Task2_To_Explore_Supervised_Machine_Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2 - To Explore Supervised Machine Learning (By Vaibhav Gulati)\n",
    "\n",
    "**Objective :** To will predict the percentage of marks that a student is expected to score based upon the number of hours studied.\n",
    "\n",
    "**Data :** Given Numbers of hours and Marks obtained by student\n",
    "**Dataset Link :** \"http://bit.ly/w-data\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing all libraries required in this notebook\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Has been imported\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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",
       "  </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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading data from remote link\n",
    "\n",
    "url = \"http://bit.ly/w-data\"\n",
    "data = pd.read_csv(url)\n",
    "print(\"Data Has been imported\")\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basics statistics of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean Hours of study is 5 hours and score obtained is 51"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>Hours</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scores</th>\n",
       "      <td>0.976191</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours    Scores\n",
       "Hours   1.000000  0.976191\n",
       "Scores  0.976191  1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation of Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aYMIEWL/+7XWDBsEzz8CoUcXF1QVJiyKiqf16t/jNzMpRR11bTvxmZuWoo66tXGv1mJnVjToq2ewWv5lZgykr8Ut6n6Sz0/IISbvnG5aZmeWl28Qv6VLgq8DX0qoBwKw8gzIzs/yU0+L/KHAy8BpARKwAts8zKDMzy085iX9DZIP9A0DSkHIOLGkvSQ+VPF6VdJGkoZLmS1qannfamg9gZmY9U07inyvpJ8COkj4L3An8tLudIuKpiJgSEVOAg4B1wM3AxcCCiJgILEivzcysQroczpkmSZ8D7A28CuwFXBIR83t4nqOBP0bEc5JOAaam9TOBhWTXEMzMrAK6TPwREZJ+HREHAT1N9qWmATek5Z0joiUdv0XSyK04rpnVk5YWmDYN5syp2jII9aCcrp57JR3c2xNI2obs4vAve7jfuZKaJTW3trb29vRmVkvqoORxLSgn8X+QLPn/UdIjkh6V9EgPznE88GBErEyvV0oaDZCeV3W0U0TMiIimiGgaMWJED05nZjWpTkoe14JyEv/xwATgKOAk4MT0XK4zeLubB+BWYHpang7c0oNjmVm9qpOSx7WgrLLMkiYD708vfx8RD5d1cGkw8DwwISJeSeuGAXOBccAy4LSIeLGr47gss1mdq8GSx7Wg12WZJV0IzAZGpscsSeeXc9KIWBcRw9qSflq3OiKOjoiJ6bnLpG9mZarluWDrqORxLSinq+cc4NCIuCQiLgEOAz6bb1hm1mO1fGG0jkoe14JyEr+AzSWvN6d1ZlYtav3C6OLFELHlo45KIVeTchL/tcB9ki6TdBlwL3BNrlGZWc/4wqj1QLkXdw8E3kfW0v9dRFT0a9gXd8264Auj1omtubh7GLA0In4QEf8K/I+kQ/MI0sx6wRdGrYfK6er5MbC25PVraZ2ZVQNfGLUeKmfOXUVJf1BEvCnJc/WaVQtfALUeKqfF/4ykCyQNSI8LgWfyDszMzPJRTuL/PHAE8Of0OBQ4N8+gzMwsP9122UTEKrKyymZmVgc6bfFL+qykiWlZkn4m6ZVUofPAyoVoZmZ9qauunguBZ9PyGcBksiqdXwb+Nd+wzMwsL10l/k0RsTEtnwj8RyqwdidQ1oTrZtbHarkQm1WNrhL/m5JGSxpINmfunSXvDco3LDPrUC0XYrOq0VXivwRoJuvuuTUiHgeQ9AE8nNOs8mq9EJtVjU4Tf0TMA3YDJkVEaRnmZuD0vAMzs3ZciM36SJfj+CNiU0S81G7daxGxtrN9zCwHba39ttIMGza41W+9Vs4NXL0maUdJN0p6UtISSYdLGippvqSl6XmnPGMwqzq9uUDrQmzWh3JN/GTDPm+PiL3JhoMuAS4GFkTERGBBem3WOHpzgdaF2KwPdVuPX5KAT5JNmP4PksYBoyLi/m722wF4OO0XJeufAqZGRIuk0cDCiNirq2O5Hr/VjdLa+a6ZbznrdT1+4N+Aw8lu4gJYA1xVxn4TgFbgWkmLJV0taQiwc0S0AKTnkZ0EfK6kZknNra2tZZzOrAb4Aq1VgXIS/6ER8UVgPUC62LtNGfv1Bw4EfhwRB5DV8S+7WyciZkREU0Q0jRgxotzdzKqXL9BalSgn8W+U1A8IAEkjgDe73gWA5cDyiLgvvb6R7ItgZeriIT2v6nHUZrXIF2itSpST+H8A3AyMlPQd4G7gu93tFBEvAM9Lauu/Pxp4ArgVmJ7WTQdu6WnQZjXJF2itSpRTlnm2pEVkiVvAqRGxpMzjnw/MlrQN2d2+Z5N92cyVdA6wDDitV5Gb1RrPlGVVotvEL2koWXfMDSXrBpQUcOtURDwEbHFFmexLxMzMClBOV8+DZKNzngaWpuU/SXpQ0kF5BmdmZn2vnMR/O3BCRAyPiGHA8cBc4AtkQz3NzKyGlJP4myLiv9teRMQdwJERcS+wbW6RmZlZLrrt4wdelPRV4Bfp9enAS2mIZznDOs3MrIqU0+L/BDAG+DXZ0MtxaV0/4OP5hWZmZnkoZzjnX8iGZXbkf/o2HDMzy1u3LX5JIyT9s6T/kvSbtkclgjOrGM9law2knK6e2cCTwO7At8imYnwgx5jMKs9z2VoDKSfxD4uIa4CNEXFXRHwaOCznuMwqx3PZWoMpq0hbem6R9BFJB5Bd7DWrDy6VbA2mnMT/j5LeDfwt8HfA1cBFuUZlVikulWwNqJzE/1JEvBIRj0XEByPiIODFvAMzqwiXSrYGVE7i/2GZ68xqj0slWwPqdBy/pMOBI4ARkr5c8tYOZDdvmdU+l0q2BtTVDVzbANulbbYvWf8q8Nd5BmVmZvnpNPFHxF3AXZKui4jnKhiTmZnlqJwibdtKmgGML90+Io7qbkdJzwJrgM3ApohoShO7zEnHexb4eJrA3czMKqCcxP9L4N/JhnFu7sU5Ppjq/bS5GFgQEZdLuji9/movjmtmZr1QTuLfFBE/7sNzngJMTcszgYU48ZuZVUw5wzn/U9IXJI2WNLTtUebxA7hD0iJJ56Z1O0dEC0B6HtnRjpLOldQsqbm1tbXM05mZWXfKafFPT89fKVkXwIQy9n1vRKyQNBKYL+nJcgOLiBnADICmpqYodz8zM+taOfX4d+/twSNiRXpeJelm4BBgpaTREdEiaTSwqrfHNzOzniunHv9gSd9II3uQNFHSiWXsN0TS9m3LwLHAY8CtvP0rYjrZrF5mZlYh5XT1XAssIruLF2A52Uifed3stzNws6S281wfEbdLegCYK+kcYBlwWm8CNzOz3ikn8e8REadLOgMgIl5XyuZdiYhngMkdrF8NHN3jSM2qRUsLTJsGc+bAqFFFR2PWY+WM6tkgaRDZBV0k7QG8kWtUZtXMs3VZjSsn8V8K3A6MlTQbWAD8fa5RmVUrz9ZldaDbxB8R84GPAWcBNwBNEbEw37DMqpRn67I6UM6ono+S3b17W0TMAzZJOjX/0MyqjGfrsjpRVldPRLzS9iIiXibr/jFrLJ6ty+pEOYm/o23KGQ1kVl88W5fViXISeLOk7wFXkY3sOZ9sXL9ZY/FsXVYnymnxnw9sIKuhPxd4HfhinkGZmVl+umzxS+oH3BIRx1QoHjMzy1mXLf6I2Aysk/TuCsVjZmY5K6ePfz3wqKT5wGttKyPigtyiMjOz3JST+G9LDzMzqwPl1OOfmWr1jIuIpyoQk1UjFyYzqxvl3Ll7EvAQWb0eJE2RdGvegVmVcWEys7pRznDOy8hmznoZICIeAno9K5fVoFotTNbSAh/4QO3Ea1Yh5ST+TaUlGxLPgdtIarUwmX+lmHWonMT/mKRPAP3StIs/BMq+R11SP0mLJc1Lr4dKmi9paXreqZexWyXUamGyWv2VYlYB5d65uy/Z5CvXA68AF/XgHBcCS0peXwwsiIiJZLX9L+7BsazSarUwWa3+SjGrgE4Tv6SBki4C/i/Z3LiHR8TBEfGNiFhfzsEljQE+AlxdsvoUYGZangm4xHM1q8XCZLX6K8WsQrpq8c8EmoBHgeOBK3tx/H8hm62rtMm4c0S0AKTnkb04rlXK4sUQseWjmguW1eqvFLMK6Woc/z4RsR+ApGuA+3tyYEknAqsiYpGkqT0NTNK5wLkA48aN6+nu1shq8VeKWQV1lfg3ti1ExCZJPT32e4GTJZ0ADAR2kDQLWClpdES0SBoNrOpo54iYAcwAaGpq8igiK181/xoxqwJddfVMlvRqeqwB9m9blvRqdweOiK9FxJiIGA9MA34TEZ8CbgWmp82mA7ds5WcwM7Me6LTFHxH9cjrn5cBcSeeQXTQ+LafzmJlZByoyhWJELAQWpuXVwNGVOK+ZmW2pnHH8ZmZWR5z4zcwajBO/mVmDceI3M2swTvxWeS6XbFYoJ36rPJdLNiuUE79VlsslmxXOid8qy+WSzQrnxG+V43LJZlXBid8qx+WSzaqCE79Vjsslm1WFitTqMQNcLtmsSrjFb2bWYJz4zcwajBO/mVmDceI3M2swTvxmZg0mt8QvaaCk+yU9LOlxSd9K64dKmi9paXreKa8Y6lJXBc6KKn7momtmNSXPFv8bwFERMRmYAnxY0mHAxcCCiJgILEivrVxdFTgrqviZi66Z1RRFRP4nkQYDdwPnAf8BTI2IFkmjgYURsVdX+zc1NUVzc3PucVa9lhaYMAHWr4dBg+CZZ2DUqO7fKyomMyuUpEUR0dR+fa59/JL6SXoIWAXMj4j7gJ0jogUgPY/sZN9zJTVLam5tbc0zzNrRVYGzooqfueiaWc2pVIt/R+Bm4Hzg7ojYseS9lyKiy35+t/h5Z8u6TVsLO6Lz9/JsfXcVk1v9ZoUrpMXfJiJeBhYCHwZWpi4e0vOqSsRQ87oqcFZU8TMXXTOrSXmO6hmRWvpIGgQcAzwJ3ApMT5tNB27JK4a60lWBs6KKn7nomllNyrNI22hgpqR+ZF8wcyNinqR7gLmSzgGWAaflGEP9qMYCZ9UYk5l1K7fEHxGPAAd0sH41cHRe57U+0tIC06bBnDnurzerM75z1zrmsflmdcuJ37bkCdHN6poTv23JY/PN6poTv72TJ0Q3q3tO/PWkL4qleWy+Wd1z4q8nfXFB1mPzzeqeE38eiihT3FcXZBcvzkpAtH94zL5Z3XDiz0MRQyF9QdbMylSRIm1bq6aKtBVRptjF0sysA4UWaWsoRbS8fUHWzHrAib8vFTUU0hdkzawHnPj7UlEtb1+QNbMecOLvS255m1kNyLMsc+NxC9vMaoBb/LWmiHsEzKyuOPHXGpdLNrOtlOfUi2Ml/VbSEkmPS7owrR8qab6kpem5y4nWC1ONLWuXSzazPpBni38T8LcRMQk4DPiipH2Ai4EFETERWJBeV59qbFn77lwz6wMVu3NX0i3Aj9JjakS0SBoNLIyIvbrat+J37hZx921PYmpTLbGZWVUq9M5dSePJ5t+9D9g5IloA0vPITvY5V1KzpObW1tZKhPm2amxZ++5cM+sjuSd+SdsBNwEXRcSr5e4XETMioikimkaMGJFfgO1V60QkvkfAzPpIrolf0gCypD87In6VVq9MXTyk51V5xtBj1dqy9t25ZtZH8hzVI+AaYElEfK/krVuB6Wl5OnBLXjH0ilvWZlbn8rxz973AmcCjkh5K674OXA7MlXQOsAw4LccYes4taDOrc7kl/oi4G1Anbx+d13nfoaUFpk2DOXM88sXMLKnvO3ercSy+mVnB6jfx+y5XM7MO1W/ir8ax+GZmVaA+E3+1jsU3M6sC9Zn4q3UsvplZFajPxO+x+GZmnarPGbg8Ft/MrFP12eI3M7NOOfGbmTUYJ34zswbjxG9m1mCc+M3MGkzFpl7cGpJagefK3Hw48Jccw+ktx1W+aowJqjOuaowJqjOuaowJ8o1rt4jYYiarmkj8PSGpuaM5JovmuMpXjTFBdcZVjTFBdcZVjTFBMXG5q8fMrME48ZuZNZh6TPwzig6gE46rfNUYE1RnXNUYE1RnXNUYExQQV9318ZuZWdfqscVvZmZdcOI3M2swdZP4Jf1M0ipJjxUdSylJYyX9VtISSY9LurAKYhoo6X5JD6eYvlV0TG0k9ZO0WNK8omNpI+lZSY9KekhSc9HxtJG0o6QbJT2Z/n0dXnA8e6W/UdvjVUkXFRlTG0lfSv/WH5N0g6SBVRDThSmexyv9d6qbPn5JRwJrgf+IiPcUHU8bSaOB0RHxoKTtgUXAqRHxRIExCRgSEWslDQDuBi6MiHuLiqmNpC8DTcAOEXFi0fFAlviBpoioqpt/JM0Efh8RV0vaBhgcES8XHRdkX+DAn4FDI6Lcmy/zimVXsn/j+0TE65LmAv8VEdcVGNN7gF8AhwAbgNuB8yJiaSXOXzct/oj4HfBi0XG0FxEtEfFgWl4DLAF2LTimiIi16eWA9Ci8BSBpDPAR4OqiY6l2knYAjgSuAYiIDdWS9JOjgT8WnfRL9AcGSeoPDAZWFBzPJODeiFgXEZuAu4CPVurkdZP4a4Gk8cABwH3FRvJWl8pDwCpgfkQUHhPwL8DfA292t2GFBXCHpEWSzi06mGQC0Apcm7rGrpY0pOigSkwDbig6CICI+DNwJbAMaAFeiYg7io2Kx4AjJQ2TNBg4ARhbqZM78VeIpO2Am4CLIuLVouOJiM0RMQUYAxySfnoWRtKJwOjzBgsAAAScSURBVKqIWFRkHJ14b0QcCBwPfDF1KxatP3Ag8OOIOAB4Dbi42JAyqdvpZOCXRccCIGkn4BRgd2AXYIikTxUZU0QsAa4A5pN18zwMbKrU+Z34KyD1o98EzI6IXxUdT6nUPbAQ+HDBobwXODn1p/8COErSrGJDykTEivS8CriZrF+2aMuB5SW/1G4k+yKoBscDD0bEyqIDSY4B/hQRrRGxEfgVcETBMRER10TEgRFxJFk3dUX698GJP3fpQuo1wJKI+F7R8QBIGiFpx7Q8iOx/jCeLjCkivhYRYyJiPFk3wW8iotBWGYCkIemiPKkr5Viyn+mFiogXgOcl7ZVWHQ0UNmCgnTOokm6eZBlwmKTB6f/Ho8mutRVK0sj0PA74GBX8m9XNZOuSbgCmAsMlLQcujYhrio0KyFqyZwKPpj51gK9HxH8VGNNoYGYaefEuYG5EVM3wySqzM3Bzli/oD1wfEbcXG9Jbzgdmp66VZ4CzC46H1F/9IeBzRcfSJiLuk3Qj8CBZd8piqqN8w02ShgEbgS9GxEuVOnHdDOc0M7PyuKvHzKzBOPGbmTUYJ34zswbjxG9m1mCc+M3MGowTv9UkSWvbvT5L0o8qeP7DJN2XqlAukXRZWj9VUo9vDpJ0naS/TstXS9qnB/tOraZqplb96mYcv1lfkNQvIjaXselM4OMR8XC6H6LtRqqpZFVi/9DbGCLiM73d16wcbvFb3ZG0m6QFkh5Jz+PS+rda1en12vQ8Nc2ZcD3ZjXZDJN2W5it4TNLpHZxmJFnBr7a6R0+kInyfB76Ufgm8v4tzStKPJD0h6bZ0vLZtFkpqSsvHSrpH0oOSfplqPiHpw8rq8N9NdtenWdmc+K1WDVLJpB/AP5S89yOyeRn2B2YDPyjjeIcA/yci9iGrW7QiIianuR06ulP3+8BTkm6W9DlJAyPiWeDfge9HxJSI+H0X5/so2a+E/YDP0kHtGEnDgW8Ax6Qicc3Al5VNIvJT4CTg/cCoMj6f2Vuc+K1WvZ6S65RUZfSSkvcOB65Pyz8H3lfG8e6PiD+l5UeBYyRdIen9EfFK+40j4h/IJoy5A/gEHX85dOVI4Ib0a2EF8JsOtjkM2Af4f+nLbTqwG7A3WdGxpZHdel8VxeysdjjxWyNoq0uyifRvPhXr2qZkm9fe2jjiaeAgsi+Af5JU+qVCyXZ/jIgfkxX9mpzqrrTX1Tm7q5cisrkS2r7g9omIc8rc16xTTvxWj/5AVuET4JNk0+4BPEuW0CGrzz6go50l7QKsi4hZZBN4bFHuWNJHUiIHmAhsBl4G1gDbl2za2Tl/B0xLE+KMBj7YQSj3Au+VtGc652BJf0VWSXV3SXuk7c7o6HOYdcajeqweXQD8TNJXyGapaqta+VPgFkn3AwsoaeW3sx/wz5LeJKuceF4H25wJfF/SOrJW/ScjYrOk/wRulHQKWfXMzs55M3AU2a+Kp8mm3nuHiGiVdBZwg6Rt0+pvRMTTymYCu03SX8i+2Kpmnmmrfq7OaWbWYNzVY2bWYJz4zcwajBO/mVmDceI3M2swTvxmZg3Gid/MrME48ZuZNZj/D7074zurVuJIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Hours vs. Scores scatter plot\n",
