Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c72029c5",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_24816/839203243.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Oil_CIT.xlsx\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msheet_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Oil\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "data = pd.read_excel(\"Oil_CIT.xlsx\", sheet_name=\"Oil\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f1a6777",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e5ac0860",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel(\"Oil_CIT.xlsx\", sheet_name=\"Oil\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4f41e559",
   "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>Oil price</th>\n",
       "      <th>CIT (% of GDP)</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>30.38</td>\n",
       "      <td>3.925538</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>25.98</td>\n",
       "      <td>3.960876</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>26.18</td>\n",
       "      <td>3.284355</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>31.08</td>\n",
       "      <td>3.217150</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>41.51</td>\n",
       "      <td>3.746955</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005</td>\n",
       "      <td>56.64</td>\n",
       "      <td>3.930804</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006</td>\n",
       "      <td>66.05</td>\n",
       "      <td>5.987249</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2007</td>\n",
       "      <td>72.34</td>\n",
       "      <td>6.233249</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008</td>\n",
       "      <td>99.67</td>\n",
       "      <td>5.900932</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009</td>\n",
       "      <td>61.95</td>\n",
       "      <td>6.020087</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010</td>\n",
       "      <td>79.48</td>\n",
       "      <td>4.746156</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011</td>\n",
       "      <td>94.88</td>\n",
       "      <td>4.892771</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012</td>\n",
       "      <td>94.05</td>\n",
       "      <td>4.149997</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>97.98</td>\n",
       "      <td>4.918297</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014</td>\n",
       "      <td>93.17</td>\n",
       "      <td>4.201878</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>48.66</td>\n",
       "      <td>3.748108</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016</td>\n",
       "      <td>43.29</td>\n",
       "      <td>3.244261</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017</td>\n",
       "      <td>50.80</td>\n",
       "      <td>3.340058</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>65.23</td>\n",
       "      <td>3.215857</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019</td>\n",
       "      <td>56.99</td>\n",
       "      <td>3.058454</td>\n",
       "      <td>-0.048946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020</td>\n",
       "      <td>39.16</td>\n",
       "      <td>3.245275</td>\n",
       "      <td>0.061083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2021</td>\n",
       "      <td>68.13</td>\n",
       "      <td>3.245275</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2022</td>\n",
       "      <td>80.00</td>\n",
       "      <td>4.698500</td>\n",
       "      <td>0.447797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2023</td>\n",
       "      <td>100.00</td>\n",
       "      <td>5.180500</td>\n",
       "      <td>0.102586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2024</td>\n",
       "      <td>60.00</td>\n",
       "      <td>4.216500</td>\n",
       "      <td>-0.186082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2025</td>\n",
       "      <td>60.00</td>\n",
       "      <td>4.216500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2026</td>\n",
       "      <td>60.00</td>\n",
       "      <td>4.216500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2027</td>\n",
       "      <td>60.00</td>\n",
       "      <td>4.216500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2028</td>\n",
       "      <td>60.00</td>\n",
       "      <td>4.216500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2029</td>\n",
       "      <td>60.00</td>\n",
       "      <td>4.216500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2030</td>\n",
       "      <td>60.00</td>\n",
       "      <td>4.216500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Oil price  CIT (% of GDP)  Unnamed: 3\n",
       "0   2000      30.38        3.925538         NaN\n",
       "1   2001      25.98        3.960876         NaN\n",
       "2   2002      26.18        3.284355         NaN\n",
       "3   2003      31.08        3.217150         NaN\n",
       "4   2004      41.51        3.746955         NaN\n",
       "5   2005      56.64        3.930804         NaN\n",
       "6   2006      66.05        5.987249         NaN\n",
       "7   2007      72.34        6.233249         NaN\n",
       "8   2008      99.67        5.900932         NaN\n",
       "9   2009      61.95        6.020087         NaN\n",
       "10  2010      79.48        4.746156         NaN\n",
       "11  2011      94.88        4.892771         NaN\n",
       "12  2012      94.05        4.149997         NaN\n",
       "13  2013      97.98        4.918297         NaN\n",
