Assignment 04.-Simple Linear Regresion .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "04b6d48e",
   "metadata": {},
   "source": [
    "# Question-01- Delivery_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c10860f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f266364",
   "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>Delivery Time</th>\n",
       "      <th>Sorting Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.50</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.75</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.35</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19.00</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17.90</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18.75</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19.83</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10.75</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16.68</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12.03</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14.88</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>13.75</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.11</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>17.83</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21.50</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Delivery Time  Sorting Time\n",
       "0           21.00            10\n",
       "1           13.50             4\n",
       "2           19.75             6\n",
       "3           24.00             9\n",
       "4           29.00            10\n",
       "5           15.35             6\n",
       "6           19.00             7\n",
       "7            9.50             3\n",
       "8           17.90            10\n",
       "9           18.75             9\n",
       "10          19.83             8\n",
       "11          10.75             4\n",
       "12          16.68             7\n",
       "13          11.50             3\n",
       "14          12.03             3\n",
       "15          14.88             4\n",
       "16          13.75             6\n",
       "17          18.11             7\n",
       "18           8.00             2\n",
       "19          17.83             7\n",
       "20          21.50             5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import dataset\n",
    "dataset=pd.read_csv('Dataset/delivery_time.csv')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd4ba93b",
   "metadata": {},
   "source": [
    "## EDA and Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "12a12ebe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21 entries, 0 to 20\n",
      "Data columns (total 2 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Delivery Time  21 non-null     float64\n",
      " 1   Sorting Time   21 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 464.0 bytes\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a4d14b5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\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='Delivery