC1km4Bbq7DaZj1y0HErHaeTw/I5pZaHBGfys8gaQrZ3FzHR8QOSdcC++Zl+X3e85OBN5EtZvRPko6KiN01K71ZBTw6y6w+vg/8haRDACQdLOkw4CDgSeCJvJUA9yBpBDAhIlYC5wJjyZYmNmso10TM6iAi7pN0AfD9NKX8c8DfkC1o9QDZjL8PA3eUOMRI4BuSDiT78ndJZMu1mjWUR2eZmVnV3JxlZmZVcxAxM7OqOYiYmVnVHETMzKxqDiJmZlY1BxEzM6uag4iZmVXt/wOY6g2Jxk4YLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"Years \")\n",
    "plt.ylabel(\"Income\")\n",
    "plt.title(\"Year wise Income In CANADA\")\n",
    "plt.scatter(df['year'], df['per capita income (US$)'])\n",
    "plt.show()"
   ]
  },
  {
   "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>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year\n",
       "0   1970\n",
       "1   1971\n",
       "2   1972\n",
       "3   1973\n",
       "4   1974\n",
       "5   1975\n",
       "6   1976\n",
       "7   1977\n",
       "8   1978\n",
       "9   1979\n",
       "10  1980\n",
       "11  1981\n",
       "12  1982\n",
       "13  1983\n",
       "14  1984\n",
       "15  1985\n",
       "16  1986\n",
       "17  1987\n",
       "18  1988\n",
       "19  1989\n",
       "20  1990\n",
       "21  1991\n",
       "22  1992\n",
       "23  1993\n",
       "24  1994\n",
       "25  1995\n",
       "26  1996\n",
       "27  1997\n",
       "28  1998\n",
       "29  1999\n",
       "30  2000\n",
       "31  2001\n",
       "32  2002\n",
       "33  2003\n",
       "34  2004\n",
       "35  2005\n",
       "36  2006\n",
       "37  2007\n",
       "38  2008\n",
       "39  2009\n",
       "40  2010\n",
       "41  2011\n",
       "42  2012\n",
       "43  2013\n",
       "44  2014\n",
       "45  2015\n",
       "46  2016"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.drop(columns='per capita income (US$)', inplace=False)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating Instance of Linear Regression Model\n",
    "model = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit The Data As per the requirments \n",
    "model = model.fit(new_df, df['per capita income (US$)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[828.46507522]\n",
      "-1632210.7578554575\n"
     ]
    }
   ],
   "source": [
    "# Show COEF and INTERCEPT\n",
    "\n",
    "print(model.coef_)\n",
    "\n",
    "print(model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([41288.69409442])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finnaly Prediction The Values \n",
    "print(model.predict([[2020]]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Improt pickle to save model in file\n",
    "\n",
    "import pickle "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save model to file as binary format\n",
    "\n",
    "with open('model_saved', 'wb') as file:\n",
    "    pickle.dump(model, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Again Load Saved Model\n",
    "\n",
    "with open('model_saved', 'rb') as fl:\n",
    "    mdl = pickle.load(fl)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[828.46507522]\n",
      "-1632210.7578554575\n"
     ]
    }
   ],
   "source": [
    "# Print Again COEF and INTERCEPT\n",
    "\n",
    "print(model.coef_)\n",
    "\n",
    "print(model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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