traditional_ML_algorithms/SimpleLinearRegression/SimpleLinearRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c6739181",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4b2e9289",
   "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>Temperature</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.566884</td>\n",
       "      <td>534.799028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26.005191</td>\n",
       "      <td>625.190122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.790554</td>\n",
       "      <td>660.632289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.595335</td>\n",
       "      <td>487.706960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.503498</td>\n",
       "      <td>316.240194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature     Revenue\n",
       "0    24.566884  534.799028\n",
       "1    26.005191  625.190122\n",
       "2    27.790554  660.632289\n",
       "3    20.595335  487.706960\n",
       "4    11.503498  316.240194"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing Data\n",
    "data = pd.read_csv('./data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d82aa83e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1eb7f66d070>"
      ]
     },
     "execution_count": 3,
     "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": [
    "#Plotting the graph (Temperature v/s Revenue)\n",
    "plt.scatter(data.Temperature, data.Revenue, marker = \"x\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d4fe112b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now to start predicting, we need to fit the model\n",
    "def fitSLR(X,y):\n",
    "    #Preprocessing\n",
    "    X = pd.DataFrame(X)\n",
    "    y = pd.DataFrame(y)\n",
    "    data = pd.concat([X,y], axis=1)\n",
    "    \n",
    "    #Calculating mean for both Target and Feature Variable\n",
    "    meanX = float(X.mean())\n",
    "    meanY = float(y.mean())\n",
    "    \n",
    "    #Calculating `x-mean` and `y-mean` for each data point\n",
    "    data['x-mean'] = X - meanX\n",
    "    data['y-mean'] = y - meanY\n",