Time', ylabel='Density'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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dZk3JAa+jSATp7n78+c65OuASoBQYC/xnyFJJ1Ghp6+CxVaWkJcVx2VQddzhZ547PJt4Xo95M0qu6WyCONOS7GPibc64mRHkkyrywaR8HGlr49PThJMbFeh2n3xqQ6B9mWrJhHx0dumhOekd3C8SzZrYVKAReMbMsQLezkh4pqqhn2c4azhgziNGD1dKrpy6ZlEN5XTOrizXMJL2ju+2+bwXmAIXOuVagAVgYymAS2Q63tvPE6lIGpyZw/mlDvY4TEc4dP0TDTNKrTuRcwvHA58zsGuBy4PzQRJJosGTDPuqaWvnM9OHExeqU1t6QmuBjvoaZpBd19yymh4BfAmcCMwIPdXGVk7Kzsp6Vew4wr2AwIzLV0qs3LZiUQ8WhZlbu0TCT9Fx3b+pbCExwahkpPdTa3sFTa8rITInnnFN185/edu74IST4YliyYR8zR6mPlfRMdwvERmAooMFN6ZHXtlVQ3dDC9XPzj3u19MPLi/soVeRITfDxsXHZLNmwj+9fMoFYXTQnPdDdwd/BwGYzW2pmi488QhlMIs/+usO8ub2SqSPSKchWp5ZQufjIMNNunY0uPdPdPYjbQhlCIp9zjmfWlJEYF8vFE3O8jhPRzj01+1/DTLNGD/I6jvRj3T3N9Q1gNxAX+H0FsDqEuSTCrC05yJ6aRi48bSgpCd39f4mcjJQEH+ecms2Sjftp19lM0gPdPYvpJuBx4I+BSbnA0yHKJBHmcGs7z2/cz/CMJKap11KfuHhiDpWHmlmhYSbpge4eg/gqMBeoA3DOvQ9khyqURJZXt1bQ0NzGJyYPI0a9lvrEOadmkxgXwz900Zz0QHcLRLNzruXIEzPzAdp3leMqrzvMOzuqKMzPYHiGrnnoK0eGmZ7XMJP0QHcLxBtm9h0gycw+DvwdeDZ0sSQSOOf4x/p9JPhiOX+C2mn0tQUTh1FV38x7uzTMJCenuwXiVqAS2AB8CVgCfC9UoSQybCs/RFFlPeeOz9aBaQ987NQs/zDTBt1pTk5Od89i6sB/UPorzrnLnXP3dOeqajO70My2mVmRmd0aZL6Z2e2B+evNbFqX+bFmtsbMnuvm3yNhor3D8fzG/QxKidcVvR5Jjvdx7qlDeEHDTHKSjlkgAl/gt5lZFbAV2GZmlWb2g+O9sJnFAncAFwETgCvNbEKXxS4CCgKPm4E7u8z/OrClW3+JhJUVu2uoPNTMRacPxRejZnxeWTAph6r6FpbvqvY6ivRDx/vkfgP/2UsznHODnHOZwCxgrpl98zjrzgSKnHM7Awe4H+GjLcIXAg86v2VAupnlAJjZcGABcO8J/UXiucOt7byypZz8QSmMzxnodZyo9rFx2STFxepsJjkpxysQ1wBXOud2HZngnNsJXB2Ydyy5QEmn56WBad1d5jfAt4GOY72Jmd1sZivNbGVlZeVxIklfeGN7JQ0t7Vw8cahuIeqxpPhYzhmfzQsb99PWfsyPkshHHK9AxDnnqrpOdM5V8sFtSI8m2DdD14HQoMuY2SVAhXNu1XHeA+fc3c65QudcYVZW1vEWlxCrbWrln0VVTBmRrtNaw8QlE3OobmjR2Uxywo5XIFpOch749wZGdHo+HOh6OsXRlpkLfMLMduMfmjrHzP5ynPeTMPDa1gqcg/PGq5V3uJgfGGZ6boOGmeTEHK9ATDazuiCPQ8DE46y7Aigws1FmFg9cAXTtALsYuCZwMHw2UOuc2+ec+z/OueHOufzAeq86564+8T9P+lJ1fTMr99QwY1QGmSnxXseRgKT4WM7VMJOchGMWCOdcrHNuYJDHAOfcMYeYnHNtwC3AUvxnIj3mnNtkZovMbFFgsSXATqAIuAf4So//IvHMy1vKiY0xPjZOXVjCzSWTcqhpaGG5hpnkBIT06iXn3BL8RaDztLs6/e7w93k61mu8DrwegnjSi/bVNrG+tJazxmYxIPF4h6ekr80fl01yfCzPrd/H3FMGex1H+gmdoC694qXN5STExXBWgU4UCEeJcbGcO34IL2zcp2Em6TYVCOmxPdUNbN1/iLMKskiKj/U6jhzFJZNyONDYyttFHzkxUSQoFQjpEeccL24uJzXBxxljNHQRzuaPy2Jgoo9n1qo3k3SPCoT0SFFFPbuqGvjYuCziffrnFM4SfP7bvS7dtJ/Gljav40g/oE+0nDTnHK9srSA9KY4Z+WrI1x8snJJLY0s7L20u9zqK9AMqEHLSdlQ2UFzTyNnjsvDF6p9SfzBrVCY5aYk8vabM6yjSD+hTLSfFv/dQzsBEH9NH6j7T/UVMjPGJKcN48/0qquubvY4jYU4FQk7KrqoG9lQ3cvZY7T30N5+ckkt7h+Mfar0hx6HbfMlJeXVrBQMSfRTq2EOfeXh5ca+91pCBCdz71q6TulfHVbNG9loOCW/6r5+csF1VDeysauCsgizitPfQL00Znk5xTaOGmeSY9OmWE/ba1gpSE3w6c6kfmzwiHQNWFx/0OoqEMRUIOSF7qhsoqqxnXsFgXffQj6Unx3NKdiqriw/Qcfzby0uU0idcTshr2ypIjo9l1qhBXkeRHpqel0FtUys7Kuu9jiJhSgVCuq2kppHt5fXMK9BV05FgfM5AEuNiWLXngNdRJEzpUy7d9urWCpLiYpk9SsceIkFcbAyTh6ezeW8dTS3tXseRMKQCId1SdqCJbeWHOLNgMAlx6tgaKQrzMmnrcKwvO+h1FAlDKhDSLa9uqyAxLoY5o3XsIZIMS09k6MBEDTNJUCoQclx7DzaxZV8dc08ZTKL2HiKKmTEtL4PSA03srzvsdRwJMyoQclyvbasgwRfDGaN1v4dINHVEOrExxnu6X7V0oQIhx7S/9jCb9tZxxpjBultchEpJ8DExN401xQdobtPBavmACoQc02vbKoj3xTD3FB17iGSzRmXS3NbBupJar6NIGFGBkKMqrzvMxrJa5oweRHK8+jpGspGZyeSkJbJ8VzVOV1ZLgAqEHNVr2yqIi43hzFN07CHSmRkzR2Wyr/YwJTWNXseRMKECIUFVHDrMhtJaZo/OJCVBew/RYMqIdBJ8MSzTwWoJUIGQoN7YVokv1jizIMvrKNJHEnyxTB2ZzoayWuqb27yOI2FABUI+oqq+mbUlB5k1ahCp2nuIKrNGDaK9w+mUVwFUICSI17dVEBtjzCvQsYdoM2RgImOHpPLuzmpa2zu8jiMeU4GQD9ld1RDYe8hkQGKc13HEA/MKsmhobmOtbiYU9VQg5EN+/1oRMWbMG6tjD9Fq9OAUhqUn8lZRlW4mFOVUIORf9lQ38NSaMmaNymSg9h6ilpkxryCLqvpmtu475HUc8ZAKhPzL718twhejvQeB04elkZ4cx1tFlV5HEQ+pQAjg33t4ck0ZV80aqb0HITbGmDtmMHuqGymubvA6jnhEBUIAuOO1ImJjjEVnj/E6ioSJwvwMkuNjeWVrhddRxCMqEEJxdSNPrC7jqpkjGTIw0es4EiYSfLGcVZDF+xX17K7SXkQ0UoGQf+09fHm+9h7kw2aP9l8s+dKWcq+jiAdUIKJcSU0jT6wu1d6DBBXvi+HssVnsqmpgR2W913Gkj6lARLk7XisiRsce5BhmjspkYKKPlzaXqxV4lAlpgTCzC81sm5kVmdmtQeabmd0emL/ezKYFpo8ws9fMbIuZbTKzr4cyZ7QqqWnk8VX+vYehadp7kODiYmOYPy6b4ppGtpfruohoErICYWaxwB3ARcAE4Eozm9BlsYuAgsDjZuDOwPQ24N+dc+OB2cBXg6wrPfS/L2/XmUvSLYX5GWSmxPOPDftpaVOPpmgRyj2ImUCRc26nc64FeARY2GWZhcCDzm8ZkG5mOc65fc651QDOuUPAFiA3hFmjzrb9h3hqTRnXzc3X3oMcly8mhksm5lBV38yD7+72Oo70kVAWiFygpNPzUj76JX/cZcwsH5gKLA/2JmZ2s5mtNLOVlZW66rO7frF0G6kJPr6svQfpplNzBjJuyAB+8/L7VBw67HUc6QOhLBAWZFrXI1zHXMbMUoEngG845+qCvYlz7m7nXKFzrjArSy0iumPVnhpe3lLOorPHkJ4c73Uc6UcWTMyhua2dn7+wzeso0gdCWSBKgRGdng8H9nZ3GTOLw18c/uqcezKEOaOKc47/eX4bg1MTuH5uvtdxpJ8ZPCCBG88czeOrSlldfMDrOBJioSwQK4ACMxtlZvHAFcDiLsssBq4JnM00G6h1zu0zMwPuA7Y4534dwoxR5/Xtlby3u4avn3sKyfG6W5ycuFvOOYWctES+/fh6Dre2ex1HQihkBcI51wbcAizFf5D5MefcJjNbZGaLAostAXYCRcA9wFcC0+cCXwDOMbO1gcfFocoaLTo6HD9/YRsjM5P53IyRXseRfio1wcf/fHoSRRX1/HKphpoiWUj/C+mcW4K/CHSedlen3x3w1SDrvU3w4xPSA8+u38uWfXX89oopxPt0jaScvLPGZnH17JHc989dnDdhCLNHD/I6koSAviWiRHNbO796cTunDh3ApZOGeR1HIsB3Lh7PyMxk/uPv66hvbvM6joSACkSUuP+fuymuaeS7C8YTE6OdM+m55Hgfv/rMZMoONvHdpzaoDUcEUoGIApWHmvndq0Wce2o28wp0KrD0nsL8TL513lieWbuXe9/a5XUc6WUqEFHg1y9t53BrO99ZMN7rKBKBbjnnFC6eOJSfPr+FN7brYtVIogIR4TbvrePRFcVcMyefMVmpXseRCGRm/OLyyYwdMoCvPbyaXbq5UMRQgYhgzjl+/NxmBibF8fVzC7yOIxEsJcHHPdcUEhtjfOG+5ew92OR1JOkFKhARbPG6vby7s5p///hY0pLjvI4jEW5EZjIP3DCT2sZWrrxnGftr1a+pv1OBiFC1Ta38+LktTBqexlWz8ryOI1Fi0vB0HrhxJtX1LVx1zzI19evnVCAi1K9e3EZNQzP/75MTidVprdKHpo3M4M/Xz2B/3WEuv/Nd3tdNhvotFYgItK7kIA8t28M1c/KZODzN6zgShWbkZ/LXL86isaWdT/3hHV7fVuF1JDkJKhARpq29g+8+vYHBqQl86/yxXseRKDZ1ZAbP3DKX4ZnJ3HD/Cu5+cwcdHbqYrj9RgYgwf3xzJxvL6rjt0tMYmKgD0+Kt3PQkHl80h/MnDOW/l2zlynuWUVLT6HUs6SYViAiydX8dv3l5Owsm5bBgUo7XcUQA/ymwd149jZ9fPolNe+u44Ddv8sA7u2lr172tw50KRIRobe/g3x9bR1pSHD9eeLrXcUQ+xMz4bOEIXvjGPKaNzOCHizdxwW/e5JUt5erhFMZ0x5gI8YfXdrBpbx13XT2dzBTdRlRC5+HlxT1a/6LTh5I/KIUXNu3jxgdWkjcombMLshg7dAAx1v0z7q6apXuahJoKRARYXXyA3736PgunDOPC04d6HUfkmMyMCcMGMm7oAFbsruGN7ZU8uGwP2QMSmHvKYCblppEQF+t1TEEFot872NjC1x5eQ056Iv9XQ0vSj8TGGLNHD2JGfiYbyg7y1vtVPLWmjH+s38fE3DSm52WQNygZO4G9CuldKhD9mHOO//j7eioOHebxRWeQlqSzlqT/iY0xpozIYPLwdEpqGlm55wDry2pZVXyAQSnxTMvLYMrwdDI0dNrnVCD6sfve3sXLW8r5wSUTmDwi3es4Ij1iZowclMLIQSlcMmkYG8tqWbnnAC9tLuelzeXkD0pmyogMJuamkRSvIai+oALRTy3bWc3Pnt/K+ROGcP3cfK/jiPSqeF8M0/IymJaXwYGGFtaVHmRN8UGeXlvGs+v3Mm7IADJT4vjYqdkk+FQsQkUFoh/aVdXAor+sIm9QMr/4zGSN0UpEy0iJZ/64bM4em8Xe2sOsLT7AutJaFv1lNQMTfSyYNIzLpuZSmJeh2+n2MhWIfqa2sZUb71+BAX+6boaOO0jUMDNy05PITU/iwtNzyBuUzNNrynhmbRl/e6+Y3PQkPjl1GJ+eNpzRujlWr1CB6Eda2jr4ysOrKDnQyF+/OJu8QSleRxLxRGyMcdbYLM4am8VPWtp4aXM5T64u487Xd3DHazuYVzCYa+bkc86p2epm3AMqEP1Ea3sHX/vbav5ZVM0vPzOZmaMyvY4kEhaS430snJLLwim5VBw6zGMrSvjLsmJuenAluelJfGFOHlfOGKmbZp0EtdroB9o7HN96bB1LN/nPWLp8+nCvI4mEpewBidxyTgFv/dfHuPPz0xiRmcTPnt/KGT97hZ88t1m3Qj1B2oMIc+0djm8/vp5n1+3l1otO5YYzR3kdSSTsxcXGcNHEHC6amMPmvXXc/eYO/vzObu5/ZzefmDKML501hnFDB3gdM+xpDyKMHW5t56t/Xc0Tq0v5xnkFLDp7jNeRRPqdCcMG8psrpvLGf87n6tl5PL9hPxf85k1uuH8FK3bXeB0vrKlAhKmDjS1cfe9ylm7ez/cWjOcb5+nmPyI9MTwjmds+cRrv3HoO3/r4WNaWHOQzd73L5Xe+wytbynUzoyA0xBSGdlbWc9ODKympaeL3V07TvR1EelFGSjz/dm4BN80bzWMrS7j7zZ3c+MBKxg0ZwKL5o7lk0jDiYvV/Z9AeRNh5Zm0Zl/7ubaobWnjoxpkqDiIhkhQfy7Vn5PP6f87nfz83GYfjm4+uY/4vXueBd3bT1NLudUTPaQ8iTDS2tPHj5zbzt/dKKMzL4HdXTSUnLcnrWCIRLy42hsumDmfh5Fxe3VrBH14v4oeLN3H7K+9z3Rn5fGFOHunJ0dkoUAUiDLy8uZwfLt5E2cEmvjJ/DN/6+Fh82sUV6VMxMcZ5E4Zw7vhsVuw+wJ2vF/Grl7bz+9eK+OSUXL4wJ4/Tc9O8jtmnVCA8tLuqgf9esoUXN5czbsgAHl80h8J8XQAn4iUzY+aoTGaOmsmWfXU8+O5unlpTxqMr/Xv3X5iTxwWnDSUxCm5qpALhgeLqRn736vs8uaaMuFjjvy48lS/OG6UDYyJhZnzOQH76qUnceuF4/r6qhIeW7eHrj6xlQKKPBRNzuGxqLjPyMyO2SaAKRB9xzvHuzmr+uryYpRv3ExNjXDsnn0XzR5M9INHreCJyDGnJcXxx3mhumDuKd3ZU8+SaUhav28sjK0rITU/i0snDOG98NlNHZkRU7ycViBByzrG9vJ6lm/bz9NoydlY2kJYUx3Vn5HPTWaMZMlCFQaQ/iYkxziwYzJkFg/nJJ9tYumk/T64u4963dnLXGzvISI7jY+OyOWd8NrNHD2JwaoLXkXtEBaKXVdc3896uGpbv