The Sparks Foundation Task 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e2f0fdf8",
   "metadata": {},
   "source": [
    "# The Sparks Foundation - GRIP - Data Science and Business Analytics -\n",
    "# January 2022\n",
    "\n",
    "# Task 1 :- Prediction Using Supervised ML\n",
    "\n",
    "# Author : Ritesh Meshram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0979b139",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing required libraries\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9e2be47",
   "metadata": {},
   "source": [
    "## Reading data from url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c96d24e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\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>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",
       "  </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"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_score = pd.read_csv(\"https://raw.githubusercontent.com/AdiPersonalWorks/Random/master/student_scores%20-%20student_scores.csv\")\n",
    "student_score.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "20acf6a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 2)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_score.shape #shape of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6aca68ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hours</th>\n",
       "      <td>25.0</td>\n",
       "      <td>5.012</td>\n",
       "      <td>2.525094</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scores</th>\n",
       "      <td>25.0</td>\n",
       "      <td>51.480</td>\n",
       "      <td>25.286887</td>\n",
       "      <td>17.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count    mean        std   min   25%   50%   75%   max\n",
       "Hours    25.0   5.012   2.525094   1.1   2.7   4.8   7.4   9.2\n",
       "Scores   25.0  51.480  25.286887  17.0  30.0  47.0  75.0  95.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_score.describe().T #statistical values. T is used for transpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ebb55def",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ritesh\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Hours', ylabel='Density'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distplot lets you show a histogram with a line on it. This can be shown in all kinds of variations.\n",
    "sns.distplot(student_score['Hours'],label='hour',color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5b55aa8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ritesh\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Scores', ylabel='Density'>"
      ]
     },
     "execution_count": 6,
     "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": [
