Prediction using Supervised Machine Learning .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f2576317",
   "metadata": {},
   "source": [
    "# Task 1 : Prediction using Supervised Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1740bfb",
   "metadata": {},
   "source": [
    "To Predict the percentage of marks of the students based on the number of hours they studied"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "739b8e55",
   "metadata": {},
   "source": [
    "AUTHOR:NAGUBANDI ALEKHYA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97f6946d",
   "metadata": {},
   "source": [
    "Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1013b024",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66a4b2aa",
   "metadata": {},
   "source": [
    "Importing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "90054efb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/AdiPersonalWorks/Random/master/student_scores%20-%20student_scores.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5fd116ea",
   "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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "11e305eb",
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bfbce6dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hours', 'Scores']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = list(df.columns)\n",
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82a5e672",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[\"Hours\"].values.reshape(-1,1)\n",
    "Y = df[\"Scores\"].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c2c416bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.5],\n",
       "       [5.1],\n",
       "       [3.2],\n",
       "       [8.5],\n",
       "       [3.5],\n",
       "       [1.5],\n",
       "       [9.2],\n",
       "       [5.5],\n",
       "       [8.3],\n",
       "       [2.7],\n",
       "       [7.7],\n",
       "       [5.9],\n",
       "       [4.5],\n",
       "       [3.3],\n",
       "       [1.1],\n",
       "       [8.9],\n",
       "       [2.5],\n",
       "       [1.9],\n",
       "       [6.1],\n",
       "       [7.4],\n",
       "       [2.7],\n",
       "       [4.8],\n",
       "       [3.8],\n",
       "       [6.9],\n",
       "       [7.8]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "acd4cef9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[21],\n",
       "       [47],\n",
       "       [27],\n",
       "       [75],\n",
       "       [30],\n",
       "       [20],\n",
       "       [88],\n",
       "       [60],\n",
       "       [81],\n",
       "       [25],\n",
       "       [85],\n",
       "       [62],\n",
       "       [41],\n",
       "       [42],\n",
       "       [17],\n",
       "       [95],\n",
       "       [30],\n",
       "       [24],\n",
       "       [67],\n",
       "       [69],\n",
       "       [30],\n",
       "       [54],\n",
       "       [35],\n",
       "       [76],\n",
       "       [86]], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6db9638",
   "metadata": {},
   "source": [
    "Visualising Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5b77ede2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZCr+kP5Z0aHp7rqRLJHXlmszM/qDWgGuHTHHbzSYu61/NDcAzkk4CrgJOBK7Z35MkdUm6XtJPJW2WdKakGZJuk7QlvZ5+APnNmsqKjUPMWbaaE5d8kznLVrNi41Cm59UaiK37yMPyiGktLmvhfzYingbeBHw6Ij4A9GR43meAb0fEi4CXA5uBJcCqiDgZWJXeN2t5tQ7QZin+C07rZenC2fR2dSCgt6uDpQtn09UxNffc1nqyHtzdI+ltwCLgDemycf/iJD0POAt4N0BEPAU8Jel8YG662nJgLfChiYQ2a0YHOlPWWAOxrV27pa4ZrT0oIva/kvQS4CLg+xFxraQTgQsiYtk4zzkVuBL4CUlrfz1wKTAUEV1V6+2MiH26eyQtBhYDdHd39w0ODmZ6QSMjI3R2dmZat5GcK7syZoIDz7VpaFfNx2b3Hjmpbbbqe5WHMmaCfHMNDAysj4j+0cszFX4ASR3A8yPi/ozr9wN3AXMi4m5JnwEeBy7OUvir9ff3x7p16zLlXLt2LXPnzs20biM5V3ZlzAQHnmvOstUMjXF2Tm9XB3cuObuQTHkpY64yZoJ8c0kas/BnPavnDcA9wLfT+6dKWrmfpz0CPBIRd6f3rwdOB7ZL6km30wPsyPQKzJqcZ8qyssh6cPfvgDOAYYCIuIfkzJ6aIuJXwMOSKn/V80i6fVaSHCsgvb55IoHNmlWtA7SeQMUaLevB3acjYpek6mVZ+oguBq6WdAjwC+A9JB8210m6EHgIeMsE8po1Nc+UZWWQtfDfK+kvgSmSTgYuAb63vyel3wz26V8iaf2bmVkBsnb1XAy8FPg9yQ+3dgGX5ZTJzMxytN8Wv6QpwMqIOAf4b/lHMjOzPO23xR8RzwBPSprcicZmZlYqWfv4fwdsknQb8ERlYURckksqMzPLTdbC/830YmZmTS5T4Y+I5ekpmS9MF90fEZ4BwsysCWUq/JLmkgyo9iAg4HhJiyLi9tySmTXIWHPZ+lx7a2VZu3r+N/Cayjg9kl4IXAv05RXMrBEqQyVXRs2sDJUMuPhby8p6Hv/U6sHZIuJn7GdYZrNmMN5QyWatKmuLf52kq4CvpvffTjLMsllTqzWXba3lZq0ga4v/fcB9JEM1XEoy2NpFeYUya5Rac9nWWm7WCrIW/oOBz0TEwoh4E/BZYMp+nmNWeh4q2dpR1sK/CqhuAnUA/7f+ccway0MlWzvK2sd/WESMVO5ExIikw3PKZNZQHirZ2k3WFv8Tkk6v3EmnVfTRLzOzJpS1xX8Z8A1JW0kmYJkJXJBXKDMzy8+4LX5Jr5D0RxHxQ+BFwNeBp0nm3n1gfxuX9KCkTZLukbQuXTZD0m2StqTX4060bmZm9bW/rp4vAE+lt88EPgx8HtgJXJlxHwMRcWrVTO9LgFURcTLJQeMlE4tsZmYHYn+Ff0pEPJbevgC4MiJuiIj/AZw0yX2eTzLuD+n1gklux8zMJkERtedMl3QvcGpEPC3pp8DiysBsku6NiFPG3bj0AMm3gwC+EBFXShqOiK6qdXZGxD7dPZIWA4sBuru7+wYHBzO9oJGRETo7OzOt20jOlV0ZM0E5c5UxE5QzVxkzQb65BgYG1lf1tjwnImpeSKZavBO4GdjIcx8UJwF3jvfcdL2Z6fWxwI+As4DhUevs3N92+vr6Iqs1a9ZkXreRnCu7MmaKKGeuMmaKKGeuMmaKyDcXsC7GqKnjntUTER+TtAroAb6TbgiSLqKL9/dpExFb0+sdkm4CzgC2S+qJiG2SeoAd+9uOmZnVT5Y5d++KiJsionrKxZ9FxIbxnidpmqQjKreB1wD3AiuBRelqi0i+TZiZWYNkPY9/MrqBmyRV9nNNRHxb0g+B6yRdCDwEvCXHDGZmNkpuhT8ifgG8fIzljwLz8tqvWZ48W5e1gjxb/GYtZXj3Hq5Y5dm6rPllHavHrO1t3/U7z9ZlLcGF3yyjp555dszlnq3Lmo0Lv1lGh0wZ+7+LZ+uyZuPCb5ZR95GHebYuawku/GYZdXVM9Wxd1hJ8Vo/ZBHi2LmsFbvGbmbUZF34zszbjwm9m1mZc+M3M2owLv5lZm/FZPbZfHpjMrLW48Nu4Vmwc4oobPTCZWStx4bdxfeLW+2sOTFbmwu9vKWa1ufDbuGoNQFbmgcn8LcVsfLkf3JU0RdJGSbek92dIuk3SlvR6et4ZbPJqDUBW5oHJxvuWYmaNOavnUmBz1f0lwKqIOBlYld63krp8/qymG5isGb+lmDVSroVf0nHA64EvVi0+H1ie3l4OLMgzgx2YBaf1Nt3AZM34LcWskfLu4/808EHgiKpl3RGxDSAitkk6NucMdoCabWCyy+fP2quPH8r/LcWskRQR+WxYOg/484j4G0lzgf8aEedJGo6Irqr1dkbEPv38khYDiwG6u7v7BgcHM+13ZGSEzs7OOryC+nKu7OqRaXj3Hrbv+h1PPfMsh0w5iO4jD6OrY2rhueqtjJmgnLnKmAnyzTUwMLA+Ivr3eSAicrkAS4FHgAeBXwFPAl8D7gd60nV6gPv3t62+vr7Ias2aNZnXbSTnyq6MmSLKmauMmSLKmauMmSLyzQWsizFqam59/BFxRUQcFxEnAG8FVkfEO4CVwKJ0tUXAzXllMDOzfRUxVs8y4FxJW4Bz0/tmZtYgDfkBV0SsBdamtx8F5jViv2Zmti+Pzmlm1mZc+M3M2owLv5lZm3HhNzNrMx6d0xrKwyWbFc+F3xrGwyWblYO7eqxhPFyyWTm48FvDeLhks3Jw4beG8XDJZuXgwm8N04yTupi1Ih/ctYapHMD1WT1mxXLht4ZqtkldzFqRu3rMzNqMC7+ZWZtx4TczazMu/GZmbcaF38yszeR2Vo+kw4DbgUPT/VwfER+RNAP4OnACyUTsfxERO/PK0UrGG+CsqMHPPOiaWfPJ83TO3wNnR8SIpKnAHZK+BSwEVkXEMklLgCXAh3LM0RLGG+AMKGTwMw+6ZtaccuvqicRIendqegngfGB5unw5sCCvDK1kvAHOihr8zIOumTUnRUR+G5emAOuBk4DPR8SHJA1HRFfVOjsjYvoYz10MLAbo7u7uGxwczLTPkZEROjs76xG/rg4016ahXZN63uzeI8d9/EByjZdpf/sdT6v+G+ahjJmgnLnKmAnyzTUwMLA+IvpHL8+18P9hJ1IXcBNwMXBHlsJfrb+/P9atW5dpX2vXrmXu3LmTzpqXA801Z9lqhsYYxbI3HeCs1mN3Ljk7t1zjZdrffvPKlKcy5ipjJihnrjJmgnxzSRqz8DfkrJ6IGAbWAq8FtkvqSUP1ADsakaHZjTfAWVGDn3nQNbPmlOdZPccAeyJiWFIHcA7wcWAlsAhYll7fnFeGVpJlgLNGn13jQdfMmlOeZ/X0AMvTfv6DgOsi4hZJ3weuk3Qh8BDwlhwztJTxBjgravAzD7pm1nxyK/wR8WPgtDGWPwrMy2u/duB8br5Za/OwzLYXn5tv1vo8ZIPtxefmm7U+F37biydEN2t9Lvy2F0+Ibtb6XPhbxIqNQ8xZtpoTl3yTOctWs2Lj0KS243PzzVqfD+62gHoekPW5+Watz4W/zoo4FXK8A7KT2bfPzTdrbS78dTRey7srx/36gKyZTYT7+OuoqFMhfUDWzCbChb+Oimp5+4CsmU2EC38dFdXyXnBaL0sXzqa3qwORDIu8dOFs99Ob2Zjcx19Hl8+ftVcfP1S1vHdtyXXfPiBrZlm5xV9HbnmbWTNwi7/O3PI2s7Jz4W8iHi7ZzOrBhb9JeLhkM6uX3Pr4JR0vaY2kzZLuk3RpunyGpNskbUmvx51ovQj1GvemnjxcspnVS54Hd58G/jYiXgy8Cni/pJcAS4BVEXEysCq9XxqVlvXQ8G6C51rWRRd//zrXzOolt8IfEdsiYkN6+7fAZqAXOB9Ynq62HFiQV4bJKGvL2r/ONbN6UUTkvxPpBOB24BTgoYjoqnpsZ0Ts090jaTGwGKC7u7tvcHAw075GRkbo7OycdNZNQ7tqPja798hJb/dAcw3v3sPQzt08W/XvdZBE7/QOujqmFpYrD2XMBOXMVcZMUM5cZcwE+eYaGBhYHxH9o5fnXvgldQLfBT4WETdKGs5S+Kv19/fHunXrMu1v7dq1zJ07d9J55yxbzdAY3Se9XR3cueTsSW/3QHNBPmf11CNXvZUxE5QzVxkzQTlzlTET5JtL0piFP9ezeiRNBW4Aro6IG9PF2yX1RMQ2ST3AjjwzTNS4v74tmH8jYGb1kOdZPQKuAjZHxCerHloJLEpvLwJuzivDZPjXt2bW6vJs8c8B3glsknRPuuzDwDLgOkkXAg8Bb8kxw6S4ZW1mrSy3wh8RdwCq8fC8vPZb4V+5mpmNrSV/uetfuZqZ1daSo3OW9Vx8M7MyaMnC71+5mpnV1pKF379yNTOrrSULv+egNTOrrSUP7lYO4PqsHjOzfbVk4Qefi29mVktLdvWYmVltLvxmZm3Ghd/MrM248JuZtRkXfjOzNtOQGbgOlKRfA7/MuPrRwG9yjDNZzpVdGTNBOXOVMROUM1cZM0G+uV4QEceMXtgUhX8iJK0ba8aZojlXdmXMBOXMVcZMUM5cZcwExeRyV4+ZWZtx4TczazOtWPivLDpADc6VXRkzQTlzlTETlDNXGTNBAblaro/fzMzG14otfjMzG4cLv5lZm2mZwi/pS5J2SLq36CzVJB0vaY2kzZLuk3RpCTIdJukHkn6UZvpo0ZkqJE2RtFHSLUVnqZD0oKRNku6RtK7oPBWSuiRdL+mn6d/XmQXnmZW+R5XL45IuKzJThaQPpH/r90q6VtJhJch0aZrnvka/Ty3Txy/pLGAE+EpEnFJ0ngpJPUBPRGyQdASwHlgQET8pMJOAaRExImkqcAdwaUTcVVSmCkn/BegHnhcR5xWdB5LCD/RHRKl+/CNpOfD/IuKLkg4BDo+I4YJjAckHODAEvDIisv74Mq8svSR/4y+JiN2SrgP+LSK+XGCmU4BB4AzgKeDbwPsiYksj9t8yLf6IuB14rOgco0XEtojYkN7+LbAZKHSigEiMpHenppfCWwCSjgNeD3yx6CxlJ+l5wFnAVQAR8VRZin5qHvDzoot+lYOBDkkHA4cDWwvO82Lgroh4MiKeBr4LvKlRO2+Zwt8MJJ0AnAbcXXCUSpfKPcAO4LaIKDwT8Gngg8CzBecYLYDvSFovaXHRYVL/Afg18C9p19gXJU0rOlSVtwLXFh0CICKGgP8FPARsA3ZFxHeKTcW9wFmSjpJ0OPDnwPGN2rkLf4NI6gRuAC6LiMeLzhMRz0TEqcBxwBnpV8/CSDoP2BER64vMUcOciDgdeB3w/rRbsWgHA6cD/xQRpwFPAEuKjZRIu53eCHyj6CwAkqYD5wMnAjOBaZLeUWSmiNgMfBy4jaSb50fA043avwt/A6T96DcAV0fEjUXnqZZ2D6wFXltsEuYAb0z70weBsyV9rdhIiYjYml7vAG4i6Zct2iPAI1Xf1K4n+SAog9cBGyJie9FBUucAD0TEryNiD3Aj8OqCMxERV0XE6RFxFkk3dUP698GFP3fpgdSrgM0R8cmi8wBIOkZSV3q7g+Q/xk+LzBQRV0TEcRFxAkk3weqIKLRVBiBpWnpQnrQr5TUkX9MLFRG/Ah6WNCtdNA8o7ISBUd5GSbp5Ug8Br5J0ePr/cR7JsbZCSTo2vX4+sJAGvmctM9m6pGuBucDRkh4BPhIRVxWbCkhasu8ENqV96gAfjoh/Ky4SPcDy9MyLg4DrIqI0p0+WTDdwU1IvOBi4JiK+XWykP7gYuDrtWvkF8J6C85D2V58L/HXRWSoi4m5J1wMbSLpTNlKO4RtukHQUsAd4f0TsbNSOW+Z0TjMzy8ZdPWZmbcaF38yszbjwm5m1GRd+M7M248JvZtZmXPjNUpJGRt1/t6TPFZXHLC8u/GY5S38vYVYaLvxmGUh6gaRVkn6cXj8/Xf5lSf+xar2R9HpuOg/DNSQ/3psm6ZvpHAj3SrqgoJdi1jq/3DWrg46qX1cDzABWprc/RzLXw3JJfwV8Fliwn+2dAZwSEQ9IejOwNSJeDyDpyLomN5sAt/jNnrM7Ik6tXID/WfXYmcA16e2vAn+SYXs/iIgH0tubgHMkfVzSn0bErrqlNpsgF36zyamMdfI06f+jdACwQ6rWeeIPK0f8DOgj+QBYKqn6Q8WsoVz4zbL5HsmooQBvJ5nKD+BBkoIOyZjvU8d6sqSZwJMR8TWSSUHKMoSytSH38ZtlcwnwJUmXk8x8VRkJ85+BmyX9AFhFVSt/lNnAJyQ9SzIa4/tyzmtWk0fnNDNrM+7qMTNrMy78ZmZtxoXfzKzNuPCbmbUZF34zszbjwm9m1mZc+M3M2sz/B8PKQHuDFVZCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter( X , Y , color = \"#1f77b4\" )\n",