8t+MfVdVAwAz8jP46vxTWDApJyrGLkUiXXK8j8umDueyqcOpbWrlze2VvLq1gle3VfDkmjIARg1OYXpeBoV5GUwdmcHorJR+NZQc0gJhZhcCvwVigXudcz/rMt8C8y8GGoHrnHOru7Ou12obWymuaaTkQCNFFfVs3lvHlv117KluBCApLpYZozK58cxRnD9hCNnaWxCJWGlJcVw6eRiXTh5GW3sH60oPsmL3AVbuPsArW8p5fFUpAHGxxujBqYwdOoCx2ankDU4hNz2J4RlJZKUmhN2xjJAVCDOLBe4APg6UAivMbLFzbnOnxS4CCgKPWcCdwKxurttrdlTW09jcTmNLG42t7TS1tNPQ3EZTazu1ja3UNLZwoKGF6oYWqupbKD3QyKHDbR96jfxByZw2bCCfmzGCWaMGMTE3jXhf//mfgoj0Dl9sDNPzMpmelwln+4ead1Y1sL70INvL69m+/xBrig/w7Lq9H1ovLtYYmpZIZnI86cnxZCTHBX7Gk54cR1J8LElxsSTG+X8mxceQ4IslKT6W5PjYkFw3Fco9iJlAkXNuJ4CZPQIsBDp/yS8EHnTOOWCZmaWbWQ6Q3411e83Fv32L5raOo85PTfCRmRJPRko8OWmJzMjPYERGMiMykxiRmUzeoBRSEzRaJyIfZWaMyUplTJe73DU0t1F6oIm9B5soPdhE2YEm9tc2UdPYyoHGFnZW1XOwoZVDzW1HeeUPDEqJZ9X3P97r2UP5rZYLlHR6Xop/L+F4y+R2c10AzOxm4ObA03oz29aDzACDgaoevkaohXvGcM8H4Z8x3POBxxk/f/xFomYb7gHsBye9et7RZoSyQAQbTOvaLvFoy3RnXf9E5+4G7j6xaEdnZiudc4W99XqhEO4Zwz0fhH/GcM8H4Z8x3PNB+GcMZYEoBUZ0ej4c2NvNZeK7sa6IiIRQKI+irgAKzGyUmcUDVwCLuyyzGLjG/GYDtc65fd1cV0REQihkexDOuTYzuwVYiv9U1T855zaZ2aLA/LuAJfhPcS3Cf5rr9cdaN1RZu+i14aoQCveM4Z4Pwj9juOeD8M8Y7vkgzDOa/wQiERGRD9OJ+iIiEpQKhIiIBKUC0YmZXWhm28ysyMxu9TpPV2a228w2mNlaM1vpdR4AM/uTmVWY2cZO0zLN7CUzez/wMyPM8t1mZmWB7bjWzC72Kl8gzwgze83MtpjZJjP7emB6WGzHY+QLm+1oZolm9p6ZrQtk/FFgerhsw6PlC5ttGIyOQQQE2ntsp1N7D+DKULX3OBlmthsodM6FzcU/ZnYWUI//ivjTA9N+DtQ4534WKLQZzrn/CqN8twH1zrlfepGpq0D3gBzn3GozGwCsAj4JXEcYbMdj5PssYbIdA33dUpxz9WYWB7wNfB34FOGxDY+W70LCZBsGoz2ID/yrNYhzrgU40t5DjsE59yZQ02XyQuCBwO8P4P8y8cRR8oUV59y+I00qnXOHgC34uwmExXY8Rr6w4fzqA0/jAg9H+GzDo+ULayoQHzha249w4oAXzWxVoMVIuBoSuJ6FwM9sj/MEc4uZrQ8MQXk2BNaVmeUDU4HlhOF27JIPwmg7mlmsma0FKoCXnHNhtQ2Pkg/CaBt2pQLxgW639/DQXOfcNPxdcL8aGD6RE3cnMAaYAuwDfuVpmgAzSwWeAL7hnKvzOk9XQfKF1XZ0zrU756bg77ww08xO9zJPV0fJF1bbsCsViA90pzWIp5xzewM/K4Cn8A+LhaPywLj1kfHrCo/zfIhzrjzwYe0A7iEMtmNgXPoJ4K/OuScDk8NmOwbLF47bEcA5dxB4Hf/4fthswyM65wvXbXiECsQHwrq9h5mlBA4QYmYpwPnAxmOv5ZnFwLWB368FnvEwy0cc+cIIuAyPt2PgAOZ9wBbn3K87zQqL7Xi0fOG0Hc0sy8zSA78nAecBWwmfbRg0Xzhtw2B0FlMngVPMfsMH7T3+n7eJPmBmo/HvNYC/RcrD4ZDPzP4GzMfftrgc+CHwNPAYMBIoBj7jnPPkQPFR8s3Hv0vvgN3Al46MU3vBzM4E3gI2AEduTPId/OP8nm/HY+S7kjDZjmY2Cf9B6Fj8//F9zDn3f81sEOGxDY+W7yHCZBsGowIhIiJBaYhJRESCUoEQEZGgVCBERCQoFQgREQlKBUJERIJSgZCIZmbtgS6ZmwKdNL9lZsf8d29m+Rbo/mpmhWZ2ex9lvaBTV89683cWXmtmD5rZIjO7pi9yiByh01wloplZvXMuNfB7NvAw8E/n3A+PsU4+8NyR7q+9lMPnnGs7geVfB/7DORcWbd0lOmkPQqJGoEXJzfibo1mgedovzGxFoFnal7quY2bzzew5M4sx//040jvNKzKzIYGrZJ8IvM4KM5sbmH+bmd1tZi8CD5rZW2Y2pdP6/wxcQHVcgdf6j8Dvr5vZ/5rZm+a/R8MMM3vS/Pc8+Emnda42/z0I1prZH83f0l6k21QgJKo453bi/3efDdwI1DrnZgAzgJvMbNRR1uvA36bhMgAzmwXsds6VA78F/jfwOp8G7u206nRgoXPuqsD06wLrjwUSnHPrT/JPaXHOnQXcFcj1VeB04DozG2Rm44HP4W/wOAVoBz5/ku8lUcrndQARDxzp3Hs+MMnMLg88TwMK8N84KphHgR8Af8bfq+vRwPTzgAn+lkUADDzSNwtY7JxrCvz+d+D7ZvafwA3A/T34G470CdsAbDrSnsHMduJvOnkm/uK0IpAriTBoVCf9iwqERJVAT6t2/F+WBnzNObe0yzL5R1n9XeAUM8vCf+OZI8M5McCcToXgyOsANBx57pxrNLOX8N/E5rNAYQ/+lObAz45Ovx957sP/tz3gnPs/PXgPiXIaYpKoEfhivwv4vfOfnbEU+HKglTVmNjbQKTeowDpPAb/G39m0OjDrReCWTu8z5Rgx7gVuB1aEuGncK8DlgQPzR+7NnBfC95MIpD0IiXRJ5r+LVxzQBjyE/wse/F/W+cDqQEvrSo5/S8pH8beGv67TtH8D7jCz9fg/U28Ci4Kt7JxbZWZ1+IepQsY5t9nMvof/DoQxQCv+4xR7Qvm+Ell0mqtIHzKzYfhvFnNq4MC3SNjSEJNIHwlc6LYc+K6Kg/QH2oMQEZGgtAchIiJBqUCIiEhQKhAiIhKUCoSIiASlAiEiIkH9f56T/VJN5ZRFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dataset['Delivery Time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ed63bab9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\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='Sorting Time', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "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": [
