ExampleRegression.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",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"C:\\\\Users\\\\Admin\\\\Desktop\\\\simple regression\\\\Exercise\\\\canada_per_capita_income.csv\")"
   ]
  },
  {
   "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>year</th>\n",
       "      <th>income</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       income \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"
   ]
  },
  {
   "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>year</th>\n",
       "      <th>income</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",
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      ],
      "text/plain": [
       "   year      income \n",
       "0  1970  3399.299037\n",
       "1  1971  3768.297935\n",
       "2  1972  4251.175484"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "%matplotlib inline\n",
    "plt.xlabel(\"year\")\n",
    "plt.ylabel(\"income\")\n",
    "plt.scatter(df.year,df.income,color=\"red\",marker=\"+\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
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     "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": [
    "%matplotlib inline\n",
    "plt.xlabel(\"year\")\n",
    "plt.ylabel(\"income\")\n",
    "plt.scatter(df.year,df[\"income \"],color=\"red\",marker=\"+\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg=linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(df[[\"year\"]],df[\"income \"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\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([39631.76394397])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[2018]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([828.46507522])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1632210.7578554575"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39631.76393850264"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2018*828.46507522+(-1632210.7578554575)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=pd.read_excel(\"C:\\\\Users\\\\Admin\\\\Desktop\\\\simple regression\\\\Exercise\\\\Book1.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year\n",
       "0   2017\n",
       "1   2018\n",
       "2   2019\n",
       "3   2020\n",
       "4   2021\n",
       "5   2022\n",
       "6   2023\n",
       "7   2024\n",
       "8   2025\n",
       "9   2026\n",
       "10  2027\n",
       "11  2028\n",
       "12  2029\n",
       "13  2030"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "p=reg.predict(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([38803.29886875, 39631.76394397, 40460.22901919])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict(d.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "d[\"income\"]=p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>38803.298869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>39631.763944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>40460.229019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>41288.694094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021</td>\n",
       "      <td>42117.159170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022</td>\n",
       "      <td>42945.624245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2023</td>\n",
       "      <td>43774.089320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024</td>\n",
       "      <td>44602.554395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2025</td>\n",
       "      <td>45431.019471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2026</td>\n",
       "      <td>46259.484546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2027</td>\n",
       "      <td>47087.949621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2028</td>\n",
       "      <td>47916.414696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2029</td>\n",
       "      <td>48744.879771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2030</td>\n",
       "      <td>49573.344847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year        income\n",
       "0   2017  38803.298869\n",
       "1   2018  39631.763944\n",
       "2   2019  40460.229019\n",
       "3   2020  41288.694094\n",
       "4   2021  42117.159170\n",
       "5   2022  42945.624245\n",
       "6   2023  43774.089320\n",
       "7   2024  44602.554395\n",
       "8   2025  45431.019471\n",
       "9   2026  46259.484546\n",
       "10  2027  47087.949621\n",
       "11  2028  47916.414696\n",
       "12  2029  48744.879771\n",
       "13  2030  49573.344847"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.to_excel(\"Example1.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2a6f9e93730>]"
      ]
     },
     "execution_count": 38,
     "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": [
    "%matplotlib inline\n",
    "plt.xlabel(\"year\")\n",
    "plt.ylabel(\"income\")\n",
    "plt.scatter(df.year,df[\"income \"],color=\"red\",marker=\"+\")\n",
    "plt.plot(df.year,reg.predict(df[[\"year\"]]),color=\"blue\")"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}