       "14  2014      93.17        4.201878         NaN\n",
       "15  2015      48.66        3.748108         NaN\n",
       "16  2016      43.29        3.244261         NaN\n",
       "17  2017      50.80        3.340058         NaN\n",
       "18  2018      65.23        3.215857         NaN\n",
       "19  2019      56.99        3.058454   -0.048946\n",
       "20  2020      39.16        3.245275    0.061083\n",
       "21  2021      68.13        3.245275    0.000000\n",
       "22  2022      80.00        4.698500    0.447797\n",
       "23  2023     100.00        5.180500    0.102586\n",
       "24  2024      60.00        4.216500   -0.186082\n",
       "25  2025      60.00        4.216500    0.000000\n",
       "26  2026      60.00        4.216500         NaN\n",
       "27  2027      60.00        4.216500         NaN\n",
       "28  2028      60.00        4.216500         NaN\n",
       "29  2029      60.00        4.216500         NaN\n",
       "30  2030      60.00        4.216500         NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7c0d1fa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f197abe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "48251ff5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5b92fa41",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_excel(\"Oil_CIT.xlsx\", sheet_name=\"Oil\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5678e452",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Oil_price</th>\n",
       "      <th>CIT_GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>30.38</td>\n",
       "      <td>3.925538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>25.98</td>\n",
       "      <td>3.960876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>26.18</td>\n",
       "      <td>3.284355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>31.08</td>\n",
       "      <td>3.217150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>41.51</td>\n",
       "      <td>3.746955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005</td>\n",
       "      <td>56.64</td>\n",
       "      <td>3.930804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006</td>\n",
       "      <td>66.05</td>\n",
       "      <td>5.987249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2007</td>\n",
       "      <td>72.34</td>\n",
       "      <td>6.233249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008</td>\n",
       "      <td>99.67</td>\n",
       "      <td>5.900932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009</td>\n",
       "      <td>61.95</td>\n",
       "      <td>6.020087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010</td>\n",
       "      <td>79.48</td>\n",
       "      <td>4.746156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011</td>\n",
       "      <td>94.88</td>\n",
       "      <td>4.892771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012</td>\n",
       "      <td>94.05</td>\n",
       "      <td>4.149997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>97.98</td>\n",
       "      <td>4.918297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014</td>\n",
       "      <td>93.17</td>\n",
       "      <td>4.201878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>48.66</td>\n",
       "      <td>3.748108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016</td>\n",
       "      <td>43.29</td>\n",
       "      <td>3.244261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017</td>\n",
       "      <td>50.80</td>\n",
       "      <td>3.340058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>65.23</td>\n",
       "      <td>3.215857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019</td>\n",
       "      <td>56.99</td>\n",
       "      <td>3.058454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020</td>\n",
       "      <td>39.16</td>\n",
       "      <td>3.245275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2021</td>\n",
       "      <td>68.13</td>\n",
       "      <td>3.245275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2022</td>\n",
       "      <td>80.00</td>\n",
       "      <td>4.698500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2023</td>\n",
       "      <td>100.00</td>\n",
       "      <td>5.180500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2024</td>\n",
       "      <td>60.00</td>\n",
       "      <td>4.216500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2025</td>\n",
       "      <td>60.00</td>\n",
       "      <td>4.216500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Oil_price   CIT_GDP\n",
       "0   2000      30.38  3.925538\n",
       "1   2001      25.98  3.960876\n",
       "2   2002      26.18  3.284355\n",
       "3   2003      31.08  3.217150\n",
       "4   2004      41.51  3.746955\n",
       "5   2005      56.64  3.930804\n",
       "6   2006      66.05  5.987249\n",
       "7   2007      72.34  6.233249\n",
       "8   2008      99.67  5.900932\n",
       "9   2009      61.95  6.020087\n",
       "10  2010      79.48  4.746156\n",
       "11  2011      94.88  4.892771\n",
       "12  2012      94.05  4.149997\n",
       "13  2013      97.98  4.918297\n",
       "14  2014      93.17  4.201878\n",
       "15  2015      48.66  3.748108\n",
       "16  2016      43.29  3.244261\n",
       "17  2017      50.80  3.340058\n",
       "18  2018      65.23  3.215857\n",
       "19  2019      56.99  3.058454\n",
       "20  2020      39.16  3.245275\n",
       "21  2021      68.13  3.245275\n",
       "22  2022      80.00  4.698500\n",
       "23  2023     100.00  5.180500\n",
