Canada per capita testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13fb3014",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1ea241d2",
   "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>PCI</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           PCI\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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Canada per capita testing.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7ca14f81",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('PCI(US$)')\n",
    "plt.scatter(df.year,df.PCI, color='red', marker='+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3b5479f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = linear_model.LinearRegression()\n",
    "reg.fit(df[['year']],df.PCI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "046c8244",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nisch\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([41288.69409442])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[2020]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6754dffc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([828.46507522])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c6fd6155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1632210.7578554575"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f69dfad0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41288.694088942604"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "828.46507522*2020+-1632210.7578554575"
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}