    "    \n",
    "    #Also calculating product(`x-mean, y-mean`) and square(x-mean)\n",
    "    data['mul'] = data['x-mean'] * data['y-mean']\n",
    "    data['sq'] = np.power(data['x-mean'], 2)\n",
    "    \n",
    "    #Summation of data['mul'] and data['sq']\n",
    "    sumXY = data['mul'].sum()\n",
    "    sumX = data['sq'].sum()\n",
    "    \n",
    "    #Calculating the coefficients or weights\n",
    "    global b1, b0\n",
    "    b1 = sumXY/sumX\n",
    "    b0 = meanY - b1 * meanX\n",
    "    \n",
    "    #We will be returning the coefficients/weights from this function\n",
    "    return b0, b1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "23c56837",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(44.831267090563585, 21.44362551068026)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitSLR(data.Temperature, data.Revenue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d304d98d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now that we have fitted the data, we will now have to predict!\n",
    "def predict(X):\n",
    "    predList, y = [], []\n",
    "    for i in range(0, len(X)):\n",
    "        predList.append(X[i])\n",
    "    for i in predList:\n",
    "        itemY = b0 + b1 * i\n",
    "        y.append(itemY)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "71d56166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[571.634336557209,\n",
       " 602.4768474448201,\n",
       " 640.7614972276658,\n",
       " 486.4699191697509,\n",
       " 291.50796254571776]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let us predict on the first five elements of the dataset, usually there should be train-test-validation dataset,\n",
    "#As this is just a demo, we will be predicting on the given dataset itself!\n",
    "predict(data.Temperature.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ffc7bb28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    534.799028\n",
       "1    625.190122\n",
       "2    660.632289\n",
       "3    487.706960\n",
       "4    316.240194\n",
