Assignment-05-Simple Linear Regression-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d19b1f56",
   "metadata": {},
   "source": [
    "# i) Delivery Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "33bd045b",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 5,
   "id": "c107607d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset1 = pd.read_csv('delivery_time.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b6762f3",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b76585",
   "metadata": {},
   "source": [
    "# EDA and DV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c8ab2d4d",
   "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": [
    "dataset1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eb285e98",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\python\\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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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(dataset1['Delivery Time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e999a331",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\python\\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": 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.distplot(dataset1['Sorting Time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f117fc49",
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renaming Columns\n",
    "dataset1=dataset1.rename({'Delivery Time':'delivery_time', 'Sorting Time':'sorting_time'},axis=1)\n",
    "dataset1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "316b5942",
   "metadata": {},
   "source": [
    "# Correlation Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "09ab868b",
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4e513b73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sorting_time', ylabel='delivery_time'>"
      ]
     },
     "execution_count": 27,
     "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=dataset1['sorting_time'],y=dataset1['delivery_time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "225c399b",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0ca74afa",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=smf.ols(\"delivery_time~sorting_time\",data=dataset1).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6fdc6ac",
   "metadata": {},
   "source": [
    "# Model Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d291b3c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept       6.582734\n",
       "sorting_time    1.649020\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding Coefficient parameters\n",
    "model.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "798b669e",
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding tvalues and pvalues\n",
    "model.tvalues , model.pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d277e377",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6822714748417231, 0.6655489208860244)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding Rsquared Values\n",
    "model.rsquared , model.rsquared_adj"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4402bd71",
   "metadata": {},
   "source": [
    "# Model Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2c4c7056",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.827834"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Manual prediction for say sorting time 5\n",
    "delivery_time = (6.582734) + (1.649020)*(5)\n",
    "delivery_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bee6134b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    9\n",
       "1    5\n",
       "2    8\n",
       "3    7\n",
       "4    6\n",
       "5    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Automatic Prediction for say sorting time 5, 8\n",
    "new_data=pd.Series([9,5,8,7,6,8])\n",
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "86d2e14d",
   "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>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sorting_time\n",
       "0             9\n",
       "1             5\n",
       "2             8\n",
       "3             7\n",
       "4             6\n",
       "5             8"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pred=pd.DataFrame(new_data,columns=['sorting_time'])\n",
    "data_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "34647534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    21.423913\n",
       "1    14.827833\n",
       "2    19.774893\n",
       "3    18.125873\n",
       "4    16.476853\n",
       "5    19.774893\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(data_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4b40653",
   "metadata": {},
   "source": [
    "# ii) Salary Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7172362d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset2 = pd.read_csv('Salary_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "510073d8",
   "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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa0b6863",
   "metadata": {},
   "source": [
    "# EDA and DV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a8d152d4",
   "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": [
    "dataset2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1a899672",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\python\\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": 45,
     "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(dataset2['YearsExperience'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9be9a00e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\python\\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": 46,
     "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(dataset2['Salary'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d5d0f6b",
   "metadata": {},
   "source": [
    "# FEATURE ENGINEERING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "dffba480",
   "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>years_experience</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": [
       "    years_experience    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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renaming Columns\n",
    "dataset2=dataset1.rename({'YearsExperience':'years_experience', 'Salary':'salary'},axis=1)\n",
    "dataset2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ee188df",
   "metadata": {},
   "source": [
    "# Correlation Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f348cf7c",
   "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>years_experience</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>years_experience</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": [
       "                  years_experience    salary\n",
       "years_experience          1.000000  0.978242\n",
       "salary                    0.978242  1.000000"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset2.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "85e1cb2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='salary', ylabel='years_experience'>"
      ]
     },
     "execution_count": 52,
     "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=dataset2['salary'],y=dataset2['years_experience'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed369de7",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "10c0d73c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=smf.ols(\"salary~years_experience\",data=dataset2).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e7bb713",
   "metadata": {},
   "source": [
    "# Model Testing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "edc98fed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept           25792.200199\n",
       "years_experience     9449.962321\n",
       "dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding Coefficient parameters\n",
    "model.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a796f2ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Intercept           11.346940\n",
       " years_experience    24.950094\n",
       " dtype: float64,\n",
       " Intercept           5.511950e-12\n",
       " years_experience    1.143068e-20\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding tvalues and pvalues\n",
    "model.tvalues , model.pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3fb75ed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9569566641435086, 0.9554194021486339)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding Rsquared Values\n",
    "model.rsquared , model.rsquared_adj"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e760f60d",
   "metadata": {},
   "source": [
    "# Model Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5790ab46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101391.898767"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Manual prediction for say years_experience 8\n",
    "salary = (25792.200199) + (9449.962321)*(8)\n",
    "salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f4d7da3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    15\n",
       "2     1\n",
       "3    20\n",
       "4    25\n",
       "5     9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data=pd.Series([10,15,1,20,25,9])\n",
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3caffc4a",
   "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>years_experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   years_experience\n",
       "0                10\n",
       "1                15\n",
       "2                 1\n",
       "3                20\n",
       "4                25\n",
       "5                 9"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pred=pd.DataFrame(new_data,columns=['years_experience'])\n",
    "data_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4215c2de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    120291.823413\n",
       "1    167541.635020\n",
       "2     35242.162520\n",
       "3    214791.446628\n",
       "4    262041.258235\n",
       "5    110841.861092\n",
       "dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(data_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "736f5604",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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