Linear Regression Single Variable- Problem statement.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing required packages\n",
    "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": 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$)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970</td>\n",
       "      <td>3399.299037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1971</td>\n",
       "      <td>3768.297935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1972</td>\n",
       "      <td>4251.175484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1973</td>\n",
       "      <td>4804.463248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1974</td>\n",
       "      <td>5576.514583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  per capita income (US$)\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"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf=pd.read_csv('canada_per_capita_income.csv')\n",
    "pf.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1829dc3ed68>"
      ]
     },
     "execution_count": 3,
     "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.scatter(pf.year,pf[['per capita income (US$)']],color='red',marker='+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1971</td>\n",
       "      <td>3768.297935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1972</td>\n",
       "      <td>4251.175484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1973</td>\n",
       "      <td>4804.463248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1974</td>\n",
       "      <td>5576.514583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1975</td>\n",
       "      <td>5998.144346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  per_capita_income\n",
       "0  1971        3768.297935\n",
       "1  1972        4251.175484\n",
       "2  1973        4804.463248\n",
       "3  1974        5576.514583\n",
       "4  1975        5998.144346"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_csv = pd.read_csv('canada_per_capita_income.csv', names=['year', 'per_capita_income' ], header=1)\n",
    "\n",
    "df_csv.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_csv1=df_csv.to_csv('canada_per_capita_income_new.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1971</td>\n",
       "      <td>3768.297935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1972</td>\n",
       "      <td>4251.175484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1973</td>\n",
       "      <td>4804.463248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1974</td>\n",
       "      <td>5576.514583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1975</td>\n",
       "      <td>5998.144346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  per_capita_income\n",
       "0  1971        3768.297935\n",
       "1  1972        4251.175484\n",
       "2  1973        4804.463248\n",
       "3  1974        5576.514583\n",
       "4  1975        5998.144346"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_csv1 = pd.read_csv('canada_per_capita_income_new.csv')\n",
