task1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TASK1- Prediction using Supervised ML\n",
    "#Predict the percentage of a student based on the no of study hours\n",
    "# what will be predicted score if a student studies for 9.25 hrs/ day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.2</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.3</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.7</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.5</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.3</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.9</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.9</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.1</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.4</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.8</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.8</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.9</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7.8</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hours  Scores\n",
       "0     2.5      21\n",
       "1     5.1      47\n",
       "2     3.2      27\n",
       "3     8.5      75\n",
       "4     3.5      30\n",
       "5     1.5      20\n",
       "6     9.2      88\n",
       "7     5.5      60\n",
       "8     8.3      81\n",
       "9     2.7      25\n",
       "10    7.7      85\n",
       "11    5.9      62\n",
       "12    4.5      41\n",
       "13    3.3      42\n",
       "14    1.1      17\n",
       "15    8.9      95\n",
       "16    2.5      30\n",
       "17    1.9      24\n",
       "18    6.1      67\n",
       "19    7.4      69\n",
       "20    2.7      30\n",
       "21    4.8      54\n",
       "22    3.8      35\n",
       "23    6.9      76\n",
       "24    7.8      86"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"https://raw.githubusercontent.com/AdiPersonalWorks/Random/master/student_scores%20-%20student_scores.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x194621112b0>"
      ]
     },
     "execution_count": 4,
     "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": [
    "plt.xlabel(\"Hours\")\n",
    "plt.ylabel(\"Scores\")\n",
    "plt.scatter(data.Hours,data.Scores,color='r',s=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr=LinearRegression()\n",
    "lr.fit(data[['Hours']],data.Scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.77580339])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=lr.coef_\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.483673405373196"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b=lr.intercept_\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict=data[['Hours']]*a+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x194623444c0>]"
      ]
     },
     "execution_count": 11,
     "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": [
    "plt.xlabel(\"Hours\")\n",
    "plt.ylabel(\"Scores\")\n",
    "plt.scatter(data.Hours,data.Scores,color='r',s=60)  \n",
    "plt.plot(data.Hours,predict,color='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([92.90985477])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict([[9.25]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}