       "Name: Revenue, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let us see what is our original values for the same!\n",
    "data.Revenue.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "304735ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting on the whole dataset\n",
    "predList = predict(data.Temperature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b9042109",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1eb7f7caf10>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9A3wDVAFbgGVABvCmUuphZ9srzqe8ArymlPoBOIox00cI0YnUXpDlEt0vBHt+Cfb8kjrPWbFhD0o7WPVuMuf8+K3Z/rfXv+Ce347l4dXbPc4vqahGa43FYiFj1i/MRVeidUg9fSFEs2mtGTx3tXk8a8JZPP3JD+ZxdL8QxgzpSVpWLhN3beLl91LMx26/6n5WjxhPYuwgAHPKp0KxZe8xsvcWygraFmqsnr6UYRBC1KuhjUW01lxdK4//2kbPwVp7fgl7cw+ye8lvsThn73zXfzhX/f5xHBYjVZOWlcvoAd3MgL88K4ek2ChGR4bL9MrTSIK+EKKOeqtnrrIR1sVK0YkqvnXL49vzSzhWWunx/Fnr3+CuL143j6+8ZSlbe0bRKySALx+41Fyklb2v0BwTcPXuQWbbnE4S9IUQHtwHa8GYGz/tuSyy9xaQFBtFSKAf0RGh2POK6+Two47u59OX/mgev3TB1Twy4RZ6hQRASQUDwruglDJLHrtP55R0TtuQoC+E8NBQ9UwwPhDmTBpG0YlK7O6zdLTm1bfn88ucLWbT6Fn/oqCLUW7hcEkFibGDWDB1pPnNofaq3IXpNgn8bUCCvhBepjU2+XYFfveAD5C2IZe0DZ75+4t//Jq0dx40j+decx9vDP1Fo/fnqnHvSum46uWA9PhPNwn6QniR1trJypXDb0xwxQk2P3MDXarKAbD3juKGPz2LNdAfiivqnJ+918jdS32c9iVBXwgvUV8u3r3ipMPh8Jjz3tA3AI+eeGwUGl2nlMJtG/+P+z5bYR7HT1/Ctn5n0dPfyoHiCjPn79ruMDoilOy9BWYKR+rjtB8J+kJ4icZ2sgoNtJKSYa/zDSA00Mpdlw03r+EKxO49ca1rgn5kQT7/ffEW8/yV469mVlzNsdXPQmLsILp18WfskJ4kx0eTkmEnNNDK2CE9PXryUh+nfUjQF8KL1JeLdwXe+r4BxESEMnviUCwWi1sqyMqcScPNomrTnl0PWvPSeylM+uFL87o/v/OfHOkaTkxEKBOj+1JUVklaVi4KxeyJwzzuxxy8lcDe7iToC+FF6ts1yrXLFHh+A3ClXlwpl5R0O8uzjA+CmZecicVi4aFV2whZ/zk5b80zr/eXKbN555yJAPgpsOUVM8bZq//yp6PY8oo8gntDPXvRPiToC+Elau8a5d6j11rX+Qaw6s44c5GUK30T3S8EW14xFyz6hDO7Kv4x7xrCyoy5+D/2GMDlNy+lyq8mbFRr48PD9W3ClldMUlyU9Oo7MKm9I4QXqW/2zrTnjJIJowZ0Y8WGPea5XfwtDOoRzPYDNQusAv2gygGJX/6b5E9qSh9ffeOTZJ9Rk/tviNTM6Rik9o4QPsI1K8bd6Mhwo+TB3kJuGhvJqq35HCut5ESlwyPgA/Q6epD1L9xsHr/5s8u4f/IsY+PyWqUW6iP5+45PapYK4WWWrNllbgKemrkTrbVRBgF4dePeOnVyANCaZ//9qEfAv+COV7l/8iyAep8zom/XOm0L0204HA4WpttIzdxZ53HR/qSnL4QXcZ+rvyX3GOXOzUsaM2bPd7z1xlzzeO7ld/LG6JPvfLr9wHEAugf7c6y00tgxa30Om3Yfkdx+ByZBX4gO6FRLKbimSLrPrW9IYFUFX7xwM72PFwCwL6wPE/7wIhVW/ybfZ2LsILTWfLuv0Fxx6wr4ktvvmCS9I0QHk5q500zPQM2snMbSJe55fKWUOUWzITd+k86OJ68xA/6vrn+c8bf9o1kBH2DT7iOs2LCHcyO7e7RLwO+4JOgL0YG4p2dcgd817bKorNIjuLt+Ts3cYebSAZ78yM4Fj6yt9/p9iw+T81gCKZkvAPDuyEuIuncVmwfENHpfPzsjlMRxg+q02/NLiI4IrTN47P6hJToWSe8I0YG4p2fcF1Ilxg4ySyxAzdTMYKti3c7D2PKK2bT7CJcM68nL6/dQXuUg0KroYrVQUFYNWrM4YzHXbFtnXmPsbWnkh/Vq0n39PKonoUFWI/Arzzr4AX4W0jbkSsXMTkKCvhAdzJI1u+rUmlcofrdsAzFndCM5Ptr8NhBotVBe5aBHsD+2vGJszkFbC1BepSmvqibB/jlLVz5uXmv+xD/y6s+nNvj63btYOXaiih7B/hwtrSQ6ItQsg5ycYCzC8rg3pcwPJamY2fFJ0BeiA3E4HBSdqGR5Vo5H+/KsHMK7WNn00zEAQgIsKKC8ykjpHK01pdIBBFWWsX3xrz3ah9/9HuXWgDqvO6p/GPlFZRSeqOLYiSpiIkL54PZxXPXcBkICrSTFRREWZOXh1dtJy6rbqz83Mty8llTM7Ngk6AvRQaRm7qTwRAU0kAovOFFFr64BdTY2qc/DHz3LDdkfmsd3T5nDu+dc2uD5PxsQxrmW7qRtyKVHsD8hgX4s+s9OcyZOcnw0FouF1MydTaqDLwG/45IyDEJ0AO4DtqMGhFFRpbHn18yvD7IqyqpO/m+19h61AFH3roKTBOHofqFMiunLGvsBM0UE9ZdVaI2ducTpJWUYhOjgatfCr60pAf+rZ26gd2mBeTw56WnsfYY06fXt+cUeHzIu9aVppA5+5yZTNoVoZ65v20op5k0Z0eB5DYXWy3dmkfNYghnwM4bHEXVfepMDfmNk6qX3kZ6+EO3IvSrm4o93sMZ+oMFza4fewKoKdjx5jUfbObPfojiwbk2c5oiJCCV95vg6G69Ij947SNAXop24L8T6JvcYe46WmoXNovuFYM83KmB2C7Rgtfpx5HjNDJ15a1/ilq8/MI8fuPwO/jV68infS6+QAK67IJK12w9iyyv22HhFpl56Fwn6QrQTV7kE7dCkbfCsk+MK+FYFheUOKDemZg4oPMAXL8zwOLcpA7UN6RFsxWKxcLikgpKKalbdGcfDq7cTFuSPxWKRHr4XkqAvRBtz5ciXrNnFpzsOMmpAWIPnDu1b0+P//IUZDCysSf9MvSmV7yKGNvl1LQoctXJER0uruGlsJH4WP8KC/PHz8/MI9BLwvY8EfSFOk9pTGauqqnhm3W4+23mQ0ZHhfJN7lK37i/l2X2GD17Dnl3Dxj1+R9s5DZtvaMy9gxq8XNPk+hvUJ5sjxSo/0kDs/ix/JCcY8fJBA7+1aFPSVUuHAy8DZGONMNwM7gLeAKCAHuFZrfUwZ/yc9BUwBSoFErfU3LXl9IToq9wFagHGL1nC0tJLhfbryXV6JWYa4Mf7Vlex6YppH26hZb1DYJbRJ9xBoVZRXaXYeLGX62IFkfJ+Pn4IDxRXmOUmxUYR18TcDvvB+Lf0v/RTwH631CGAUYAfuB9ZqrYcCa53HAJOBoc4/twLPt/C1heiQ3Adopz23nrGL1pJfXEFFtea7vBKG9wk+6TXu+WyFR8B/8NJbibov/aQBf3jfrkT3M84pd87tHx0ZzoNXnc2muROYfE6E572imT2x6Ski0fmdck9fKdUN+CWQCKC1rgAqlFJXARc7T1sBfArcB1wFvKqNhOZGpVS4UipCa513yncvRAfhnspxzbdvaCOTHQdLG7xOv6LDbHw+0aNt8L0r0arx/tnIfsH4+weQvbeAxHGDPBZavX97LECDdXOkVo5vaUl6ZzBwCFiulBoFbAb+DPR1C+T5QF/nz/2BvW7P3+ds8wj6SqlbMb4JMHDgwBbcnhBtwz2Vo5QiNXMHmbYDHDtecfInu/nPK3cw4nDNh8Q1N/ydb/ob0yYtGEXUalMYeVWt/Hj3T2NJybDXSR0tTLcxPyGGsCD/JtXNEd6tJUHfCpwHzNRab1JKPUVNKgcArbVWSjVrOZ/WehmwDIzaOy24PyFOO/dUDkByfDSZNqN+jV8T42hcTjavvzXPPM4a+DN+f90ij3POGRDGt/uK6jz3zkuGsHb7IUKdUywViuy9BQ3Wtgc8vpFID9/3tCTo7wP2aa03OY/fwQj6B1xpG6VUBHDQ+fh+INLt+QOcbUJ0eA0VGatdM8cVYIP8LZRV1tc3r+HnqObHv1/l0XbezNc5GtytzrmugN+rawCH3b5BrN1+iJV3xGK1Gv+Uw7o0rzcvAd/3nPJArtY6H9irlBrubLoUsAErgenOtumAa9ngSuAmZRgLFEo+X3QG7nvWuv649qx1BX/3Xa0AhvcJafSas9a/4RHwH704kaj70usEfEutmOwe8HuFBGDLK+aRD3eYc//nTBpWZ579/IQY5kwa1uzfW3inls7Tnwm8rpQKAHYDSRgfJG8rpWYAucC1znNXY0zX/AFjymZSC19biNPOPX2zZc8xzo3sjsYYoE2MHcTCVTbCulgpPOE5B76iuv5efu+So3z17E0ebUPu+QCHxa/OuX4Kqt0SnH1CAzjoNt1y09wJ5upZpZTHtw930psX7qSevhAnobXmoVXbPGbiJMYOQqFYnpVDTEQotrxiEmMHgaZOSQWXxelPeuxRe+3v/8aXkWc3+T5GD+hGtttCrpiIUFbdGYefn5/57SMsyF969aLRevqyIkOIJlgwdaTHcVpWrrFvbGwUIYFWYiJCmTdlBKFBdb88n3V4DzmPJZgBf0vEcKLuS29WwAfI3lfI6AHd2L1osvlBM3XpehwOhzlgW1RWKaWQRaOkDIMQDdBas2TNLgpPVJC9p6DeczbnHuW8gcY2g+c/stZj8FZpB2/+ay5j9m0z23725zcpCmo831+fIKuFsioH5VXVAKTPHE/CM19gyytmyAPGtoj17XIlRG0S9IVPaepWf6mZOyk6UYlDO1ixYY/Z7gq+Llv3F1FeWYUFYw9bl0m7NvLSew+bx7dddT8fjhjfpHvsHuxvllh2KatyEN0vhEkx/cySCekzx5sBH6TmvWgaSe8In+E+CwfwmIXjzuFwGIO3WcZq1VH9a6pgugd8lx0HS82FUyHlpeQ8lmAG/OyIYQy554MmBfwu/orofiF89cAEYiLqlltInzmeuy4bbt57Sobd43HZ5Uo0hQR94RPcZ+G4gmN9efDUzJ2kZNgJDfSjV0gAaVm5fLu/7qKo+vz5i3/x/ZJrzeMrkp7h6psW1zszp7ZzzgjhRKXmwsE9eGT1Dmx5xXUCvyt/737vSXFR/PToFJLiojx+NyEaIukd4RMaWkTlngfXWlN4ooK0rFx6BPtztLT+UsS1DT66n3Uv/dE8XnbBNBZNmNHIM+r6eVRPzo/qRVgXf0CbA7VJcVEkx0eb+fuUDLuUVBAtIlM2hU/RWjN47mrz+KdHp5hBcvHHOygqq2TT7iPmxiWNUdrBq2/N5xe52Wbb6Fn/oqBLw5ui1Md9yueCqSNRSrH44x0Ul1eZQd3hcJCSYfeYktnU8Qnhexqbsik9feF1GgqGrrSIO1cxsiVrdpJpP4A9r5gRfU8+u6b2xiazpt7DypiLmn2v3btYWXlHLI98uMOjl37XZcM9fo/6ti6URVjiVEjQF16ldsVLV6APDbRSXF5l5sFDA62ssR9g+focMwduzysmyN/C9gMN9/KDK06w5enfE1htpH5sfQYzdfoSqpuQt6/PsRNVXPlslrnIyp0EdXE6yECu8BonG6wNC7KSFBfFvCkjKC6rMgdLbf8rYtPuIwCNFkm7beP/YUv9jRnw46cvYUrSM/UG/O7B/k2650CrhZBAvzoBX4jTRXr6okNrTt66ocHaXiEBoGH2xGHmB8GWPccYHdnNo/Z8eBerx1x7l8iCfP774i3m8Yrz4lkw6bY651kVfDd/Ar9a9hU5h497PBbopyh3K6QT3c/Y8Ly8ysHIM8IlHy/ajAR90WE1lKpprL6MK/C7Aj7A4ZIKsx5O9r4CM9AP79vV47l1Ar7WvPzuQib++JXZ9PM7/8mRruH1vnaVhmkvfsl2t0HgXl396R0ahD2/mCCrhaiewQT5+5G9r5