    "sns.distplot(dataset['Sorting Time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b24b947d",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e919b751",
   "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>delivery_time</th>\n",
       "      <th>sorting_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.50</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.75</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.35</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19.00</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17.90</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18.75</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19.83</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10.75</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16.68</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12.03</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14.88</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>13.75</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.11</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>17.83</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21.50</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    delivery_time  sorting_time\n",
       "0           21.00            10\n",
       "1           13.50             4\n",
       "2           19.75             6\n",
       "3           24.00             9\n",
       "4           29.00            10\n",
       "5           15.35             6\n",
       "6           19.00             7\n",
       "7            9.50             3\n",
       "8           17.90            10\n",
       "9           18.75             9\n",
       "10          19.83             8\n",
       "11          10.75             4\n",
       "12          16.68             7\n",
       "13          11.50             3\n",
       "14          12.03             3\n",
       "15          14.88             4\n",
       "16          13.75             6\n",
       "17          18.11             7\n",
       "18           8.00             2\n",
       "19          17.83             7\n",
       "20          21.50             5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renaming Columns\n",
    "dataset=dataset.rename({'Delivery Time':'delivery_time', 'Sorting Time':'sorting_time'},axis=1)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b592f0c5",
   "metadata": {},
   "source": [
    "## Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "501cd8c7",
   "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>delivery_time</th>\n",
       "      <th>sorting_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>delivery_time</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.825997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sorting_time</th>\n",
       "      <td>0.825997</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               delivery_time  sorting_time\n",
       "delivery_time       1.000000      0.825997\n",
       "sorting_time        0.825997      1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7ce5492b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sorting_time', ylabel='delivery_time'>"
      ]
     },
     "execution_count": 10,
     "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": [
