Task1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6b879749",
   "metadata": {},
   "source": [
    "# Data Science and Business Analytics Intern (TSF GRIP)\n",
    "## Name: Vishal Chavhan\n",
    "# Task-1 : Prediction Using Supervised ML\n",
    "### Predict the percentage of the student based on the number of study hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "609797b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the liabraries\n",
    "import pandas as pd\n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "#%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04951796",
   "metadata": {},
   "source": [
    "# Step-1: Reading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "83de34c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data read successfully\n"
     ]
    },
    {
     "data": {
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       "</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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url=\"http://bit.ly/w-data\"\n",
    "dataset=pd.read_csv(url)\n",
    "print(\"Data read successfully\")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9eb723db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>count</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.012000</td>\n",
       "      <td>51.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.525094</td>\n",
       "      <td>25.286887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.700000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours     Scores\n",
       "count  25.000000  25.000000\n",
       "mean    5.012000  51.480000\n",
       "std     2.525094  25.286887\n",
       "min     1.100000  17.000000\n",
       "25%     2.700000  30.000000\n",
       "50%     4.800000  47.000000\n",
       "75%     7.400000  75.000000\n",
       "max     9.200000  95.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66530dde",
   "metadata": {},
   "source": [
    "## Getting the first 10 elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d5092cd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</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",
       "  </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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaa7b8b6",
   "metadata": {},
   "source": [
    "### Summary of the given dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "95ab3357",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25 entries, 0 to 24\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Hours   25 non-null     float64\n",
