exercise.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "31b10c16-91b9-456a-a8c8-bea88baf6757",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "75927c4a-4f89-419e-951f-da4ff7b8e9be",
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"canada_per_capita_income.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0fa7463f-c69c-4728-a554-fbaa56255743",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x28447a45010>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('per capita income(US$)')\n",
    "plt.scatter(df.year, df['per capita income (US$)'], color='red', marker='+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e15abf03-1648-4fb4-b649-2054c16bdfb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg=linear_model.LinearRegression()\n",
    "reg.fit(df[['year']],df['per capita income (US$)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "819dd840-0dc8-4bdf-9036-f2e8fd736fef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([44602.55439531])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[2024]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4549d67c-3510-4771-8dac-753224778e01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([828.46507522])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3bfb9008-7a7d-4776-aa24-53460d3a2a68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x284479da6d0>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('per capita income(US$)')\n",
    "plt.scatter(df.year, df['per capita income (US$)'], color='red', marker='+')\n",
    "plt.plot(df.year,reg.predict(df[['year']]),color='blue')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}