    "sns.regplot(x=dataset['sorting_time'],y=dataset['delivery_time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdbe270a",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fa4a001d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=smf.ols(\"delivery_time~sorting_time\",data=dataset).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b091a712",
   "metadata": {},
   "source": [
    "## Model Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b85a285a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept       6.582734\n",
       "sorting_time    1.649020\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding Coefficient parameters\n",
    "model.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "930497f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Intercept       3.823349\n",
       " sorting_time    6.387447\n",
       " dtype: float64,\n",
       " Intercept       0.001147\n",
       " sorting_time    0.000004\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding tvalues and pvalues\n",
    "model.tvalues , model.pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f49999a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6822714748417231, 0.6655489208860244)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding Rsquared Values\n",
    "model.rsquared , model.rsquared_adj"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9f18513",
   "metadata": {},
   "source": [
    "## Model Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ca201cda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.827834"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Manual prediction for say sorting time 5\n",
    "delivery_time = (6.582734) + (1.649020)*(5)\n",
    "delivery_time\n",
    "14.827834"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0078fc77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5\n",
       "1    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Automatic Prediction for say sorting time 5, 8\n",
    "new_data=pd.Series([5,8])\n",
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ff1efb6b",
   "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>sorting_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sorting_time\n",
       "0             5\n",
       "1             8"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pred=pd.DataFrame(new_data,columns=['sorting_time'])\n",
    "data_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a208bf29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    14.827833\n",
       "1    19.774893\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(data_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d390a2c3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "646fe390",
   "metadata": {},
   "source": [
    "# Question-02-Salary_hike"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0a64f7b0",
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.9</td>\n",
       "      <td>63218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.1</td>\n",
       "      <td>57081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.5</td>\n",
       "      <td>61111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.9</td>\n",
       "      <td>67938.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>66029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.3</td>\n",
       "      <td>83088.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>93940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.8</td>\n",
       "      <td>91738.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.1</td>\n",