      " 1   Scores  25 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 528.0 bytes\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4571e0db",
   "metadata": {},
   "source": [
    "# Step-2 : Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7484304a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zK0A1lbZ8x29mVoBqKm058ZuZFahaSlsu9ZiZ1ZiCEr+k4yWdnS73lzQ027DMzCwr7SZ+SZeQjKH/9XRTD+D6LIMyM7PsFHLH/0ngNGALQES8DOyTZVBmZpadQhL/9ogIIAAk9SrkxJKGS1qe99oo6TxJfSQtlLQq/bnfu/kFzMysYwpJ/LdK+gnJJOn/ANwP/LS9gyLi2YgYHRGjgQ8CbwJ3AhcBD0TEMOCBdN3MzIqkze6c6STptwBHAhuB4cA3I2JhB68zCXg+Il6SdDowId0+D3iIpA3BzMyKoM3EHxEh6b8jYiTQ0WSf70zgpnT5gIhYly6/AhzwLs5rZlWiWoY8rgSFlHqWSjqmsxeQtAdJ4/Avm7+X33bQwnEzJDVIamhsbOzs5c2sAlTTkMeVoJDEfyzwmKTnJT0haYWkJzpwjY8BSyPi1XT9VUkDANKf61s6KCLmRER9RNT379+/A5czs0pTTUMeV4JChmw4+V1e49O8U+YBuBuYDsxOf971Ls9vZhWumoY8rgTt3vFHxEtAb+AT6at3uq1dadfPE4E78jbPBk6UtAqYnK6bWQ2rtdm8Sq2QJ3dnATcA+6ev6yXNLOTkEbElIvpGxBt52zZExKSIGBYRkyPiL50N3swS85etZfzsRQy96B7Gz15UcbXxahryuBIUUuo5Bzg2IrYASLoCeAy4KsvAzKwwuYbRXI081zAKVEyvmGoa8rgSFJL4BeS3uuxKt5lZGWirYbSSEme1DHlcCQpJ/D8HFku6M12fAszNLCIz6xA3jFpHtZv4I+L7kh4Cjk83nR0RyzKNyswKVk1zwVpxFNK4exywKiJ+GBE/BJ6XdGz2oZlZIdwwah1VyANcPwY2561vTreZWRmYMmYgl08dycDedQgY2LuOy6eOdL3cWlVQ4246tAIAEfGWJM/Va1ZG3DBqHVHIHf8Lkr4qqUf6mgW8kHVgZmaWjUIS/5eADwFr09exwIwsgzIzs+wU0qtnPcmwymZmVgVaveOX9A+ShqXLkvQzSW+kI3R+oHghmplZV2qr1DMLeDFd/jRwNHAocD7wg2zDMjOzrLRV6tkZETvS5VOB/4qIDcD9kv49+9DMLMezU1lXauuO/y1JAyT1JJkz9/689/xIoFmReHYq62ptJf5vAg0k5Z67I+IpAEn/C3fnNCsaz05lXa3VUk9ELJB0CLBPRLye91YDMC3zyMwM8CBs1vXa7McfETubJf3c5CqbWzvGzLqWZ6eyrlbIA1ydJqm3pNskPSNppaRxkvpIWihpVfpzvyxjMCsXnZ0ly4OwWVfLNPGTdPu8NyKOJOkOuhK4CHggIoYBD6TrZlXt3TTQehA262rKG3+t5R0kAWcBh0bEpZIGAwdGxO/bOe69wPL0uMjb/iwwISLWSRoAPBQRbd661NfXR0NDQ0G/kFk5Gj97UYtj5g/sXcf/XDSxBBFZLZC0JCLqm28v5I7/R8A4koe4ADYBVxdw3FCgEfi5pGWSrpXUCzggItal+7wCHNBKwDMkNUhqaGxsLOByZuXLDbRWTgpJ/MdGxLnANoC0sXePAo7rDnwA+HFEjAG20Kysk34TaPErR0TMiYj6iKjv379/AZczK19uoLVyUkji3yGpG2mCltQfeKuA49YAayJicbp+G8kHwatpiYf05/oOR21WYdxAa+WkkMT/Q+BOYH9JlwGPAv/W3kER8QrwZ0m5f9mTgKeBu4Hp6bbpwF0dDdqs0riB1spJu427AJKOJEncIumRs7Kgk0ujgWtJSkMvAGeTfNjcCgwGXgLOiIi/tHUeN+6amXVca4277Y7HL6kPSTnmprxtPfIGcGtVRCwH/uaiJB8iZmZWAoWUepaS9M55DliVLr8oaamkD2YZnJmZdb1CEv9C4JSI6BcRfYGPAQuAr5B09TQzswpSSOI/LiJ+nVuJiPuAcRHxO2DPzCIzM7NMtFvjB9ZJuhC4OV2fRtIlsxuFdes0M7MyUsgd/2eAQcD89DU43dYNOCOrwMzMLBvt3vFHxGvAzFbe/mPXhmNmZlkrpDtnf+CfgfcBPXPbI8IjS1lV8Hy2VmsKKfXcADxDMujat0mmYnw8w5jMisbz2VotKiTx942IucCOiHg4Ij4P+G7fqoLns7VaVEivntwTuuskfRx4GeiTXUhmxePhkq0WFZL4v5NOqvKPwFXAvsB5WQZlViwH9a5rcYIUD5ds1ayQUs/rEfFGRDwZER+JiA8CbQ6qZlYpPFyy1aJCEv9VBW4zqzgeLtlqUaulHknjgA8B/SWdn/fWviQPb5lVhSljBjrRW01pq8a/B7B3us8+eds3Ap/KMigzM8tOq4k/Ih4GHpZ0XUS8VMSYzMwsQ4X06tlT0hxgSP7+hTy5K+lFYBOwC9gZEfXpxC63pOd7kWQGrtc7GriZmXVOIYn/l8A1JFMo7mpn35Z8JB3vJ+cikukbZ0u6KF2/sBPnNTOzTigk8e+MiB934TVPByaky/OAh3DiNzMrmkK6c/5K0lckDZDUJ/cq8PwB3CdpiaQZ6bYDImJduvwKcEBLB0qaIalBUkNjY2OBlzMzs/YUcsc/Pf15Qd62AA4t4NjjI2KtpP2BhZKeyX8zIkJStHRgRMwB5gDU19e3uI+ZmXVcIePxD+3sySNibfpzvaQ7gbEks3cNiIh1kgYA6zt7fjMz67h2Sz2S9pL0jbRnD5KGSTq1gON6SdontwycBDwJ3M073yKmA3d1NngzM+u4Qko9PweWkDzFC7CWpKfPgnaOOwC4U1LuOjdGxL2SHgdulXQO8BKevtHMrKgKSfyHRcQ0SZ8GiIg3lWbztkTEC8DRLWzfAEzqcKRmZcCzdVk1KCTxb5dUR9Kgi6TDgL9mGpVZGcrN1pWbuCU3Wxfg5G8VpZDunJcA9wIHS7oBeIBkDl6zmuLZuqxaFNKrZ6GkpcBxgIBZzZ7ENasJnq3LqkUhvXo+SfL07j0RsQDYKWlK5pGZlZnWZuXybF1WaQoq9UTEG7mViGgiKf+Y1RTP1mXVopDG3ZY+HAo5zqyq5Bpw3avHKl0hCbxB0veBq9P1c0n69ZvVHM/WZdWgkFLPTGA7yRj6NwPbSJK/mZlVoDbv+CV1AxZExEeKFI+ZmWWszTv+iNgFvCXpvUWKx8zMMlZIjX8zsELSQmBLbmNEfDWzqMzMLDOFJP470peZmVWBQp7cnZeO1TM4Ivxseg3ywGRm1aWQJ3c/ASwnGa8HSaMl3Z1xXFYmcgOTrW3aSvDOwGTzl60tdWhm1kmFdOf8FsnMWU0AEbGcwqZdtCpQqQOTzV+2lvGzFzH0onsYP3uRP6jM8hRS498REW80G4L/rYzisTJTiQOTefhks7YVcsf/lKTPAN3SaRevAn5b6AUkdZO0TNKCdH2opMWS/ijpFkl7dDJ2K4JKHJisUr+lmBVLoU/uvo9k8pUbgTeA8zpwjVnAyrz1K4ArI+Jw4HXgnA6cy4qsEgcmq8RvKWbF1Gril9RT0nnAvwOrgXERcUxEfCMithVyckmDgI8D16brAiYCt6W7zAOmdDp6y9yUMQO5fOpIBvauQ8DA3nVcPnVkWZdMKvFbilkxtVXjnwfsAB4BPgaMoGN3+gD/QTJb1z7pel+gKSJ2putrgPLNIAZU3sBkF5w8fLcaP5T/txSzYmor8R8VESMBJM0Fft+RE0s6FVgfEUskTehoYJJmADMABg8e3NHDrYZ5+GSztrWV+HfkFiJiZ7NePYUYD5wm6RSgJ7Av8AOgt6Tu6V3/IKDFfnYRMQeYA1BfXx8dvbjVtkr7lmJWTG017h4taWP62gSMyi1L2tjeiSPi6xExKCKGAGcCiyLiLOBB4FPpbtOBu97l72BmZh3Q6h1/RHRr7b136ULgZknfAZYBczO6jpmZtaAoUyhGxEPAQ+nyCyRPApuZWQkU0o/fzMyqiBO/mVmNceI3M6sxTvxmZjWmKI27ZuAJXczKhRO/FYWHSjYrHy71WFF4qGSz8uHEb0XhoZLNyocTvxWFh0o2Kx9O/FYUlTihi1m1cuOuFYWHSjYrH078VjQeKtmsPLjUY2ZWY5z4zcxqjBO/mVmNceI3M6sxTvxmZjUms149knoCvwH2TK9zW0RcImkocDPQF1gCfDYitmcVRzVpa5CzUg2A5oHXzCpPlt05/wpMjIjNknoAj0r6f8D5wJURcbOka4BzgB9nGEdVaGuQM6AkA6B54DWzypRZqScSm9PVHukrgInAben2ecCUrGKoJm0NclaqAdA88JpZZcq0xi+pm6TlwHpgIfA80BQRO9Nd1gAt3hpKmiGpQVJDY2NjlmFWhLYGOSvVAGgeeM2sMmWa+CNiV0SMBgYBY4EjO3DsnIioj4j6/v37ZxVixWhrkLNSDYDmgdfMKlNRevVERBPwIDAO6C0p17YwCFhbjBgqXVuDnJVqADQPvGZWmbLs1dMf2BERTZLqgBOBK0g+AD5F0rNnOnBXVjFUk0IGOSt27xoPvGZWmRQR2ZxYGkXSeNuN5JvFrRFxqaRDSZJ+H2AZ8HcR8de2zlVfXx8NDQ2ZxGlmVq0kLYmI+ubbM7vjj4gngDEtbH+BpN5vZcp9882qm4dltt24b75Z9fOQDbYb9803q35O/LYb9803q35O/LYb9803q35O/FVi/rK1jJ+9iKEX3cP42YuYv6xzj0e4b75Z9XPjbhXoygZZ9803q35O/F2sFF0h22qQ7cy1PSm6WXVz4u9CpeoK6QZZM+sI1/i7UKm6QrpB1sw6wom/C5XqztsNsmbWEU78XahUd95Txgzk8qkjGdi7DgEDe9dx+dSRrtObWYtc4+9CF5w8fLcaPxTvztsNsmZWKCf+LuSukGZWCZz4u5jvvM2s3DnxVxAPl2xmXcGJv0J4uGQz6yqZ9eqRdLCkByU9LekpSbPS7X0kLZS0Kv25X1YxdFZXjXvTlTxcspl1lSy7c+4E/jEijgKOA86VdBRwEfBARAwDHkjXy0buznpt01aCd+6sS538/XSumXWVzBJ/RKyLiKXp8iZgJTAQOJ1kLl7Sn1OyiqEzyvXO2k/nmllXKcoDXJKGkMy/uxg4ICLWpW+9AhzQyjEzJDVIamhsbCxGmED53ln76Vwz6yqZJ35JewO3A+dFxMb89yIigGjpuIiYExH1EVHfv3//rMN8W7neWfvpXDPrKpn26pHUgyTp3xARd6SbX5U0ICLWSRoArM8yho4q5dO37fEzAmbWFbLs1SNgLrAyIr6f99bdwPR0eTpwV1YxdIbvrM2s2imptmRwYul44BFgBfBWuvlikjr/rcBg4CXgjIj4S1vnqq+vj4aGhkziNDOrVpKWRER98+2ZlXoi4lFArbw9Kavr5vgpVzOzllXlk7t+ytXMrHVVOR5/ufbFNzMrB1WZ+Mu1L76ZWTmoysRfrn3xzczKQVUmfj/lambWuqps3PVMWGZmravKxA9+ytXMrDVVWeoxM7PWOfGbmdUYJ34zsxrjxG9mVmOc+M3Makxmo3N2JUmNJCN5FqIf8FqG4XRWOcZVjjGB4+qIcowJyjOucowJso3rkIj4m5msKiLxd4SkhpaGIS21coyrHGMCx9UR5RgTlGdc5RgTlCYul3rMzGqME7+ZWY2pxsQ/p9QBtKIc4yrHmMBxdUQ5xgTlGVc5xgQliKvqavxmZta2arzjNzOzNjjxm5nVmKpJ/JJ+Jmm9pCdLHUuOpIMlPSjpaUlPSZpV6pgAJPWU9HtJf0jj+napY8qR1E3SMkkLSh1LjqQXJa2QtFxSQ6njyZHUW9Jtkp6RtFLSuBLHMzz9G+VeGyWdV8qYciR9Lf23/qSkmyT1LIOYZqXxPFXsv1PV1PglfRjYDPxXRLy/1PEASBoADIiIpZL2AZYAUyLi6RLHJaBXRGyW1AN4FJgVEb8rZVwAks4H6oF9I+LUUscDSeIH6iOirB7+kTQPeCQirpW0B7BXRDSVOCwg+QAH1gLHRkShD19mFctAkn/jR0XEVkm3Av8dEdeVMKb3AzcDY4HtwL3AlyLij8W4ftXc8UfEb4C/lDqOfBGxLiKWpsubgJVAyScJiMTmdLVH+ir5HYCkQcDHgWtLHUu5k/Re4MPAXICI2F4uST81CXi+1Ek/T3egTlJ3YC/g5RLHMwJYHBFvRsRO4GFgarEuXjWJv9xJGgKMARaXOBTg7ZLKcmA9sDAiyiGu/wD+GXirxHE0F8B9kpZImlHqYFJDgUbg52lp7FpJvUodVJ4zgZtKHQRARKwFvgesBtYBb0TEfaWNiieBEyT1lbQXcApwcLEu7sRfBJL2Bm4HzouIjaWOByAidkXEaGAQMDb96lkykk4F1kfEklLG0YrjI+IDwMeAc9OyYql1Bz4A/DgixgBbgItKG1IiLTudBvyy1LEASNoPOJ3kw/IgoJekvytlTBGxErgCuI+kzLMc2FWs6zvxZyytod8O3BARd5Q6nubS8sCDwEdLHMp44LS0nn4zMFHS9aUNKZHeMRIR64E7SeqypbYGWJP3Te02kg+CcvAxYGlEvFrqQFKTgT9FRGNE7ADuAD5U4piIiLkR8cGI+DDwOvBcsa7txJ+htBF1LrAyIr5f6nhyJPWX1DtdrgNOBJ4pZUwR8fWIGBQRQ0jKBIsioqR3ZQCSeqUN86SllJNIvqaXVES8AvxZ0vB00ySgpJ0G8nyaMinzpFYDx0naK/1/chJJe1tJSdo//TmYpL5/Y7GuXTWTrUu6CZgA9JO0BrgkIuaWNirGA58FVqT1dICLI+K/SxcSAAOAeWnPi/cAt0ZE2XSfLDMHAHcm+YLuwI0RcW9pQ3rbTOCGtLTyAnB2iePJfTieCHyx1LHkRMRiSbcBS4GdwDLKY/iG2yX1BXYA5xazcb5qunOamVlhXOoxM6sxTvxmZjXGid/MrMY48ZuZ1RgnfjOzGuPEbxVJ0uZm65+T9J9FvP5xkhano1CulPStdPsESR1+OEjSdZI+lS5fK+moDhw7oZxGM7XyVzX9+M26gqTu6aBZ7ZkHnBERf0ifh8g9SDWBZJTY33Y2hoj4QmePNSuE7/it6kgaImmRpCckPZA+GbnbXXW6vjn9OUHSI5LuBp5On9a9J52v4ElJ01q4zP4kA37lxj16Oh2I70vA19JvAie0cU1J+k9Jz0q6Pz1fbp+HJNWnyydJekzSUkm/TMd9QtJHlYzDv5Qijupo1cGJ3ypVnfIm/QAuzXvvKmBeRIwCbgB+WMD5PkAyJ8ERJOMWvRwRR6dzO7T0pO6VwLOS7pT0RUk9I+JF4BrgyogYHRGPtHG9T5J8SzgK+HtaGDtGUj/gG8DkdJC4BuB8JZOI/BT4BPBB4MACfj+ztznxW6XamibX0ekoo9/Me28c74x78gvg+ALO9/uI+FO6vAI4UdIVkk6IiDea7xwRl5JMGHMf8Bla/nBoy4eBm9JvCy8Di1rY5ziSD4b/ST/cpgOHAEeSDDq2KpJH78tiMDurHE78Vkt2kv6bl/QeYI+897bkFiLiOZJvACuA70jK/1Ahb7/nI+LHJIN+HZ2Ou9KRa7ZHJHMl5D7gjoqIczpwvFmLnPitGv2WZIRPgLOAXMnlRZLSCCTjxfdo6WBJBwFvRsT1wHdpYbhjSR9PR3oEGEYylnoTsAnYJ2/X1q75G2BaOiHOAOAjLYTyO2C8pMPTa/aSdATJSKpDJB2W7vfpln4Ps9a4V49Vo5kkM1NdQDJLVW7Uyp8Cd0n6A0lpZksrx48EvivpLZKRE7/cwj6fBa6U9CbJXf1ZEbFL0q+A2ySdnsbR2jXvBCaSDKW8Gnis+QUiolHS54CbJO2Zbv5GRDynZCawe9LrP8LuHzZmbfLonGZmNcalHjOzGuPEb2ZWY5z4zcxqjBO/mVmNceI3M6sxTvxmZjXGid/MrMb8fws7gdBbhwXFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.plot(x='Hours' , y='Scores' ,style='o')\n",
    "plt.title('Hours vs Percentage')\n",
    "plt.xlabel('Hours Studied')\n",
    "plt.ylabel('Percentage Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87f28ab3",
   "metadata": {},
   "source": [
    "# Step-3 : Training of Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "12d591b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "adcc267f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7361f35",
   "metadata": {},
   "source": [
    "## Training Part "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f77c96b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Complete\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "print(\"Training Complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d29a7a9",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Plotting the regression line\n",
    "line=regressor.coef_*X+regressor.intercept_\n",
    "\n",
    "#Plotting for the test data\n",
    "plt.scatter(X, y, color=\"purple\")\n",
    "plt.plot(X, line,color=\"red\")\n",
    "plt.title(\"Training set\")\n",
    "plt.xlabel(\"Hours\")\n",
    "plt.ylabel(\"Scores\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12a365f8",
   "metadata": {},
   "source": [
    "# Step-5 : Prediction and Comparing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2413d1d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.5]\n",
      " [3.2]\n",
      " [7.4]\n",
      " [2.5]\n",
      " [5.9]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test)\n",
    "y_pred= regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a883e5",