       "24  2024      60.00  4.216500\n",
       "25  2025      60.00  4.216500"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4aaa0a42",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e39696cc",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Expected 2D array, got 1D array instead:\narray=[ 30.38  25.98  26.18  31.08  41.51  56.64  66.05  72.34  99.67  61.95\n  79.48  94.88  94.05  97.98  93.17  48.66  43.29  50.8   65.23  56.99\n  39.16  68.13  80.   100.    60.    60.  ].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_24816/4150998804.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOil_price\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCIT_GDP\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    660\u001b[0m         \u001b[0maccept_sparse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpositive\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"csr\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"csc\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"coo\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    661\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 662\u001b[1;33m         X, y = self._validate_data(\n\u001b[0m\u001b[0;32m    663\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_sparse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_numeric\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    664\u001b[0m         )\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    574\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    575\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 576\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    577\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    578\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m    954\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"y cannot be None\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    955\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 956\u001b[1;33m     X = check_array(\n\u001b[0m\u001b[0;32m    957\u001b[0m         \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    958\u001b[0m         \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_sparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    759\u001b[0m             \u001b[1;31m# If input is 1D raise error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    760\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 761\u001b[1;33m                 raise ValueError(\n\u001b[0m\u001b[0;32m    762\u001b[0m                     \u001b[1;34m\"Expected 2D array, got 1D array instead:\\narray={}.\\n\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    763\u001b[0m                     \u001b[1;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[ 30.38  25.98  26.18  31.08  41.51  56.64  66.05  72.34  99.67  61.95\n  79.48  94.88  94.05  97.98  93.17  48.66  43.29  50.8   65.23  56.99\n  39.16  68.13  80.   100.    60.    60.  ].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
     ]
    }
   ],
   "source": [
    "model.fit(data.Oil_price, data.CIT_GDP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eaa74cd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data.Oil_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "36dbdd3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      30.38\n",
       "1      25.98\n",
       "2      26.18\n",
       "3      31.08\n",
       "4      41.51\n",
       "5      56.64\n",
       "6      66.05\n",
       "7      72.34\n",
       "8      99.67\n",
       "9      61.95\n",
       "10     79.48\n",
       "11     94.88\n",
       "12     94.05\n",
       "13     97.98\n",
       "14     93.17\n",
       "15     48.66\n",
       "16     43.29\n",
       "17     50.80\n",
       "18     65.23\n",
       "19     56.99\n",
       "20     39.16\n",
       "21     68.13\n",
       "22     80.00\n",
       "23    100.00\n",
       "24     60.00\n",
       "25     60.00\n",
       "Name: Oil_price, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "512c587f",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'CIT_GDP' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_24816/3144322388.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mCIT_GDP\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'CIT_GDP' is not defined"
     ]
    }
   ],
   "source": [
    "y=CIT_GDP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3b9e0826",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=data.CIT_GDP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "add1fa2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     3.925538\n",
       "1     3.960876\n",
       "2     3.284355\n",
       "3     3.217150\n",
       "4     3.746955\n",
       "5     3.930804\n",
       "6     5.987249\n",
       "7     6.233249\n",
       "8     5.900932\n",
       "9     6.020087\n",
       "10    4.746156\n",
       "11    4.892771\n",
       "12    4.149997\n",
       "13    4.918297\n",
       "14    4.201878\n",
       "15    3.748108\n",
       "16    3.244261\n",
       "17    3.340058\n",
       "18    3.215857\n",
       "19    3.058454\n",
       "20    3.245275\n",
       "21    3.245275\n",
       "22    4.698500\n",
       "23    5.180500\n",
       "24    4.216500\n",
       "25    4.216500\n",
       "Name: CIT_GDP, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9099bae7",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'log' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_24816/1626449797.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'log' is not defined"