       "      <td>98273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.9</td>\n",
       "      <td>101302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.2</td>\n",
       "      <td>113812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.7</td>\n",
       "      <td>109431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>105582.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "      <td>116969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.6</td>\n",
       "      <td>112635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.3</td>\n",
       "      <td>122391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>121872.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience    Salary\n",
       "0               1.1   39343.0\n",
       "1               1.3   46205.0\n",
       "2               1.5   37731.0\n",
       "3               2.0   43525.0\n",
       "4               2.2   39891.0\n",
       "5               2.9   56642.0\n",
       "6               3.0   60150.0\n",
       "7               3.2   54445.0\n",
       "8               3.2   64445.0\n",
       "9               3.7   57189.0\n",
       "10              3.9   63218.0\n",
       "11              4.0   55794.0\n",
       "12              4.0   56957.0\n",
       "13              4.1   57081.0\n",
       "14              4.5   61111.0\n",
       "15              4.9   67938.0\n",
       "16              5.1   66029.0\n",
       "17              5.3   83088.0\n",
       "18              5.9   81363.0\n",
       "19              6.0   93940.0\n",
       "20              6.8   91738.0\n",
       "21              7.1   98273.0\n",
       "22              7.9  101302.0\n",
       "23              8.2  113812.0\n",
       "24              8.7  109431.0\n",
       "25              9.0  105582.0\n",
       "26              9.5  116969.0\n",
       "27              9.6  112635.0\n",
       "28             10.3  122391.0\n",
       "29             10.5  121872.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import dataset\n",
    "dataset=pd.read_csv('Dataset/Salary_Data.csv')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28e5d296",
   "metadata": {},
   "source": [
    "## EDA and Data Visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2df5b3ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30 entries, 0 to 29\n",
      "Data columns (total 2 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   YearsExperience  30 non-null     float64\n",
      " 1   Salary           30 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 608.0 bytes\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "993358d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\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='YearsExperience', ylabel='Density'>"
      ]
     },
     "execution_count": 23,
     "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": [
    "sns.distplot(dataset['YearsExperience'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9dcad135",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\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='Salary', ylabel='Density'>"
      ]
     },
     "execution_count": 24,
     "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": [
    "sns.distplot(dataset['Salary'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58903f6f",
   "metadata": {},
   "source": [
    "## Correlation Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "76665016",