    "#distplot lets you show a histogram with a line on it. This can be shown in all kinds of variations.\n",
    "sns.distplot(student_score['Scores'],label='Scores',color='green' )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "377e8dc8",
   "metadata": {},
   "source": [
    "Let's plot our data points on 2-D graph to eyeball our dataset and see if we can manually find any relationship between the data. We can create the plot with the following script:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0bbd5b33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wM7POyZL4Z0r6IbCHpC8CDwI/2tFOEfFiRIyNiLHAkcB64B7gKmBuRIwE5qavzcysTDrsx59Okn4ncDDwFnAQcE1EzOnkeSYAv4uIVyRNBFrS9dOAeSTXEMzMrAw6TPwREZJ+ERFHAp1N9sXOAe5Il/eKiOXp8ZdLGrwTxzUzs07KUup5VNJRXT2BpF1ILg7/rJP7TZbUKqm1ra2tq6c3s1pSB0Me14IsQzZ8DLhI0hKSnj0i+TEwOuM5TgEWRsSK9PUKSUPS1v4QYGWpnSJiKjAVoLm5OTKey8xqVZ0MeVwLsiT+U3byHOfyXpkH4D5gEjAlfb53J49vZvWgToY8rgU7LPVExCvAHsBp6WOPdN0OSeoDfBz4edHqKcDHJS1O35vSyZjNrB7VyZDHtWCHLX5JlwNf5L3kPV3S1Ij47o72jYj1wIB261aR9PIxM3tPnQx5XAuU3JvVwQbSM8C4iFiXvu4LzO9EjX+nNTc3R2tra7lOZ1ab5s930rT3kbQgIprbr89S4xewtej11nSdmVULXxi1TsiS+G8FHpN0T/r6DOCW3CIys87zhVHrhB0m/oj4jqR5wEdIWvoXRMSTeQdmZp1QuDBaaPH7wqh1IMvF3WOB5yNiYfp6d0nHRMRjuUdnZtn4wqh1QpZSzw+ADxe9XldinZlVWh3MBWvlkWXIBkVR15+IeAdP0m5mVrOyJP6XJV0mqWf6uBx4Oe/AzMwsH1kS/0XAccCr6eMYYHKeQZmZWX6y9OpZSTKsspmZ1YHttvglfVHSyHRZkn4s6U1Jz0jyhV0zsxrVUanncmBJunwuMAbYH/gq8K/5hmVmZnnpKPFviYjN6fKpwH9GxKqIeBDINOG6mZlVn44S/zuShkjqRTKa5oNF7/XONywzK8kzVFk36Oji7jVAK9AE3BcRzwNI+nPcndOs/DwQm3WT7bb4I2IWsB8wKiK+WPRWK3B23oGZWTulBmIz64IOu3NGxBZgdbt163KNyMxK80Bs1k1yHXpB0h7AzcBhQACfB14E7gSGk/QaOisiVpc+gpm9ywOxWTfJcufuzvhX4IGIOJikO+gi4CpgbkSMBOamr80ax85coB03Dq6+2knfdkqWYZkFfBbYPyL+XtIwYO+IeHwH+/UDxgPnA0TEJmCTpIlAS7rZNGAecGUX4zerLb5Aa1UgS4v/34FxJDdxAawBvp9hv/2BNuBWSU9Kujmdr3eviFgOkD4PLrWzpMmSWiW1trW1ZTidWQ3wBVqrAlkS/zERcQmwESCtx++SYb8eJGP2/yAijiAZxz9zWScipkZEc0Q0Dxo0KOtuZtWtcIG2qckXaK1islzc3SypieTiLJIGAe9k2G8ZsKxopq67SBL/CklDImK5pCHAyi7EbVabfIHWqkCWxP9vwD3AYEnXA38FfHNHO0XEHyX9QdJBEfEiyd2/L6SPScCU9PnergZvVpM8U5ZVWJZhmWdIWkCSuAWcERGLMh7/UmCGpF1I7va9gKS8NFPShcBS4MwuRW5mZl2SpVdPf5JyzB1F63oWDeC2XRHxFNBc4q0JnYjRzMy6UZaLuwtJeue8BCxOl38vaaGkI/MMzszMul+WxP8A8MmIGBgRA4BTgJnAl0m6epqZWQ3JkvibI+J/Ci8iYjYwPiIeBXbNLTIzM8tFll49f5J0JfDT9PXZwOq0i2eWbp1mZlZFsrT4PwMMBX5B0vVyWLquCTgrt8jMzCwXWbpzvk7SLbOU/+3ecMzMLG9ZunMOAr4OHAr0KqyPiBNyjMvMzHKSpdQzA/gtMAL4NskY+k/kGJNZ+XkuW2sgWS7uDoiIWyRdHhEPAQ9JeijvwMzKxkMlW4PJ0uIv3KG7XNKnJB1BcrHXrD54qGRrMFla/P8g6YPA3wDfBfoBV+QZlFlZeS5bazBZEv/qiHgTeBP4GICk43ONyqycPFSyNZgsif+7JBOq7GidWe3yUMnWQLab+CWNA44DBkn6atFb/Uhu3jIzsxrUUYt/F2C3dJvdi9a/RTIZi5mZ1aDtJv6irpu3RcQrZYzJzMxylKXGv6ukqcDw4u2z3LkraQmwBtgKbImI5nRilzvT4y0BzkoncDczszLIkvh/BvwHcDNJAu+sj6Xj/RRcBcyNiCmSrkpfX9mF45qZWRdkSfxbIuIH3XjOiUBLujwNmIcTv5lZ2WS5c/e/JH1Z0hBJ/QuPjMcPYLakBZImp+v2iojlAOnz4FI7SposqVVSa1tbW8