     ]
    }
   ],
   "source": [
    "x=log(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "00af11c0",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ln' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_24816/3698002332.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mln\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'ln' is not defined"
     ]
    }
   ],
   "source": [
    "x=ln(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "373631ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math as math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5ef9aaf0",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'e' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_24816/598817500.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'e' is not defined"
     ]
    }
   ],
   "source": [
    "x=math.log(x, e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "816afc97",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot convert the series to <class 'float'>",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_24816/236187745.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    183\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    184\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mconverter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 185\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"cannot convert the series to {converter}\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    186\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    187\u001b[0m     \u001b[0mwrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34mf\"__{converter.__name__}__\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: cannot convert the series to <class 'float'>"
     ]
    }
   ],
   "source": [
    "x=math.log(x, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ba872628",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_24816/3250791651.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mModel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'Model' is not defined"
     ]
    }
   ],
   "source": [
    "Model.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d8548c44",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Expected 2D array, got 1D array instead:\narray=[ 30.38  25.98  26.18  31.08  41.51  56.64  66.05  72.34  99.67  61.95\n  79.48  94.88  94.05  97.98  93.17  48.66  43.29  50.8   65.23  56.99\n  39.16  68.13  80.   100.    60.    60.  ].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_24816/4194991417.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    660\u001b[0m         \u001b[0maccept_sparse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpositive\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"csr\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"csc\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"coo\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    661\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 662\u001b[1;33m         X, y = self._validate_data(\n\u001b[0m\u001b[0;32m    663\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_sparse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_numeric\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    664\u001b[0m         )\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    574\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    575\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 576\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    577\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    578\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m    954\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"y cannot be None\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    955\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 956\u001b[1;33m     X = check_array(\n\u001b[0m\u001b[0;32m    957\u001b[0m         \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    958\u001b[0m         \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_sparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    759\u001b[0m             \u001b[1;31m# If input is 1D raise error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    760\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 761\u001b[1;33m                 raise ValueError(\n\u001b[0m\u001b[0;32m    762\u001b[0m                     \u001b[1;34m\"Expected 2D array, got 1D array instead:\\narray={}.\\n\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    763\u001b[0m                     \u001b[1;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[ 30.38  25.98  26.18  31.08  41.51  56.64  66.05  72.34  99.67  61.95\n  79.48  94.88  94.05  97.98  93.17  48.66  43.29  50.8   65.23  56.99\n  39.16  68.13  80.   100.    60.    60.  ].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
     ]
    }
   ],
   "source": [
    "model = LinearRegression().fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6e2ed9a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.array(x).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3a6761ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 30.38],\n",
       "       [ 25.98],\n",
       "       [ 26.18],\n",
       "       [ 31.08],\n",
       "       [ 41.51],\n",
       "       [ 56.64],\n",
       "       [ 66.05],\n",
       "       [ 72.34],\n",
       "       [ 99.67],\n",
       "       [ 61.95],\n",
       "       [ 79.48],\n",
       "       [ 94.88],\n",
       "       [ 94.05],\n",
       "       [ 97.98],\n",
       "       [ 93.17],\n",
       "       [ 48.66],\n",