   "metadata": {},
   "source": [
    "### Actual vs Predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8d3774a6",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>16.884145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>33.732261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>75.357018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>26.794801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>60.491033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  16.884145\n",
       "1      27  33.732261\n",
       "2      69  75.357018\n",
       "3      30  26.794801\n",
       "4      62  60.491033"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({'Actual':y_test, 'Predicted': y_pred})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3ea7bc11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traning Score: 0.9515510725211552\n",
      "Test Score: 0.9454906892105355\n"
     ]
    }
   ],
   "source": [
    "print('Traning Score:', regressor.score(X_train,y_train))\n",
    "print('Test Score:', regressor.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5d6faaef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar', figsize=(6,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9b6a6cf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = 9.25\n",
      "Predicted Score = 93.69173248737535\n"
     ]
    }
   ],
   "source": [
    "Hours=9.25\n",
    "test=np.array([Hours])\n",
    "test=test.reshape(-1,1)\n",
    "own_pred=regressor.predict(test)\n",
    "print(\"No of Hours = {}\".format(Hours))\n",
    "print(\"Predicted Score = {}\".format(own_pred[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdc72c90",
   "metadata": {},
   "source": [
    "# Step-6 : Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "170e5275",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.183859899002975\n",
      "Mean Squared Error: 21.598769307217406\n",
      "Root Mean Squared Error: 4.647447612100367\n",
      "R-2: 0.9454906892105355\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(\"Mean Absolute Error:\",metrics.mean_absolute_error(y_test, y_pred))\n",
    "print(\"Mean Squared Error:\", metrics.mean_squared_error(y_test, y_pred))\n",
    "print(\"Root Mean Squared Error:\",np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "print('R-2:',metrics.r2_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e6f1a5f",
   "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}