bTmZnZjmRp8U9Kn79WtC6A/TPse3xEvCZpMDBH0m+zBhYRU4GpAM3NzZF1PzMz61iW8fhHdPXgEfFa+rxS0j3A0cAKSUMiYrmkIcDKrh7fzMw6b4elHkl9JH0z7dmDpJGSTs2wX19JuxeWgZOA54D7eO9XxCSSWb3MzKxMspR6bgUWkNzFC7CMpKfPrB3stxdwj6TCeW6PiAckPQHMlHQhsBQ4syuBm5lZ12RJ/AdExNmSzgWIiA1Ks3lHIuJlYEyJ9auACZ2O1MzMukWWXj2bJPUmuaCLpAOAt3ONyqyaebYuq3FZWvzXAg8A+0qaARwPnJ9nUGZVy7N1WR3I0qtnjqSFwLGAgMvb3Ylr1jhKzdblxG81Jkuvnk+T3L17f0TMArZIOiP3yMyqUWG2rqYmz9ZlNStLjf/adAYuACLiDZLyj1njKczWdd11LvNYzcpS4y/15ZBlP7P65Nm6rMZlafG3SvqOpAMk7S/pJpJ+/WZmVoOyJP5LgU0kY+jPBDYAl+QZlJmZ5afDko2kJuDeiDixTPGYmVnOOmzxR8RWYL2kD5YpHjMzy1mWi7QbgWclzQHWFVZGxGW5RWVmZrnJkvjvTx9mZlYHsty5Oy0dq2dYRLxYhpjMzCxHWe7cPQ14imS8HiSNlXRfznFZtfHAZGZ1I0up51skM2fNA4iIpyR1eVYuq0EemMysrmTpx7+leMiGlOfAbSSlBiarBf6VYlZSlhb/c5I+AzRJGglcBjyS9QTpvQCtwKsRcaqk/iQ3gw0HlgBnRcTqzgZuZVQYmKzQ4q+Fgcn8K8Vsu7LeuXsoyeQrtwNvAld04hyXA4uKXl8FzI2IkcDc9LVVs1ocmKxWf6WYlcF2W/ySegEXAQcCzwLjImJLZw4uaSjwKeB64Kvp6olAS7o8jeTawZWdOa5VQK0NTFaLv1LMyqSjUs80YDPwG+AUYBSda+kD/AvwdWD3onV7RcRygIhYLmlwJ49ptmOFXynz5iVJv5a+tMxy1lHiPyQiDgeQdAvweGcOLOlUYGVELJDU0tnAJE0GJgMMGzass7ub1d6vFLMy6ajGv7mw0NkST+p44HRJS4CfAidImg6skDQEIH1eWWrniJgaEc0R0Txo0KAunN7MzErpKPGPkfRW+lgDjC4sS3prRweOiKsjYmhEDAfOAX4ZEZ8D7gMmpZtNAu7dyc9gZmadsN1ST0Q05XTOKcBMSRcCS4EzczqPmZmVUJYpFCNiHu/d+bsKmFCO85qZ2bay9OM3M7M64sRvZtZgnPjNzBqME7+ZWYNx4jczazBO/FZ+Hi7ZrKLK0p3T7F0eLtms4tzit/LycMlmFefEb+VVGC65qcnDJZtViEs9Vl4eLtms4pz4rfw8XLJZRbnUY2bWYJz4zcwajBO/mVmDceI3M2swTvxmZg3Gid/MrMHklvgl9ZL0uKSnJT0v6dvp+v6S5khanD7vmVcMZma2rTxb/G8DJ0TEGGAs8AlJxwJXAXMjYiQwN31tWXU0wFmlBj/zoGtmNSW3G7giIoC16cue6SOAiUBLun4ayVy8V+YVR13paICzSg1+5kHXzGpOrjV+SU2SngJWAnMi4jFgr4hYDpA+D97OvpMltUpqbWtryzPM2tHRAGeVGvzMg66Z1ZxcE39EbI2IscBQ4GhJh3Vi36kR0RwRzYMGDcotxprS0QBnlRr8zIOumdWcsozVExFvSJoHfAJYIWlIRCyXNITk14Bl0dEAZ5Ua/MyDrpnVHCWl+BwOLA0CNqdJvzcwG7gB+HNgVURMkXQV0D8ivt7RsZqbm6O1tTWXOM3M6pWkBRHR3H59ni3+IcA0SU0kJaWZETFL0nxgpqQLgaXAmTnGYGZm7eTZq+cZ4IgS61cBE/I6r5mZdcx37lpp7ptvVrc8EYtty33zzeqaW/y2LffNN6trTvy2LffNN6trLvXYttw336yuOfHXk/nzuy9Ze0J0s7rlxF8vfEHWzDJyjT8PlegK6QuyZpaRW/zdrVIt78IF2cJ5fUHWzLbDib+7lWp5lyPx+4KsmWXkxN/dKtny9gVZM8vAib+7ueVtZlXOiT8PbnmbWRVzrx4zswbjxG9m1mCc+GuNh0s2s52UW41f0r7AfwJ7A+8AUyPiXyX1B+4EhgNLgLMiYnVecdQV351rZt0gzxb/FuBvImIUcCxwiaRDgKuAuRExEpibvq4+1diy9t25ZtYN8px6cTmwPF1eI2kRsA8wEWhJN5sGzAOuzCuOLqnWlrXvzjWzblCWGr+k4STz7z4G7JV+KRS+HAZvZ5/Jkloltba1tZUjzPdUa8u6cI/AdddVz5eRmdWc3PvxS9oNuBu4IiLekpRpv4iYCkwFaG5ujvwiLKGaW9a+R8DMdlKuiV9ST5KkPyMifp6uXiFpSEQslzQEWJlnDF3iu2/NrI7l2atHwC3Aooj4TtFb9wGTgCnp8715xbBT3LI2szqVZ4v/eOA84FlJT6XrvkGS8GdKuhBYCpyZYwxmZtZOnr16Hga2V9CfkNd5zcysY/V952419sU3M6uw+h2ds1r74puZVVj9tvirtS++mVmF1W/iL/TFb2qqvr74ZmYVVL+lHvfFNzMrqX4TP7gvvplZCfVb6jEzs5Kc+M3MGowTv5lZg3HiNzNrME78ZmYNxonfzKzBKKK8c5x0haQ24JWMmw8EXs8xnK5yXNlVY0xQnXFVY0xQnXFVY0yQb1z7RcSg9itrIvF3hqTWiGiudBztOa7sqjEmqM64qjEmqM64qjEmqExcLvWYmTUYJ34zswZTj4l/aqUD2A7HlV01xgTVGVc1xgTVGVc1xgQViKvuavxmZtaxemzxm5lZB5z4zcwaTN0kfkk/lrRS0nOVjqWYpH0l/UrSIknPS7q8CmLqJelxSU+nMX270jEVSGqS9KSkWZWOpUDSEknPSnpKUmul4ymQtIekuyT9Nv33VdExyCUdlP6NCo+3JF1RyZgKJH0l/bf+nKQ7JPWqgpguT+N5vtx/p7qp8UsaD6wF/jMiDqt0PAWShgBDImKhpN2BBcAZEfFCBWMS0Dci1krqCTwMXB4Rj1YqpgJJXwWagX4RcWql44Ek8QPNEVFVN/9Imgb8JiJulrQL0Cci3qhwWEDyBQ68ChwTEVlvvswrln1I/o0fEhEbJM0E/jsibqtgTIcBPwWOBjYBDwAXR8Ticpy/blr8EfFr4E+VjqO9iFgeEQvT5TXAImCfCscUEbE2fdkzfVS8BSBpKPAp4OZKx1LtJPUDxgO3AETEpmpJ+qkJwO8qnfSL9AB6S+oB9AFeq3A8o4BHI2J9RGwBHgI+Xa6T103irwWShgNHAI9VOJRCSeUpYCUwJyIqHhPwL8DXgXcqHEd7AcyWtEDS5EoHk9ofaANuTUtjN0vqW+mgipwD3FHpIAAi4lXgRmApsBx4MyJmVzYqngPGSxogqQ/wSWDfcp3cib9MJO0G3A1cERFvVTqeiNgaEWOBocDR6U/PipF0KrAyIhZUMo7tOD4iPgycAlySlhUrrQfwYeAHEXEEsA64qrIhJdKy0+nAzyodC4CkPYGJwAjgQ0BfSZ+rZEwRsQi4AZhDUuZ5GthSrvM78ZdBWke/G5gRET+vdDzF0vLAPOATlY2E44HT03r6T4ETJE2vbEiJiHgtfV4J3ENSl620ZcCyol9qd5F8EVSDU4CFEbGi0oGkTgR+HxFtEbEZ+DlwXIVjIiJuiYgPR8R4kjJ1Wer74MSfu/RC6i3Aooj4TqXjAZA0SNIe6XJvkv8xflvJmCLi6ogYGhHDScoEv4yIirbKACT1TS/Kk5ZSTiL5mV5REfFH4A+SDkpXTQAq1mGgnXOpkjJPailwrKQ+6f+PE0iutVWUpMHp8zDgLyjj36xHuU6UN0l3AC3AQEnLgGsj4pbKRgUkLdnzgGfTmjrANyLivysXEkOAaWnPiw8AMyOiarpPVpm9gHuSfEEP4PaIeKCyIb3rUmBGWlp5GbigwvGQ1qs/Dnyp0rEURMRjku4CFpKUU56kOoZvuFvSAGAzcElErC7XieumO6eZmWXjUo+ZWYNx4jczazBO/GZmDcaJ38yswTjxm5k1GCd+q0mS1rZ7fb6k75Xx/MdKeiwdhXKRpG+l61skdfrmIEm3SfqrdPlmSYd0Yt+WahrN1Kpf3fTjN+sOkpoiYmuGTacBZ0XE0+n9EIUbqVpIRol9pKsxRMQXurqvWRZu8VvdkbSfpLmSnkmfh6Xr321Vp6/Xps8t6ZwJt5PcaNdX0v3pfAXPSTq7xGkGkwz4VRj36IV0EL6LgK+kvwQ+2sE5Jel7kl6QdH96vMI28yQ1p8snSZovaaGkn6VjPiHpE0rG4X+Y5K5Ps8yc+K1W9VbRpB/A3xe99z2SeRlGAzOAf8twvKOB/xsRh5CMW/RaRIxJ53YodafuTcCLku6R9CVJvSJiCfAfwE0RMTYiftPB+T5N8ivhcOCLlBg7RtJA4JvAiekgca3AV5VMIvIj4DTgo8DeGT6f2buc+K1WbUiT69h0lNFrit4bB9yeLv8E+EiG4z0eEb9Pl58FTpR0g6SPRsSb7TeOiL8nmTBmNvAZSn85dGQ8cEf6a+E14JcltjkWOAT4f+mX2yRgP+BgkkHHFkdy631VDGZntcOJ3xpBYVySLaT/5tPBunYp2mbduxtHvAQcSfIF8E+Sir9UKNrudxHxA5JBv8ak466019E5dzReikjmSih8wR0SERdm3Ndsu5z4rR49QjLCJ8BnSabdA1hCktAhGZ+9Z6mdJX0IWB8R00km8NhmuGNJn0oTOcBIYCvwBrAG2L1o0+2d89fAOemEOEOAj5UI5VHgeEkHpufsI+nPSEZSHSHpgHS7c0t9DrPtca8eq0eXAT+W9DWSWaoKo1b+CLhX0uPAXIpa+e0cDvyzpHdIRk68uMQ25wE3SVpP0qr/bERslfRfwF2SJpKMnrm9c94DnEDyq+Ilkqn33ici2iSdD9whadd09Tcj4iUlM4HdL+l1ki+2qpln2qqfR+c0M2swLvWYmTUYJ34zswbjxG9m1mCc+M3MGowTv5lZg3HiNzNrME78ZmYN5v8DjYySQ0opyykAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the distribution of scores\n",
    "student_score.plot(x='Hours', y='Scores', style='.', color= 'red')  \n",
    "plt.title('Hours vs Percentage')  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e3b8219",
   "metadata": {},
   "source": [
    "### The next step is to divide the data into \"attributes\" (inputs) and \"labels\" (outputs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d78b2d5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((25,), (25, 1))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = student_score.iloc[:, :-1].values  \n",
