Linear Regression Equation_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "46cc3746",
   "metadata": {},
   "source": [
    "## Practical - 4\n",
    "\n",
    "## Linear Regression Equation\n",
    "\n",
    "## Problem"
   ]
  },
  {
   "attachments": {
    "Prac-4.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "cef44957",
   "metadata": {},
   "source": [
    "![Prac-4.png](attachment:Prac-4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec782345",
   "metadata": {},
   "source": [
    "## Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "12b831a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "537fbe69",
   "metadata": {},
   "source": [
    "## Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a2be0f6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = [0.58,0.86,0.29,0.20,0.56,0.28,0.08,0.41,0.22,0.35,0.59,0.22,0.26,0.12,0.65,0.70,0.30,0.70,0.39,0.72,0.45,0.81,0.04,0.20,0.95]\n",
    "x2 = [0.71,0.13,0.79,0.20,0.56,0.92,0.01,0.60,0.70,0.73,0.13,0.96,0.27,0.21,0.88,0.30,0.15,0.09,0.17,0.25,0.30,0.32,0.82,0.98,0.00]\n",
    "y = [1.45,1.93,0.81,0.61,1.55,0.95,0.45,1.14,0.74,0.98,1.41,0.81,0.89,0.68,1.39,1.53,0.91,1.49,1.38,1.73,1.11,1.68,0.66,0.69,1.98]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "30079460",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'X1':x1,'X2':x2,'Y':y})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3585d63b",
   "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>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.58</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.41</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.59</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.65</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.39</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.45</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.81</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      X1    X2     Y\n",
       "0   0.58  0.71  1.45\n",
       "1   0.86  0.13  1.93\n",
       "2   0.29  0.79  0.81\n",
       "3   0.20  0.20  0.61\n",
       "4   0.56  0.56  1.55\n",
       "5   0.28  0.92  0.95\n",
       "6   0.08  0.01  0.45\n",
       "7   0.41  0.60  1.14\n",
       "8   0.22  0.70  0.74\n",
       "9   0.35  0.73  0.98\n",
       "10  0.59  0.13  1.41\n",
       "11  0.22  0.96  0.81\n",
       "12  0.26  0.27  0.89\n",
       "13  0.12  0.21  0.68\n",
       "14  0.65  0.88  1.39\n",
       "15  0.70  0.30  1.53\n",
       "16  0.30  0.15  0.91\n",
       "17  0.70  0.09  1.49\n",
       "18  0.39  0.17  1.38\n",
       "19  0.72  0.25  1.73\n",
       "20  0.45  0.30  1.11\n",
       "21  0.81  0.32  1.68\n",
       "22  0.04  0.82  0.66\n",
       "23  0.20  0.98  0.69\n",
       "24  0.95  0.00  1.98"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9af499f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x= df[['X1','X2']]\n",
    "y = df['Y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c51835d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "lm = LinearRegression().fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "865dc088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.43354711505518506"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5fc85fe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.65299345, 0.00394488])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5e566cfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The estimated linear regression equation will be :- y = 0.43354711505518506 + 1.6529934509657123*x1 + 0.0039448751847171865*x2\n"
     ]
    }
   ],
   "source": [
    "print(f'The estimated linear regression equation will be :- y = {lm.intercept_} + {lm.coef_[0]}*x1 + {lm.coef_[1]}*x2')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}