    "df_csv1.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1829dcfc400>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3gr88lRcQEeFZfM1seHiG3pZV5HLWZWQjqFYyxDvVzcysvRVJIpsj4vqa18TMOo/XDGl5RZLIEkn/AfwC+Pud6hFxW81qZWZmLaFIEjkmPefHBwfZDLpmZtXra3G4BdKyiozOenM9KmJmZq1nwCQi6X9GxI8lnVPu8/x8WGZmO8UtkJZVqSWyT3p+cT0qYmZmrWfAJBIR30vPX6hfdczMrJUUmcXXzMysLCcRMzOrmpOImZlVrch9Ikh6G3AYsGdfLCLOq1WlzMysNRRZHvdS4DTg42STL76HbDLGwbYbI2mJpNWSVkk6O8VHSFos6f70fECKS9K3JPVIukvSkbl9TU3l75c0NRc/Kq2S2JO21ZDPgJmZVa3I5aw3RsSZwMY0UusfgDEFttsGfCoiXgscC8yQNBGYBdwUEROAm9J7gJPJ1lWfAEwnTT0vaQQwh+zO+aOBOX2JJ5WZnttucoF6mZnZMCmSRP6anp+W9DLgWWD8YBtFxNq++bUiYgvZ+uijgCnAvFRsHnBqej0FmB+ZZcD+kkYCJwGLI2JDWt99MTA5fbZfRNySlu6dn9uXmZnVQZEkcq2k/YH/AG4DHgYWDOWHSBoHHAEsBw6JiLWQJRrg4FRsFPBobrPeFKsU7y0TL/fzp0vqltS9fv36oVTdzMwqKNKx/pWI2ApcLelass71vxX9AZL2Ba4GPhERT1Totij3QVQR3zEYMReYC1AqlcqWMbM68ESLbadIS+SWvhcRsTUiNudjlUjanSyB/CQifpHCj6VLUaTndSneywv7WkYDawaJjy4TNzOzOqk0AeNLyS4P7SXpCLb/5b8fsPdgO04jpS4DVvebrHERMBX4cnq+JhefKWkBWSf65ohYK+kG4Eu5zvQTgdkRsUHSFknHkl0mOxP4dpGDNrOdNNQWhRefaluVLmedBHyA7C/8fBLYAvxrgX0fB7wfuFvSHSn2r2TJY6GkacAjZEOGAa4DTgF6gKeBswBSsjgfWJHKnRcRG9LrjwJXAHsB16eHmZnVibKBTRUKSO+KiKvrVJ+aK5VK0d3d3ehqmLWm/i2K44/PnvMtikqtDLdAWpaklRFR6h8fdD0RYFy5NUW8noiZmRVZT2TfelTEzFpApeVsi/R7uAXSdryeiJmZVW3Q+0QkHQp8k2zqkiAb3vvJiHiwxnUzs2ZVrkVRqZVibavIfSI/BRYCI4GXAT8HflbLSplZE5g0aXtCMBtAkTvWFRE/yr3/saSZtaqQWctphb+861nHZj4PNuyKJJElkmaRzZcVZNPC/780uy65ezbMrB34xkAbgiJJ5LT0/OF+8Q+SJZVDh7VGZq2iFb5sW6GO1tIGTSIRMei072ZtrxW+fIerju4gtyEoujzu64CJvHB53Pm1qpRZS2iFL9sidWzm+lvTKzLEdw4wiSyJXEe2AuFvyRaBMmsv/b9Qd/Zy0HB/QVd7k181nFSsgCItkXcDrwduj4izJB0C/KC21TJrIa3wZVupBeL+EtsJRZLIXyPieUnbJO1Htv6HO9OtvQz2hVqLqc+Hss9K+2uFy2rWtookke60PO73gZXAk8CtNa2VWSerVzJw8rFhUGR01sfSy0sl/QrYLyLuqm21zOpssC/UoX7B7uxEhUOt32Dbm9VIkY71dwI3R8TmiHhY0v6STo2I/1OH+pl1jkb1UTj52E4ocjlrTkT8su9NRGxKI7acRKw11fOv+aFMVFhknip/4VuTKTIBY7kyRVowl0taJ+meXOzzkv4s6Y70OCX32WxJPZLuk3RSLj45xXrS9Ct98fGSlku6X9KVkvYocCxmzauvk/z447NHvtPcrEkV7Vj/OnAx2TQnHyfrYB/MFcB32PF+kosi4qv5gKSJwOnAYWQzBd8o6VXp44uBtwK9wApJiyLiXuDCtK8Fki4FpgGXFKiXdapmG9LqBGFtoEhL5OPAM8CVZFPC/xWYMdhGEfEboOjkjFOABRGxNSIeAnqAo9OjJyIejIhnyCaBnCJJwAnAVWn7ecCpBX+WWXNzC8RaSJHRWU8BswYrNwQzJZ0JdAOfioiNwChgWa5Mb4oBPNovfgxwILApIraVKb8DSdOB6QBjx44djmOwVuQhrWbDrkhLZDhdArwCOBxYC3wtxVWmbFQRLysi5kZEKSJKXV1dQ6uxmZkNqNAEjMMlIh7rey3p+8C16W0vMCZXdDSwJr0uF38c2F/Sbqk1ki9vVplbIGbDpmJLRNKukj45XD9M0sjc23cCfSO3FgGnS3qRpPHABLK74lcAE9JIrD3IOt8XRUQAS8jm9QKYClwzXPU0M7NiKrZEIuI5SVOAi4a6Y0k/I5v99yBJvcAcYJKkw8kuPT1MWugqIlZJWgjcC2wDZkTEc2k/M4EbgF2ByyNiVfoR5wILJH0RuB24bKh1NDOznaPsj/oKBaQLgJeQjc56qi8eEbfVtmq1USqVoru7u9HVMDNrKZJWRkSpf7xIn8gb0/N5uViQDbE1M7MOVmSI75vrURGzYeVhvGZ1MegQX0mHSLpM0vXp/URJ02pfNTMza3ZFLmddAfwQ+Lf0/o9k/SPuyLbm02xTm5i1uSI3Gx4UEQuB5wHSfRnP1bRWZmbWEoq0RJ6SdCDpjnBJxwKba1ors2p5ahOzuiqSRM4huxnwFZJ+B3Sx/SY/MzPrYEVGZ90m6Xjg1WRzVt0XEc/WvGZmO8MtELO6KLK41J7Ax4A3kV3S+i9Jl0bE32pdOTMza25FLmfNB7YA307vzwB+BLynVpUyK8T9HmYNVySJvDoiXp97v0TSnbWqkJmZtY4iSeR2ScdGxDIASccAv6tttcxy+rc4fC+IWdMokkSOAc6U9Eh6PxZYLeluICLiv9esdtY5nAjMWlKRJDK55rUwK2ewFocTj1nDFRni+6d6VMQ6QLkvfV+aMmtpdV0e12xIBmtxONGYNVzNkoiky4G3A+si4nUpNoJs8sZxZCsbvjciNkoS8E3gFOBp4AN9i15Jmgp8Nu32ixExL8WPIpscci/gOuDsGGyFLWuMSq0NX5oya2lFJmCs1hXs2J8yC7gpIiYAN6X3ACeTras+AZgOXAJ/TzpzyDr3jwbmSDogbXNJKtu3nftu2lU+2ZhZU6lZSyQifiNpXL/wFLJ11wHmAUvJ1kqfAsxPLYllkvaXNDKVXRwRGwAkLQYmS1oK7BcRt6T4fOBU4PpaHY8VVK5FUaS14SRh1pJq2RIp55CIWAuQng9O8VHAo7lyvSlWKd5bJl6WpOmSuiV1r1+/fqcPwsgSQl9SMLOO1Swd6yoTiyriZUXEXGAuQKlUcr9JLRQZZeXWhlnbqXcSeUzSyIhYmy5XrUvxXmBMrtxoYE2KT+oXX5rio8uUt1obKFmYWUeqdxJZBEwFvpyer8nFZ0paQNaJvjklmhuAL+U6008EZkfEBklb0gJZy4Ez2T5BpDWCR1mZdaRaDvH9GVkr4iBJvWSjrL4MLJQ0DXiE7TMBX0c2vLeHbIjvWQApWZwPrEjlzuvrZAc+yvYhvtfjTvX6cLIwsxx12q0VpVIpuru7G12N1uckYtZRJK2MiFL/eLN0rFsz8pBcMxtEvYf4mplZG3FLxHbkSRHNrCC3RDqdbxo0s53glojtyCOwzKwgJ5FO4HU8zKxGnERaTaUv++FOBE4oZjYIJ5F25nU8zKzGnERaRaWE4PmszKxBnETamdfxMLMacxJpFZUSgi9NmVmDOIl0AicVM6sRJ5FmVO3lJycLM6sz37FuZmZVc0ukUXwDoJm1AbdEzMysag1piUh6GNgCPAdsi4iSpBHAlcA44GHgvRGxUZKAb5KtfPg08IGIuC3tZyrw2bTbL0bEvHoeR1V8A6CZtZFGtkTeHBGH51bKmgXcFBETgJvSe4CTgQnpMR24BCAlnTlka7IfDczJrcVuZmZ10Ex9IlPI1mQHmAcsBc5N8fmRreO7TNL+kkamsov71lyXtBiYDPysvtUeRP9WhW8ANLM20qiWSAD/KWmlpOkpdkhErAVIzwen+Cjg0dy2vSk2UNzMzOqkUS2R4yJijaSDgcWS/lChrMrEokJ8xx1kiWo6wNixY4da18FVM9LKrQ0zawMNaYlExJr0vA74JVmfxmPpMhXpeV0q3guMyW0+GlhTIV7u582NiFJElLq6uobzUMzMOlrdWyKS9gF2iYgt6fWJwHnAImAq8OX0fE3aZBEwU9ICsk70zRGxVtINwJdyneknArPreCgeaWVmHa8Rl7MOAX6ZjdxlN+CnEfErSSuAhZKmAY8A70nlryMb3ttDNsT3LICI2CDpfGBFKndeXyd7TTgZmJntQNmgp85RKpWiu7t76BvWc0VBM7MmI2ll7paMv2umIb7NyVORmJkNyElkODihmFmHchIZjDvIzcwG5AkYzcysam6JFOUWiJnZDtwSMTOzqjmJmJlZ1ZxEzMysak4iZmZWNScRMzOrmpOImZlVrePmzpK0HvhThSIHAY/XqTqtwOdjO5+LF/L5eKF2Px8vj4gd1tLouCQyGEnd5SYZ61Q+H9v5XLyQz8cLder58OUsMzOrmpOImZlVzUlkR3MbXYEm4/Oxnc/FC/l8vFBHng/3iZiZWdXcEjEzs6o5iZiZWdU6IolIulzSOkn35GKvl3SLpLsl/V9J+6X4P0u6I/d4XtLh6bOjUvkeSd+SpEYdU7WGeC52lzQvxVdLmp3bZrKk+9K5mNWIYxkOQzwfe0j6YYrfKWlSbpt2+N0YI2lJ+rdeJensFB8habGk+9PzASmudKw9ku6SdGRuX1NT+fslTW3UMe2MKs7Ha9LvzVZJn+63r7b4/1JWRLT9A/hH4EjgnlxsBXB8ev1B4Pwy2/034MHc+1uBfwAEXA+c3Ohjq+W5AN4HLEiv9wYeBsYBuwIPAIcCewB3AhMbfWx1OB8zgB+m1wcDK4Fd2uh3YyRwZHr9YuCPwETgK8CsFJ8FXJhen5KOVcCxwPIUHwE8mJ4PSK8PaPTx1eF8HAy8AbgA+HRuP23z/6XcoyNaIhHxG2BDv/Crgd+k14uBd5XZ9AzgZwCSRgL7RcQtkf1mzAdOrU2Na2eI5yKAfSTtBuwFPAM8ARwN9ETEgxHxDLAAmFLrutfCEM/HROCmtN06YBNQaqPfjbURcVt6vQVYDYwi+7edl4rNY/uxTQHmR2YZsH86FycBiyNiQ0RsJDuHk+t4KMNiqOcjItZFxArg2X67apv/L+V0RBIZwD3AO9Lr9wBjypQ5jZREyH55enOf9aZYOxjoXFwFPAWsBR4BvhoRG8iO+9Hc9u10LmDg83EnMEXSbpLGA0elz9rud0PSOOAIYDlwSESsheyLlewvbhj496Dtfj8Kno+BtN35yOvkJPJBYIaklWRN1WfyH0o6Bng6IvqulZe7xt0u46MHOhdHA88BLwPGA5+SdCjtfS5g4PNxOdkXQDfwDeD3wDba7HxI2he4GvhERDxRqWiZWFSIt6QhnI8Bd1Em1rLno7+OXWM9Iv4AnAgg6VXA2/oVOZ3trRDIvjxG596PBtbUso71UuFcvA/4VUQ8C6yT9DugRPZXVb7l1jbnAgY+HxGxDfhkXzlJvwfuBzbSJr8bknYn+8L8SUT8IoUfkzQyItamy1XrUryX8r8HvcCkfvGltax3rQzxfAxkoPPUFjq2JSLp4PS8C/BZ4NLcZ7uQXcZY0BdLzdYtko5NI2/OBK6pa6VrpMK5eAQ4IY3C2Yes8/QPZB3PEySNl7QHWcJdVP+a18ZA50PS3uk8IOmtwLaIuLddfjdS3S8DVkfE13MfLQL6RlhNZfuxLQLOTL8fxwKb07m4AThR0gFp5NKJKdZSqjgfA2nr/y8N79mvx4OsRbGWrMOrF5gGnE022uKPwJdJd++n8pOAZWX2UyK7Xv4A8J38Nq3yGMq5APYFfg6sAu4FPpPbzymp/APAvzX6uOp0PsYB95F1sN5INjV2O/1uvInsMstdwB3pcQpwINmAgvvT84hUXsDF6ZjvBkq5fX0Q6EmPsxp9bHU6Hy9Nv0NPkA266CUbcNE2/1/KPTztiZmZVa1jL2eZmdnOcxIxM7OqOYmYmVnVnETMzKxqTiJmZlY1JxGzGkr3UPxW0sm52Hsl/aqR9TIbLh7ia1Zjkl5Hdr/NEduLH1cAAAFPSURBVGQzut4BTI6IB3Zin7tFdge9WUM5iZjVgaSvkE1muQ+wJSLOT+tszCCbHvz3wMyIeF7SXLLp6fcCroyI89I+eoHvkc2I+42I+HkDDsXsBTp27iyzOvsCcBvZZI6l1Dp5J/DGiNiWEsfpwE/J1qrYkKbgXyLpqoi4N+3nqYg4rhEHYFaOk4hZHUTEU5KuBJ6MiK2S3kK2gFF3WgRxL7ZPF36GpGlk/z9fRraOSV8SubK+NTerzEnErH6eTw/I5p26PCL+d76ApAlkc3cdHRGbJP0Y2DNX5Km61NSsII/OMmuMG4H3SjoIQNKBksYC+wFbgCdyqwSaNS23RMwaICLulvQF4MY05fyzwEfIFry6l2xG4AeB3zWulmaD8+gsMzOrmi9nmZlZ1ZxEzMysak4iZmZWNScRMzOrmpOImZlVzUnEzMyq5iRiZmZV+/+Mxikv4CC/eAAAAABJRU5ErkJggg==\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('per capita income (US$)')\n",
    "plt.scatter(df_csv1.year,df_csv1[['per_capita_income']],color='red',marker='+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1971</td>\n",
       "      <td>3768.297935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1972</td>\n",
       "      <td>4251.175484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1973</td>\n",
       "      <td>4804.463248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1974</td>\n",
       "      <td>5576.514583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1975</td>\n",
       "      <td>5998.144346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  per_capita_income\n",
       "0  1971        3768.297935\n",
       "1  1972        4251.175484\n",
