Exp6_regression.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": [
    {
     "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>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",
       "  </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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"C:\\\\Users\\\\Tejas Pundpal\\\\Documents\\\\ML\\\\simple regression\\\\Exercise\\\\canada_per_capita_income.csv\")\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['year', 'income '], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.rename(columns={'income ':'income'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2b5e0d59190>"
      ]
     },
     "execution_count": 13,
     "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='+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = linear_model.LinearRegression()\n",
    "reg.fit(df[['year']],df.income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4007.7657094])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[1975]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([828.46507522])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1632210.7578554575"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4007.765704042511"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "828.46507522*1975-1632210.7578554575"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24719.39258996])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[2000]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pd.read_csv(\"C:\\\\Users\\\\Tejas Pundpal\\\\Documents\\\\ML\\\\simple regression\\\\years.csv\")\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "p=reg.predict(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "d['income']=p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.to_csv(\"income_prediction.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2b5e0cd3bb0>]"
      ]
     },
     "execution_count": 32,
     "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('years')\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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8150.09108551])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[1980]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([41288.69409442])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[2020]])"
   ]
  },
  {
   "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": 4
}