       "       [ 43.29],\n",
       "       [ 50.8 ],\n",
       "       [ 65.23],\n",
       "       [ 56.99],\n",
       "       [ 39.16],\n",
       "       [ 68.13],\n",
       "       [ 80.  ],\n",
       "       [100.  ],\n",
       "       [ 60.  ],\n",
       "       [ 60.  ]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "19903556",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9ed2208b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.92553837, 3.96087644, 3.28435458, 3.21715024, 3.74695511,\n",
       "       3.93080428, 5.98724859, 6.2332485 , 5.90093214, 6.02008673,\n",
       "       4.74615574, 4.89277078, 4.14999701, 4.91829714, 4.20187793,\n",
       "       3.74810753, 3.24426072, 3.34005764, 3.21585705, 3.05845437,\n",
       "       3.24527491, 3.24527491, 4.6985    , 5.1805    , 4.2165    ,\n",
       "       4.2165    ])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "699ade3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression().fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4c6a39dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "eff3cf1f",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'LinearRegression' object has no attribute 'coef'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_24816/2858601289.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'LinearRegression' object has no attribute 'coef'"
     ]
    }
   ],
   "source": [
    "model.coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d8155e0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02372042])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c66662b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.7514885417000547"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fdd508c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_excel(\"Oil_CIT.xlsx\", sheet_name=\"Oil\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d3f05282",
   "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>Oil_price</th>\n",
       "      <th>CIT_GDP</th>\n",
       "      <th>Log_Oil_price</th>\n",
       "      <th>Log_CIT_GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>30.38</td>\n",
       "      <td>3.925538</td>\n",
       "      <td>1.482588</td>\n",
       "      <td>0.593899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>25.98</td>\n",
       "      <td>3.960876</td>\n",
       "      <td>1.414639</td>\n",
       "      <td>0.597791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>26.18</td>\n",
       "      <td>3.284355</td>\n",
       "      <td>1.417970</td>\n",
       "      <td>0.516450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>31.08</td>\n",
       "      <td>3.217150</td>\n",
       "      <td>1.492481</td>\n",
       "      <td>0.507471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>41.51</td>\n",
       "      <td>3.746955</td>\n",
       "      <td>1.618153</td>\n",
       "      <td>0.573678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005</td>\n",
       "      <td>56.64</td>\n",
       "      <td>3.930804</td>\n",
       "      <td>1.753123</td>\n",
       "      <td>0.594481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006</td>\n",
       "      <td>66.05</td>\n",
       "      <td>5.987249</td>\n",
       "      <td>1.819873</td>\n",
       "      <td>0.777227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2007</td>\n",
       "      <td>72.34</td>\n",
       "      <td>6.233249</td>\n",
       "      <td>1.859379</td>\n",
       "      <td>0.794714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008</td>\n",
       "      <td>99.67</td>\n",
       "      <td>5.900932</td>\n",
       "      <td>1.998564</td>\n",
       "      <td>0.770921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009</td>\n",
       "      <td>61.95</td>\n",
       "      <td>6.020087</td>\n",
       "      <td>1.792041</td>\n",
       "      <td>0.779603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010</td>\n",
       "      <td>79.48</td>\n",
       "      <td>4.746156</td>\n",
       "      <td>1.900258</td>\n",
       "      <td>0.676342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011</td>\n",
       "      <td>94.88</td>\n",
       "      <td>4.892771</td>\n",
       "      <td>1.977175</td>\n",
       "      <td>0.689555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012</td>\n",
       "      <td>94.05</td>\n",
       "      <td>4.149997</td>\n",
       "      <td>1.973359</td>\n",
       "      <td>0.618048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>97.98</td>\n",
       "      <td>4.918297</td>\n",
       "      <td>1.991137</td>\n",
       "      <td>0.691815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014</td>\n",
       "      <td>93.17</td>\n",
       "      <td>4.201878</td>\n",
       "      <td>1.969276</td>\n",
       "      <td>0.623443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>48.66</td>\n",
       "      <td>3.748108</td>\n",
       "      <td>1.687172</td>\n",
       "      <td>0.573812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016</td>\n",
       "      <td>43.29</td>\n",
       "      <td>3.244261</td>\n",
       "      <td>1.636388</td>\n",
       "      <td>0.511116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017</td>\n",
       "      <td>50.80</td>\n",
       "      <td>3.340058</td>\n",
       "      <td>1.705864</td>\n",
       "      <td>0.523754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>65.23</td>\n",
       "      <td>3.215857</td>\n",
       "      <td>1.814447</td>\n",