       "2  1973        4804.463248\n",
       "3  1974        5576.514583\n",
       "4  1975        5998.144346"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_csv1.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg=linear_model.LinearRegression()\n",
    "reg.fit(df_csv1[['year']], df_csv1.per_capita_income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([41483.31239984])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict(2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([838.70814393])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1652707.138337022"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41483.31240157806"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y= m*x+c\n",
    "# y=\n",
    "838.70814393*2020 -1652707.138337022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year\n",
       "0  2016\n",
       "1  2017\n",
       "2  2018\n",
       "3  2019\n",
       "4  2020"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_csv1 = pd.read_csv('Predict_canada_per_capita_income.csv' )\n",
    "df_csv1.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([38128.47982412, 38967.18796805, 39805.89611198, 40644.60425591,\n",
       "       41483.31239984, 42322.02054377, 43160.7286877 , 43999.43683163,\n",
       "       44838.14497556, 45676.85311949, 46515.56126341, 47354.26940734,\n",
       "       48192.97755127, 49031.6856952 , 49870.39383913, 50709.10198306,\n",
       "       51547.81012699, 52386.51827092, 53225.22641485])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict(df_csv1)"
   ]
  },
  {
   "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",
       "      <th>predict_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>38128.479824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>38967.187968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>39805.896112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>40644.604256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>41483.312400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021</td>\n",
       "      <td>42322.020544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2022</td>\n",
       "      <td>43160.728688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2023</td>\n",
       "      <td>43999.436832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2024</td>\n",
       "      <td>44838.144976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2025</td>\n",
       "      <td>45676.853119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2026</td>\n",
       "      <td>46515.561263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2027</td>\n",
       "      <td>47354.269407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2028</td>\n",
       "      <td>48192.977551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2029</td>\n",
       "      <td>49031.685695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2030</td>\n",
       "      <td>49870.393839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2031</td>\n",
       "      <td>50709.101983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2032</td>\n",
       "      <td>51547.810127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2033</td>\n",
       "      <td>52386.518271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2034</td>\n",
       "      <td>53225.226415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  predict_income\n",
       "0   2016    38128.479824\n",
       "1   2017    38967.187968\n",
       "2   2018    39805.896112\n",
       "3   2019    40644.604256\n",