    "y = student_score.iloc[:, 1].values  \n",
    "y.shape, x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c8295bdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split this data into training and test sets\n",
    "from sklearn.model_selection import train_test_split \n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y,test_size=0.1, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afe29944",
   "metadata": {},
   "source": [
    "### Training the Algorithm\n",
    "\n",
    "We have split our data into training and testing sets, and now is finally the time to train our algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a04aef19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression  \n",
    "reg = LinearRegression()  \n",
    "reg.fit(x_train, y_train) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf8956e0",
   "metadata": {},
   "source": [
    "###  Plotting the regression line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bff65c2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zahloq+RSmzf3ruavvRY++tHKx2lWIVmmcz5L0nqhnD9VNhyzApRrlZwl8fvirdWpLE3aRgGfBf4O2KFrf0Qck2NcZtWTpVVyqT/+EfbrsXbQsmWw9945BWhWWVmGem4Cvk+y4PrHSTpqemUUaxxdrZLnz0+Sfn/Vvqt8awBZLu7uERHXAxsi4p6I+N/AETnHZVZdEyfCxRf3nfSvvLJ30t+0yUnf6lKWin9D+tgh6SRgObBXfiGZ1ZieCX/8eHjiiUJCMauELIn/C5JeB/wr8E1gV+CTeQZlVhOGD09uvCrlCt8aQJahnlURsToiHomId0fE24Hnsxxc0jJJD0taKKk93TdS0jxJS9LH3YfyBzCruA0bkiq/NOlfc42TvjWMLIn/mxn39eXdEXFIRHQtyDINaI2ICUArA1iA3Sx3UjKzp1QETJ1aTDxmOehzqEfSROAdwChJny751a4kN28N1qnApPT5LGA+cNEQjmc2dAsXwtve1n3f00/DXr6cZY2nvzH+7YCd09fsUrL/ReAfMh4/gLslBXBNRMwAxkREB0BEdEgaXe6NkqYCUwHGjRuX8XRmg+ApmtZk+kz8EXEPcI+kmRHx5CCPf2RELE+T+zxJi7O+Mf2QmAHQ0tLif4VWeeeeCzNndt+3eXP5DwKzBpJlVs/2kmYA40tfn+XO3YhYnj6ulHQ7cBjJQi5j02p/LMkiL2bV1TO5jxoFK/1X0ZpDlsT/A+C/gOuATVkPLGknYJuIeCl9/h7gcuBOkrt/r0wf7xho0GaD5mEds0yJf2NEfGcQxx4D3K7kH9q2wM0RMVfS74E5ks4DngLOGMSxzQbm5Zdhxx2777vmGs/WsaaUJfH/WNK/ALcDr3btjIh+5/JHxFLg4DL7nyNZuN2sOipd5be1ZevrY1ajsiT+KenjZ0r2BbBv5cMxq6Cf/xwmT+6+769/hTFjBn/MofTuN6sRWfrx71ONQMwqKq+x/MH27jerIVu9c1fSjpI+l87sQdIESSfnH5rZIJx0UvklECt1Aberd/+wYdl695vVoCxDPTcAC0ju4gV4hmSmz0/yCspsUHom/IMOqvyatwPp3W9Wo7Ik/jdGxAclnQkQES9LvsPFaki1p2hOnOiEb3UtS5O29ZJGkFzQRdIbKZndY1aY1at7J/3vf9/z8s22IkvFfwkwF3iDpJuAI4Fz8gzKbKt8I5bZoG214o+IecDpJMl+NtASEfPzDcusD7ff3jvpP/+8k77ZAGy14pf0PuAXEfHTdHs3SadFxI/yDs6sG1f5ZhWRZYz/kohY3bURES+QDP+YVUdLS75TNM2aTJbEX+41Wa4NmA2dBAsWbNmePNkJ32yIsiTwdklXAd8mmdlzPsm8frP8eFjHLDdZKv7zgfXA94E5wMvAJ/IMympQWxtMn5485qmzs3fSnzvXSd+sgvqt+CUNA+6IiOOqFI/Vomo1JnOVb1YV/Vb8EbEJWCfpdVWKx2pRucZklXTDDb2T/po1Q0/61fqWYlZnsozxvwI8LGkesLZrZ0RckOUE6beGduAvEXGypJEkw0bjgWXAByJi1QDjtmrqakzWVfFXsjFZXlW+2yeb9SnLGP9Pgf8L3EtyUbfrJ6sLgUUl29OA1oiYALSm21bLuhqTXXFF5RLouHH5TtHM+1uKWR3L0o9/VtqrZ1