       "      <td>0.507297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019</td>\n",
       "      <td>56.99</td>\n",
       "      <td>3.058454</td>\n",
       "      <td>1.755799</td>\n",
       "      <td>0.485502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020</td>\n",
       "      <td>39.16</td>\n",
       "      <td>3.245275</td>\n",
       "      <td>1.592843</td>\n",
       "      <td>0.511251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2021</td>\n",
       "      <td>68.13</td>\n",
       "      <td>3.245275</td>\n",
       "      <td>1.833338</td>\n",
       "      <td>0.511251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2022</td>\n",
       "      <td>80.00</td>\n",
       "      <td>4.698500</td>\n",
       "      <td>1.903090</td>\n",
       "      <td>0.671959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2023</td>\n",
       "      <td>100.00</td>\n",
       "      <td>5.180500</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.714372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2024</td>\n",
       "      <td>60.00</td>\n",
       "      <td>4.216500</td>\n",
       "      <td>1.778151</td>\n",
       "      <td>0.624952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2025</td>\n",
       "      <td>60.00</td>\n",
       "      <td>4.216500</td>\n",
       "      <td>1.778151</td>\n",
       "      <td>0.624952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Oil_price   CIT_GDP  Log_Oil_price  Log_CIT_GDP\n",
       "0   2000      30.38  3.925538       1.482588     0.593899\n",
       "1   2001      25.98  3.960876       1.414639     0.597791\n",
       "2   2002      26.18  3.284355       1.417970     0.516450\n",
       "3   2003      31.08  3.217150       1.492481     0.507471\n",
       "4   2004      41.51  3.746955       1.618153     0.573678\n",
       "5   2005      56.64  3.930804       1.753123     0.594481\n",
       "6   2006      66.05  5.987249       1.819873     0.777227\n",
       "7   2007      72.34  6.233249       1.859379     0.794714\n",
       "8   2008      99.67  5.900932       1.998564     0.770921\n",
       "9   2009      61.95  6.020087       1.792041     0.779603\n",
       "10  2010      79.48  4.746156       1.900258     0.676342\n",
       "11  2011      94.88  4.892771       1.977175     0.689555\n",
       "12  2012      94.05  4.149997       1.973359     0.618048\n",
       "13  2013      97.98  4.918297       1.991137     0.691815\n",
       "14  2014      93.17  4.201878       1.969276     0.623443\n",
       "15  2015      48.66  3.748108       1.687172     0.573812\n",
       "16  2016      43.29  3.244261       1.636388     0.511116\n",
       "17  2017      50.80  3.340058       1.705864     0.523754\n",
       "18  2018      65.23  3.215857       1.814447     0.507297\n",
       "19  2019      56.99  3.058454       1.755799     0.485502\n",
       "20  2020      39.16  3.245275       1.592843     0.511251\n",
       "21  2021      68.13  3.245275       1.833338     0.511251\n",
       "22  2022      80.00  4.698500       1.903090     0.671959\n",
       "23  2023     100.00  5.180500       2.000000     0.714372\n",
       "24  2024      60.00  4.216500       1.778151     0.624952\n",
       "25  2025      60.00  4.216500       1.778151     0.624952"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3675f4a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.array(data.Log_Oil_price).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d635a93c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.48258777],\n",
       "       [1.41463915],\n",
       "       [1.41796964],\n",
       "       [1.49248101],\n",
       "       [1.61815273],\n",
       "       [1.75312324],\n",
       "       [1.81987282],\n",
       "       [1.8593785 ],\n",
       "       [1.99856446],\n",
       "       [1.79204131],\n",
       "       [1.90025786],\n",
       "       [1.97717468],\n",
       "       [1.9733588 ],\n",
       "       [1.99113744],\n",
       "       [1.9692761 ],\n",
       "       [1.6871721 ],\n",
       "       [1.63638759],\n",
       "       [1.70586371],\n",
       "       [1.81444738],\n",
       "       [1.75579866],\n",
       "       [1.59284268],\n",
       "       [1.83333839],\n",
       "       [1.90308999],\n",
       "       [2.        ],\n",
       "       [1.77815125],\n",
       "       [1.77815125]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "64383c93",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=np.array(data.Log_CIT_GDP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ce59d8e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.59389923, 0.59779129, 0.51645004, 0.50747134, 0.57367849,\n",
       "       0.59448142, 0.77722729, 0.79471444, 0.77092062, 0.77960275,\n",
       "       0.67634199, 0.68955487, 0.61804778, 0.69181476, 0.62344343,\n",
       "       0.57381204, 0.51111575, 0.52375396, 0.50729674, 0.48550201,\n",
       "       0.51125149, 0.51125149, 0.67195923, 0.71437168, 0.6249521 ,\n",
       "       0.6249521 ])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8f4e739b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Model = LinearRegression().fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6159ada5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.30650027])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c9f0d30d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07628554184785474"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27bd0508",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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