       "4   2020    41483.312400\n",
       "5   2021    42322.020544\n",
       "6   2022    43160.728688\n",
       "7   2023    43999.436832\n",
       "8   2024    44838.144976\n",
       "9   2025    45676.853119\n",
       "10  2026    46515.561263\n",
       "11  2027    47354.269407\n",
       "12  2028    48192.977551\n",
       "13  2029    49031.685695\n",
       "14  2030    49870.393839\n",
       "15  2031    50709.101983\n",
       "16  2032    51547.810127\n",
       "17  2033    52386.518271\n",
       "18  2034    53225.226415"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_csv1['predict_income']=reg.predict(df_csv1)\n",
    "df_csv1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Writing data to csv file\n",
    "df_csv1.to_csv('Predict_canada_per_capita_income_new.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>predict_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>38128.479824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>38967.187968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>39805.896112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>40644.604256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>41483.312400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021</td>\n",
       "      <td>42322.020544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2022</td>\n",
       "      <td>43160.728688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2023</td>\n",
       "      <td>43999.436832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2024</td>\n",
       "      <td>44838.144976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2025</td>\n",
       "      <td>45676.853119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2026</td>\n",
       "      <td>46515.561263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2027</td>\n",
       "      <td>47354.269407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2028</td>\n",
       "      <td>48192.977551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2029</td>\n",
       "      <td>49031.685695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2030</td>\n",
       "      <td>49870.393839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2031</td>\n",
       "      <td>50709.101983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2032</td>\n",
       "      <td>51547.810127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2033</td>\n",
       "      <td>52386.518271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2034</td>\n",
       "      <td>53225.226415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  predict_income\n",
       "0   2016    38128.479824\n",
       "1   2017    38967.187968\n",
       "2   2018    39805.896112\n",
       "3   2019    40644.604256\n",
       "4   2020    41483.312400\n",
       "5   2021    42322.020544\n",
       "6   2022    43160.728688\n",
       "7   2023    43999.436832\n",
       "8   2024    44838.144976\n",
       "9   2025    45676.853119\n",
       "10  2026    46515.561263\n",
       "11  2027    47354.269407\n",
       "12  2028    48192.977551\n",
       "13  2029    49031.685695\n",
       "14  2030    49870.393839\n",
       "15  2031    50709.101983\n",
       "16  2032    51547.810127\n",
       "17  2033    52386.518271\n",
       "18  2034    53225.226415"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_csv2=pd.read_csv('Predict_canada_per_capita_income_new.csv')\n",
    "df_csv2             "
   ]
  }
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