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YearsExperience</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>0.978242</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 YearsExperience    Salary\n",
       "YearsExperience         1.000000  0.978242\n",
       "Salary                  0.978242  1.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dd8909f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='YearsExperience', ylabel='Salary'>"
      ]
     },
     "execution_count": 26,
     "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": [
    "sns.regplot(x=dataset['YearsExperience'],y=dataset['Salary'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50213920",
   "metadata": {},
   "source": [
    "## Model Building "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2ee29d4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=smf.ols(\"Salary~YearsExperience\",data=dataset).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7498491e",
   "metadata": {},
   "source": [
    "## Model Testing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e6c02085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept          25792.200199\n",
       "YearsExperience     9449.962321\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding Cefficient Parameters\n",
    "model.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cd9942d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Intercept          11.346940\n",
       " YearsExperience    24.950094\n",
       " dtype: float64,\n",
       " Intercept          5.511950e-12\n",
       " YearsExperience    1.143068e-20\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding Pvalues and tvalues\n",
    "model.tvalues, model.pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f50ceba0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9569566641435086, 0.9554194021486339)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding Rsquared values\n",
    "model.rsquared , model.rsquared_adj"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfcb71dd",
   "metadata": {},
   "source": [
    "## Model Prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "da48505f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54142.087162"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Manual prediction for say 3 Years Experience\n",
    "Salary = (25792.200199) + (9449.962321)*(3)\n",
    "Salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee01c83f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Automatic Prediction for say 3 & 5 Years Experience "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0079cb4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data=pd.Series([3,5])\n",
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "55869751",
   "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>YearsExperience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
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       "</div>"
      ],
      "text/plain": [
       "   YearsExperience\n",
       "0                3\n",
       "1                5"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pred=pd.DataFrame(new_data,columns=['YearsExperience'])\n",
    "data_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "754f2300",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    54142.087163\n",
       "1    73042.011806\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(data_pred)"
   ]
  }
 ],
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