    "plt.xlabel(\"Hours\")\n",
    "plt.ylabel(\"Scores\")\n",
    "plt.title(\"Hours Vs Scores%\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f754c3b3",
   "metadata": {},
   "source": [
    "The Graph shows Linear Relation between Hours and Scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ac35f35",
   "metadata": {},
   "source": [
    "Splitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "04309b6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e9b6218a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e2ee0e4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3382633f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = LinearRegression()\n",
    "LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d4c6fc78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c5ad825b",
   "metadata": {},
   "outputs": [],
   "source": [
    "line = LR.coef_ * X + LR.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f746e35",
   "metadata": {},
   "source": [
    "Visualising Train Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f9317676",
   "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.scatter(x_train , y_train , color = \"green\")\n",
    "plt.plot(X , line , color = \"red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43d2d38f",
   "metadata": {},
   "source": [
    "Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c82be449",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[28.29854338],\n",
       "       [68.78094963],\n",
       "       [55.28681421],\n",
       "       [61.0700151 ],\n",
       "       [59.14228147]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = LR.predict(x_test)\n",
    "Y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "870905fb",
   "metadata": {},
   "source": [
    "Visualising Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "70d870a0",
   "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.scatter(x_test,y_test , color = \"blue\")\n",
    "plt.plot(x_test,Y_pred , color = \"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a1114e8c",
   "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>Actual Score</th>\n",
       "      <th>Predicted Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.7</td>\n",
       "      <td>30</td>\n",
       "      <td>28.298543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.9</td>\n",
       "      <td>76</td>\n",
       "      <td>68.780950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "      <td>55.286814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.1</td>\n",
       "      <td>67</td>\n",
       "      <td>61.070015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "      <td>59.142281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hours  Actual Score  Predicted Score\n",