xEPD6Qg0vaCzgJ+CLw6XT3qcCk9PksYD5w0UCOawWoVGOyCNimR71x9tlw441DP3apPL+lmNW5LHfuvhf4CrAdsI+kQ4DLI+KUDMf/OvBZYJeSfWMiogMgIjokje7jvFOBqQDjxo3LcCqredW8eOv2yWZ9yjLUcylwGPACQEQsBLa6Kle6WMvKiBjUnP+ImBERLRHRMmrUqMEcwmrF8uW9k/5vfpP/jJ2JE+Hii530zXrIcnF3Y0Ss7tGCP8u/2COBUySdCOwA7CrpRmCFpLFptT8WWDngqK1+eIqmWc3JUvE/IuksYFi67OI3gfu29qaIuDgi9oqI8cCHSBq9fRi4ky0LuE8B7hhc6FbTvvvd3kl//XonfbMakKXiPx/4d5LFV24G7gK+MIRzXgnMkXQe8BRwxhCOZbXIVb5ZTesz8UvaAfg48CbgYWBiRGwczEnS/v3z0+fPAccO5jhW4978ZliypPs+J3yzmtPfUM8soIUk6Z9AMrPHrLeIpMovTfqXX+6kb1aj+hvqOSAiDgSQdD3wu+qEZHXFwzpmdae/in9D15PBDvFYA1u6tHfSX7TISd+sDvRX8R8s6cX0uYAR6baAiIhdc4/OapOrfLO61mfFHxHDImLX9GeXiNi25LmTfjO66qreSX/TJid9szrjJRQtm54Jf/RoWLFicMdqa3MrBbMCOfFb/173Onjxxe77hlLhu12yWeGy3LlrzWjTpqTKL0363/jG0Id13C7ZrHCu+K23PC/eul2yWeFc8dsWS5b0TvrLllX24m0ei7qY2YC44rdEtXvlO+GbFcYVf7O7+ureSX/zZk/RNGtgrvibWc+E/973wp13FhOLmVWNE38zGjcOnn66+z5X+GZNw0M9zWT9+qTKL036P/qRk75Zk8mt4k/7+d8LbJ+e59aIuETSSOD7wHhgGfCBiFiVVxyWcn8dM0vlWfG/ChwTEQcDhwDHSzoCmAa0RsQEoDXdtrw89FDvpN/Z6aRv1sRyq/gjIoA16ebw9CeAU4FJ6f5ZJCtzXZRXHA2nvz43PX9XrSrfvXfM6kquF3clDQMWkCzf+O2IuF/SmIjoAIiIDkmj84yhofTX56b0dxJs7LGEQl4VvnvvmNWdXC/uRsSmiDgE2As4TNJbs75X0lRJ7ZLaOzs7c4uxrvTX56b0d6VJf8qUfId13HvHrO5UZTpnRLwgaT5wPLBC0ti02h8LrOzjPTOAGQAtLS0ekIb++9zcd1+SfEtVYxzfvXfM6k5uFb+kUZJ2S5+PAI4DFgN3AlPSl00B7sgrhoZTrs9N19DOT36y5XWV6KI5lJjMrKYpckoQkg4iuXg7jOQDZk5EXC5pD2AOMA54CjgjIp7v71gtLS3R3t6eS5x1zVM0zawfkhZEREvP/XnO6nkIeFuZ/c8Bx+Z13qbwpz/BhAnd961bByNGFBOPmdUV37lbb6TuSf/970+q/Eon/bY2mD49eTSzhuJePfXizjvh1FO77/MUTTMbBFf89UDqnvRvvNFTNM1s0Jz4a9kFF/S+gBsBZ5+d73m7pmgOG+YpmmYNyEM9teiVV3qP2S9bBnvvXZ3zd03RdBsGs4bkxF9rRo6EVSXNSsePhyeeyPbeSvbM8fKIZg3Lib9WlJuiuX49DB+e7f2+IGtmGXmMPw8DnQrZc4rmtGnJWH7WpA++IGtmmbnir7SBVN5z5sAHP9h932Bn67hnjpll5MRfaeUq73KJv+dsnbvvhsmTB39eX5A1s4yc+Ctta5X3uefCzJnd91VqTr4vyJpZBk78ldZX5b12Ley8c/fXLl8OY8dWO0Iza3JO/HnoWXkPGwabN2/ZPvhgWLiw6mGZmYETf74efRTe2mPRsY0bkw8CM7OCeDpnXqTuSf8LX0jG8oea9N0108yGyBV/pf3yl3DMMd33VerirW/SMrMKyHPpxTdI+qWkRZIelXRhun+kpHmSlqSPu+cVQ1VFJFV+adK/997KdtH0TVpmVgF5DvVsBP41IvYHjgA+IekAYBrQGhETgNZ0u/YMZEjlq1+FbUr+U06cmCT8o46qbEzummlmFZDn0osdQEf6/CVJi4DXA6cCk9KXzQLmAxflFcegZB1SWbcOdtqp+77Vq2HXXfOJyzdpmVkFVOXirqTxJOvv3g+MST8Uuj4cRvfxnqmS2iW1d3Z2ViPMLbIMqZx8cvek//nPJ1V+Xkm/y8SJcPHFTvpmNmi5X9yVtDNwG/DJiHhRPVsV9CEiZgAzAFpaWnJcbqqM/u6+feIJ2Hff7q/fvLl3CwYzsxqVa+KXNJwk6d8UET9Md6+QNDYiOiSNBVbmGcOg9DWk0jO5/+QncNJJ1Y7OzGxIckv8Skr764FFEXFVya/uBKYAV6aPd+QVw5CU3n07dy6ccEL33+e55q2ZWY7yrPiPBD4CPCxpYbrv30gS/hxJ5wFPAWfkGMPQRHSfrQOwZAm86U3FxGNmVgF5zur5NdDXwPexeZ23m6EsRThrFpxzzpbtyZOT1slmZnWuce/cHexdruvXJ6thPfXUln1r1vSetmlmVqcat1fPYO5ynTkTtt9+S9KfPz8Z7nHSN7MG0rgV/0CWInz2WRg1asv26afDrbd6iqaZNaTGTfxZ73K96CL48pe3bP/5z73n6ZuZNZDGTfzQ/1KEixfD/vtv2b70UrjkkqqEZWZWpMZO/OVEJHPy77pry75Vq2C33QoLycysmhr34m45ra3JvPyupD97dvJB4KRvZk2kOSr+V16B8eNhxYpke//94cEHYfjwQsMyMytC41f8114LI0ZsSfptbfDYY076Zta0Gjvx33ADTJ2aPD/rrKSL5hFHFBuTmVnBGnuo54ADkkQ/e3Yy1GNmZg2e+A8/PNvSiWZmTaSxh3rMzKwXJ34zsybjxG9m1mRyS/ySvitppaRHSvaNlDRP0pL0cfe8zm9mZuXlWfHPBI7vsW8a0BoRE4DWdNvMzKoot8QfEfcCz/fYfSowK30+Czgtr/ObmVl51R7jHxMRHQDp4+i+XihpqqR2Se2dnZ1VC9DMrNHV7MXdiJgRES0R0TKqdJEUMzMbkmrfwLVC0tiI6JA0FliZ5U0LFix4VtKTGc+xJ/DsoCPMj+PKrhZjgtqMqxZjgtqMqxZjgnzj2rvczmon/juBKcCV6eMdWd4UEZlLfkntEdEyuPDy47iyq8WYoDbjqsWYoDbjqsWYoJi48pzOORtoA/aT9Iyk80gS/mRJS4DJ6baZmVVRbhV/RJzZx6+OzeucZma2dTV7cXcIZhQdQB8cV3a1GBPUZly1GBPUZly1GBMUEJciotrnNDOzAjVixW9mZv1w4jczazINk/jLNYWrBZLeIOmXkhZJelTShTUQ0w6SfifpwTSmy4qOqYukYZL+IOknRcfSRdIySQ9LWiipveh4ukjaTdKtkhanf78mFhzPful/o66fFyV9ssiYukj6VPp3/RFJsyXtUAMxXZjG82i1/zs1zBi/pKOBNcB/R8Rbi46nS3qj2tiIeEDSLsAC4LSIeKzAmATsFBFrJA0Hfg1cGBG/LSqmLpI+DbQAu0bEyUXHA0niB1oioqZu/pE0C/hVRFwnaTtgx4h4oeCwgOQDHPgLcHhEZL35Mq9YXk/yd/yAiHhZ0hzgZxExs8CY3grcAhwGrAfmAv8cEUuqcf6Gqfj7aApXuIjoiIgH0ucvAYuA1xccU0TEmnRzePpTeAUgaS/gJOC6omOpdZJ2BY4GrgeIiPW1kvRTxwJ/Ljrpl9gWGCFpW2BHYHnB8ewP/DYi1kXERuAe4H3VOnnDJP56IGk88Dbg/oJD6RpSWUjSNmNeRBQeE/B14LPA5oLj6CmAuyUtkDS16GBS+wKdwA3p0Nh1knYqOqgSHwJmFx0EQET8BfgK8BTQAayOiLuLjYpHgKMl7SFpR+BE4A3VOrkTf5VI2hm4DfhkRLxYdDwRsSkiDgH2Ag5Lv3oWRtLJwMqIWFBkHH04MiIOBU4APpEOKxZtW+BQ4DsR8TZgLTWyvkU67HQK8IOiYwFIF3w6FdgH+FtgJ0kfLjKmiFgEfAmYRzLM8yCwsVrnd+KvgnQc/Tbgpoj4YdHxlEqHB+bTe9GcajsSOCUdT78FOEbSjcWGlIiI5enjSuB2knHZoj0DPFPyTe1Wkg+CWnAC8EBErCg6kNRxwBMR0RkRG4AfAu8oOCYi4vqIODQijiYZpq7K+D448ecuvZB6PbAoIq4qOh4ASaMk7ZY+H0HyD2NxkTFFxMURsVdEjCcZJvhFRBRalQFI2im9KE86lPIekq/phYqIvwJPS9ov3XUsUNiEgR7OpEaGeVJPAUdI2jH993gsybW2QkkanT6OA06niv/Nqt2dMzdpU7hJwJ6SngEuiYjri40KSCrZjwAPp2PqAP8WET8rLiTGArPSmRfbAHMiomamT9aYMcDtSb5gW+DmiJhbbEivOR+4KR1aWQqcW3A8pOPVk4F/KjqWLhFxv6RbgQdIhlP+QG20b7hN0h7ABuATEbGqWidumOmcZmaWjYd6zMyajBO/mVmTceI3M2syTvxmZk3Gid/MrMk48VtdkrSmx/Y5kr5VxfMfIen+tAvlIkmXpvsnSRrwzUGSZkr6h/T5dZIOGMB7J9VSN1OrfQ0zj9+sEiQNi4hNGV46C/hARDyY3g/RdSPVJJIusfcNNoaI+Ohg32uWhSt+aziS9pbUKumh9HFcuv+1qjrdXpM+TkrXTLiZ5Ea7nST9NF2v4BFJHyxzmtEkDb+6+h49ljbh+zjwqfSbwFH9nFOSviXpMUk/TY/X9Zr5klrS5++R1CbpAUk/SHs+Iel4JX34f01y16dZZk78Vq9GqGTRD+Dykt99i2RdhoOAm4BvZDjeYcC/R8QBJH2LlkfEwenaDuXu1P0a8Lik2yX9k6QdImIZ8F/A1yLikIj4VT/nex/Jt4QDgY9RpneMpD2BzwHHpU3i2oFPK1lE5FrgvcBRwN9k+POZvcaJ3+rVy2lyPSTtMvr5kt9NBG5On38PeGeG4/0uIp5Inz8MHCfpS5KOiojVPV8cEZeTLBhzN3AW5T8c+nM0MDv9trAc+EWZ1xwBHAD8Jv1wmwLsDbyFpOnYkkhuva+JZnZWP5z4rRl09SXZSPp3Pm3WtV3Ja9a+9uKIPwJvJ/kAmC6p9EOFktf9OSK+Q9L06+C070pP/Z1za/1SRLJWQtcH3AERcV7G95r1yYnfGtF9JB0+Ac4mWXYPYBlJQoekP/vwcm+W9LfAuoi4kWQBj17tjiWdlCZygAnAJuAF4CVgl5KX9nXOe4EPpQvijAXeXSaU3wJHSnpTes4dJb2ZpJPqPpLemL7uzHJ/DrO+eFaPNaILgO9K+gzJKlVdXSuvBe6Q9DuglZIqv4cDgf8naTNJ58R/LvOajwBfk7SOpKo/OyI2SfoxcKukU0m6Z/Z1ztuBY0i+VfyRZOm9biKiU9I5wGxJ26e7PxcRf1SyEthPJT1L8sFWM+tMW+1zd04zsybjoR4zsybjxG9m1mSc+M3MmowTv5lZk3HiNzNrMk78ZmZNxonfzKzJ/H/N0gosjwkcvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the regression line\n",