    "\n",
    "data.plot(x = 'Hours', y = 'Scores', style = '^', color = 'r')  \n",
    "plt.title('Hours vs Percentage')  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> It can be easily seen that hours and marks have a linear relation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preperation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividing the data into \"attributes\" (inputs) and \"labels\" (outputs)\n",
    "# Converting pandas dataframe into numpy arrays\n",
    "\n",
    "X = data.iloc[:, :-1].values  \n",
    "Y = data.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries used for supervised learning technique \n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Successfully done\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, Y_train)\n",
    "print('Training Successfully done')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Scores')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize =(12,12))\n",
    "training = plt.subplot(211)\n",
    "line = model.coef_ * X + model.intercept_\n",
    "training.scatter(X, Y)\n",
    "training.plot(X, line)\n",
    "plt.show\n",
    "training.set_title('Trained Set')\n",
    "training.set_xlabel('Hours')\n",
    "training.set_ylabel('Scores')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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) # Testing data - In Hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16.88414476 33.73226078 75.357018   26.79480124 60.49103328]\n"
     ]
    }
   ],
   "source": [
    "Y_pred = model.predict(X_test)\n",
    "print(Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe = pd.DataFrame({'Actual': Y_test, 'Predicted': Y_pred})\n",
    "dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation of Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.9806835759589493\n"
     ]
    }
   ],
   "source": [
    "#R-square value\n",
    "slope, intercept, r, p, std_err = stats.linregress(Y_test, Y_pred)\n",
    "print('Accuracy', r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Score is 0.98 which means 98% of our data fits the model and is giving accurate results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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