       "0    2.7            30        28.298543\n",
       "1    6.9            76        68.780950\n",
       "2    5.5            60        55.286814\n",
       "3    6.1            67        61.070015\n",
       "4    5.9            62        59.142281"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predict = pd.DataFrame({\"Hours\": x_test.reshape(1,-1)[0] ,\"Actual Score\" :y_test.reshape(1,-1)[0], \n",
    "                           \"Predicted Score\" : Y_pred.reshape(1,-1)[0]})\n",
    "df_predict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab4f089f",
   "metadata": {},
   "source": [
    "Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "57bd35d5",
   "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>Actual Score</th>\n",
       "      <th>Predicted Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.7</td>\n",
       "      <td>30</td>\n",
       "      <td>28.298543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "      <td>55.286814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "      <td>59.142281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.1</td>\n",
       "      <td>67</td>\n",
       "      <td>61.070015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.9</td>\n",
       "      <td>76</td>\n",
       "      <td>68.780950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hours  Actual Score  Predicted Score\n",
       "0    2.7            30        28.298543\n",
       "2    5.5            60        55.286814\n",
       "4    5.9            62        59.142281\n",
       "3    6.1            67        61.070015\n",
       "1    6.9            76        68.780950"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sorted = df_predict.sort_values(by = \"Hours\")\n",
    "df_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ec394cbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a438d7df",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Pravallika\\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 `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\Pravallika\\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 `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "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": [
    "title = \"Actual Values Vs Predicted Values\"\n",
    "ax1 = sns.distplot(df_sorted[\"Actual Score\"], hist = False , color = \"red\" , label = \"Actual Score\")\n",
    "sns.distplot(df_sorted[\"Predicted Score\"] , hist = False , color = \"blue\" , label = \"Predicted Score\" , ax = ax1)\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.title(title)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "db6f12ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.4842792417677035\n",
      "Mean Squared Error: 24.11100785917006\n",
      "Root Mean Squared Error: 4.910296107076442\n",
      "Accuracy: 0.8998712298207223\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('Accuracy:', metrics.r2_score(y_test, Y_pred))"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}