    "line = reg.coef_ * x + reg.intercept_\n",
    "\n",
    "# Plotting for the test data\n",
    "plt.scatter(x, y, color='red', marker='.')\n",
    "plt.plot(x, line, color= 'red');\n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d5fd85f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fa370256",
   "metadata": {},
   "source": [
    "### Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "befe6054",
   "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>17.302210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>34.055294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>75.445267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  17.302210\n",
       "1      27  34.055294\n",
       "2      69  75.445267"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=reg.predict(x_test) # predicted testset values\n",
    "actual_predicted=pd.DataFrame({'Actual':y_test,'Predicted':y_pred})\n",
    "actual_predicted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ba31a81",
   "metadata": {},
   "source": [
    "now ploting it in graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "37f1c4e2",
   "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": [
    "plt.plot(x_test,y_test,label=\"test\")\n",
    "plt.plot(x_test,y_pred,color=\"red\",label=\"predict\")\n",
    "plt.xlabel(\"Hours Studied\")\n",
    "plt.ylabel(\"Percentage Score\")\n",
    "leg=plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ac4ffe",
   "metadata": {},
   "source": [
    "### You can also test with your own data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4b0da06f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours studied = [[2], [5.4], [8], [9]]\n",
      "Predicted Score  = [22.22958753 55.73575592 81.35811999 91.2128754 ]\n"
     ]
    }
   ],
   "source": [
    "hours = [[2],[5.4],[8],[9]]\n",
    "own_pred = reg.predict(hours)\n",
    "print(\"No of Hours studied = {}\".format(hours))\n",
    "print(\"Predicted Score  = {}\".format(own_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "286a0a91",
   "metadata": {},
   "source": [
    "### Evaluating the model\n",
    "\n",
    "The final step is to evaluate the performance of algorithm. This step is particularly important to compare how well different algorithms perform on a particular dataset. For simplicity here, we have chosen the mean square error. There are many such metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0058e2d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 5.399450313612007\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', \n",
    "      metrics.mean_absolute_error(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8a39d1f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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