DofqEEWC2EdZEZN6LtSNAXHVLtzUncNwRJiotqsGdc32Btr5AAj8AP4G+BHQeO1366afxPW/jn28nm8d1T5vDuOZee9L6315r1kzDqDMKCrCgFl47ow5xJw1BKOT+8rMyeOEwCvmhTMmVTdFjuOXmXxurL1F60lBwfzcJ0W7371DYkqLKML5feRFiFsY/tjz0GcPnNS6nya7h/VLs0g3mvsVHMn1oz5dJVPsF1rxLsxekiVTZFp1Tf5iSN1ZdRSmH7XxExEaGEBvqRkmEnJMBCoLVp/5vP+PJ9ti/+tRnwr7rxSS79wwuNBnyovzQDgKamQ+Ue8F33KkR7kPSO6LAam1fvvmmI61ytNdH9QknbkIvWYM8vJsBPUVHd+LfZ/oUHWf/CzebxGz+7jLmTZzXpHkf07cr2A8fp2dWfI8c9V/C6vmG4FlwJ0RFI0BcdUu1UTe1NvsOCrBSVVTkXVu2isLSCD7/Pp6iskp5d/bHnFwM0HvC15rl/P8qUnVlm0wV3vMqhkB5Nvs/DJRXcNCaSrf8r4sjxQmMjFSB7TwHZ+wo9ZgcJ0RFI0BcdklKqwfoyoYFGwF++Pge0UdN+q1tRtBMn2ZAcYOyerbz5xgPm8f2X38mbo69o9n0ePl4JCi4a1ptzI7szf2pNOsrYSlFm5oiORQZyRYd2spIK7oO8TRFYWc4XL8ygd2kBAPvC+jDhDy9SYW3aYiqA6IhQJkX3IdN2AHt+CaMjw3n/9liAJq8pEOJ0kto7otNqqBSBUop5U0Y0K+jftHkVC9e8aB7/6vrH2TzAc6C4V0gADoeDo6VVdTYmD7Ja+N0FA5g/dSQWi4XZE4eRkm5vsDcvAV90RBL0RYdWu7fsmvqYmrmDTNuBJl2jX9FhNj6faB6/O/IS7o6/C9yue8OF/bFaraRl5fKz/mH0CXXUqcFTVuUApTyLoE2V3apE5yJBX3RYtVfkLv54B2vsB5gY3Yc19oPY8orpEexP//Agvvtfcd0LaE1q+pNMs31qNo25PY0Dob3qnPrPL/czvE8wowd046LhfdBaY7EcwJZXc92YiFC61erVS8AXnY3M0xcdUu3iaQ6HgzV2Iwhn2g9yybBe5kYn9QX8n++zkfP4VDPgJ0/6E1H3pdcb8AH8lLHt4eiB4fz50rMoLq8yNzFx7UxlyyumqKxKdqYSnZr09EWHpJQiNNBKTESoR/G0IH8L9rxi7Hl1A70C/Ksq+eSlWxlQdAiAAyE9+OUfX6bcGtDo61Vrz9W+sjOV8FYS9EWHpLU2e9vuGit9/Lvs//DoR0vN499e9yibBp7T5NdMjo82A/qcScM8xhNcgV8CvujsJL0jOiTX7JxeIZ499O7BdfspvUuOkvNYghnw04ePJ+reVc0K+AApGXYcjpoPFdnERHgj6emLDmnxxzvItB/gcEmFR/uxUs/yx4+tforffpdpHsf96R/s79an3mtaFDgaSMe70kibdh9hUkxf5kwa3rJfQIgOSoK+6HAcDgfF5VXY84rpHmytE+h7dfVnwK7v+fdrd5ttKRNu4ZULrm78utrYzKRroB9H3a7ZI9ifS0f0Jr+wDFteMWOG9JSFVcJrSdAX7aa+1bZL1uyiqKySeVNGsOrb/9Xp6ftXV/J26q0MOfY/AAqCQhh7expl/kFNes2QICtHjlfSPdifY6WVBFotHC2t5Jl1uwGjx++e2xfC20jQF+0iNXOHWTDNVW9+YbqN7L3GzlZaa/qHd/EI+r/+bg1PrF5iHt//xyd4M3xEo6/jbwH3sV9XJcxjpZXG/ri1BorTZ46vUwZZCG8iQV+0OdciK1fATY6P5sJFazlcUsHoAd2I7hfisfFJz+MFbF56g3mcedYY/nDNPI8VtQ2pdBilFRLOifDYKhFg1Z1xnPnX/3icn5Jhl1k6wqu1OOgrpfyAr4H9WusEpdRg4E2gJ7AZuFFrXaGUCgReBX4OHAF+q7XOaenri87FfSpmj2B/jzn4AHuPlXrUpV/48fPctCXDPP7FH19mb3i/Zr9m2oZcovuFeLRf8Mhaj2PXYC40vlmLEJ1Za3yP/TNgdzt+DEjVWp8FHANmONtnAMec7anO84SPUUqRHB9NTEQoR0sr6zzuCvhn5/9AzmMJZsB/7KLpRN2X3uyA77pmTEQo9vwSEmMHsXvRZPP1ewT78+MjV5grbmMiQp172krAF96pRT19pdQAIB54BLhLGf9SJgC/d56yAngQeB64yvkzwDvAUqWU0rKm3au5/vO673CllKo3tQLg56gmY/ksRhw20jsnrIH8fOY/CQgLgbLqel8j0E9RfpLdsSZG92XMkJ5mD35idF9nex/8/Pw8avXLdE3hzVqa3lkC3AuEOo97AgVaa9d8uH1Af+fP/YG9AFrrKqVUofP8w+4XVErdCtwKMHDgwBbenmhPqZk7+WznQc6N7E5ygjEj5qFV28jeU8DeY6V1zr/S9ilPr3rCPJ7+m4f4bMjPAaiqqD/gA5RXG9skTorpQ6b9YL0lGorLqzxm5dx12XBmTxxqDtrKilvhK0456CulEoCDWuvNSqmLW+uGtNbLgGVgbKLSWtcVbUtrTeGJCrL3GlsGbvrpCOcPCufVjXvrnBt+oojsp39vHn82+Dym/+Yhj4HaikY2w+rZ1Z9Vd8byyIc7sDvHCo66zc5pKFcvm5ULX9SSnn4ccKVSagoQBIQBTwHhSimrs7c/ANjvPH8/EAnsU0pZgW4YA7rCSy2YOhKtNSs27MGWV2zO1ukVEmBOxZy39iVu+foD8zmX/OFFfurR3zzuFezH8Urd6BaICT+LwM/PzyySFhroR3F5Ncnx0aRk2AkNtDJmSE8pliYErbRdorOn/xfn7J3/A97VWr+plHoB2Kq1fk4pdQdwjtb6T0qp3wHXaK2vbey6sl1i55SauZPCExUsmDoSgMFzV9c5J+bwT6x+ZaZ5vHj89Twdd12zX8vYurAvd11m5OHdt1Os728hfEFbb5d4H/CmUuphYAvwirP9FeA1pdQPwFHgd6fhtUU7czgcfLbzoLnAyr2AGYDFUc37r/2FUfm7ANAWC+fMepOSwOBGrzuqfxjnDurOpt1HsOfX7GgV6GdhzqRh5rF7Vcz6/hbC17VK0Ndafwp86vx5N3BhPeeUAb9pjdcTHZOrhz86MpzsvYWs2LDH4/Ep27/guQ/+Zh6vuOcJFgXEUF7VSMLe6dxB3dFaY88vISYilEkxfSk8UUlaVi4L020yCCtEE8mKXNEqXDtdpWXlMmpAGNERoeYsmrCyErY+VfPFbmPk2Vx33SK0ssBJAr6rh5+WlcvoyHASYwcxPyEGi8VipmwkVy9E00nQF83inht3n4PvWnSltfYooXDvZ2ncvvEd8/jSGc/zY6/IOtcNsirKqjSBVgvlVQ6zuqayKI/drGZPHCobmwjRAhL0RaPcg3xq5k4KSytYcOVIlqzZRUFpOQpFt+AAZk8cysJ0G2FB/gCcdXgPa1653bzO0nHXknrRTTS0hqqsyphrv/KOcSz6z05CA/0oKquiW5cALBZLg8FdAr4QzSNBXzRo8cc7KDxRwYNXng3Ap9sP8O3+Ir7ZcxSlLHy7zyheNqp/GAWl5azYsIfeXfx4+/V7uXCfzbzOz/78JkVBIXQP8uPYiYYXWY0Z3AOr1WoG+NrbFQohWk6CvgDq1rZf/PEOXvrvbnN+/PyEGHKPGqtot+73XPH67f4ivt1fxGU7N7Ds/UfM9j9dPZf/DI8zjxsL+InjBtEtOMDjHiTQC9H6pHC4D6m9JsN1nJq5k4XpNnN6pcPh4GNbvhnwV2zYw5l//Q8FJzx3sHIJLT9OzmMJZsDfEjGcIfd8QN/p1zGir2dly+F9gvnxkSuIjgj1aFdKMXvi0Jb/kkKIRklP30ekZu6kqKzSI3Vi5OCtFJVVmfvDps8cT0qGne35JYR3sdYJ9LW3L5z9xevMXv+Gebzi+Q945nAXHCUVrNjoOWUT4HBJJb96foNZLuGsPiGM7N/NKJOgpKSxEKebBH0f4JpO6V5/ZmG6jeXrc0iKi6Krv4Uewf7Y8ooZ8sCHAGb9mtpcAX/IkX188vKfzPZlF0xj0YQZRJeFcLikpM7zXLNyjpRWojlOdL9Q7PnFjOzfjeT4aACZeilEG5Cg7wNcUxsBj01LkmKjzPo0tQP80dJKFFB7so3SDl57K5nxud+abZfOfYfxFw6DjXvwtyhz/1l37guwjpZWcbS0mKS4KLNnLz18IdqG5PR9hHvgd9Foc3597V2ljMc9XfzjV/z0+JVmwP/79AWcn/IxPzqCUBbF9LEDsfhZ6gT8hrgHegn4QrQN6en7CFcO311aVq4R2RUe9WxcLBg5/BMFxWx5+joCq43UzrY+Q7hyeio3jh/CFIeDVzfuJXtvAe/dFsu057KafE9SPkGItidB38vUnnrpmqHjnsOfnxDDwlU2lmflkLYht6FL4QBu/PQNZq9bYbbFJz7Ftr5n0j3Yn+T4aCwWCxaLhbSsXHM8AKiT4gnwU1Q4V2ZF9wsl0GqR/WiFaAetUlr5dJHSys3T8AwdY5Vs7cemPbee7L2F5vNH9O3KmCE9+Sb3GIXbdvL5sj+Yj6Wdl8CDk/7k8XqjI7vx/u1xaK09Ar6r7k7Prv70CQ3Cnm/M6w/vYsVqsXD4eAVJsVFoNN26BHhUyRRCtFxbl1YW7eBkM3Tq5PO1Zu/R2lsWKlZk5fJO+iOcb9totp4383WOBner5zUhNXMHa+wHPdoPFZUxOrIb790WCxjfMtKycs3pn+73Iz18IdqWBH0vYQ7Uas8ZOtERocybMgKlFA6Hg5QMOyEBfny+6xBHjnsOuPba+F9y3k42j++Kn8N7Z1/a4GueOzCcTNsBs9xx+szxJDzzBba8YnqHGqtvLRYLC6aO9CjCJukcIdqPBH0vsmTNLnStOTf2vGLGPPoJvzt/AJ/sOIQtr5jofqEcPV4OQJC/BXW8lC+fvZHQihMA/NgrkssTn8Ev0B+q6k//+Smw/a+ISTH9UOqAxxz/mIhQJkb3Ncsf1x5AlgFcIdqPBH0v4dqI3L1H7XK4pIKln+4GjAFWe34xQVZjtu4N699h3rp/mOdeedNitkYYOfaqBgI+QLWGEf1CmDNpGLMnDvXI6afPHO8R8D0GkJ3HID1+IdqDBP1OzqMSJScPoK4ZNb2O5PHFCzPM9n+NupwHrpjZ0NPq9VVuAQ6Hg4dXb/doT8mwe9TAr70IC2T1rRDtRYJ+J1Z7tk5YFysxEaHY8oobfpLWPP/vR5m8s2Y+/QV3vMahkO7Neu0R/UKw5xUzdel6bHnF5uvGRIR69OTnTBpWp0Sy9PCFaD+yIrcTcZ9eq7Wm6IQxW2fhKpvzuKrRgD8udys5j081A/59V8zk3Ic+qhPwzzkjlMRxgxq9F4VRDtn1erY8o6xC+szxJMVFefTkawd4CfhCtB/p6XcStXv1YJRR6BsayPKsHJZn5ZjnDu/blcMlFebsnMDKcta/cDO9So05+Xu69WXKn16kBCuUVhoBXmGOB1Q5tLloK8hqoayefWzt+SVcOLiHR5vU0RGi45Ogf5rVt0K2uQGxoTn4aVm5dO/i+Z8wvIuVHQeOAzCibwhjVr/BQ2teNB+fM/MZ3g8eDEDPrv5E9ghmwZUjzcfTsnLNkgyJsYPYsucY3+4ronuwPzeOHURxWRXLs3Lo2dWfb90WdoHMyhGiM5Cgfxo1tkK2OatQG6qSGWhVHKtV777gRBVBVsWtgwO465aLzfZ3z57A3fF3sXvRZLqtspG9r4BfDu3NnEnDzCBdez79gqnGXrjnDezBvPgR+Pn5obVGa032vgKy9xbKrBwhOhkJ+qfJyVbINrfH7wr8rusBlNc3pVJr/v7vJ5i67VOzacztaRwI7QVASrqd+VONoFz7G0hD8+ldr+/627Ux+rkDu8usHCE6GQn6p0mDNezdpi82R31BuXa+/fx923jn9fvM43mTbuOf58UDEN0vBHt+iZn7nz+1pixDU+bT1/7dZFaOEJ2TBP3TqL7eeVMCo2uWjus8h8NBSrqd5Vn1B+WAqkrWLbuV/sWHADgQ0oNf/vFlyq0BgJGbT46PJuGZLzhWWklYF8/e+KnOp5dZOUJ0PhL0T6NTKUGQmrmTz3YcZPTAcBZMHWk+58Pv8xkdGW4+t6u/hUCrhWu+Xs2jHy01n3/t7/9G8QVjKXerjz8/IYaUdDv2/BISYwcxe+LQOukl6bkL4Rsk6J8mp1KCQGvNZzsPkr2vkOx9NTNjXIOrV4zsaw6kbvlyOzseucY8Z9WIXzDzyntBKYIOH/e4rqtEQlJslJnWqW9AWXruQng/CfqnyammTEZHhps17t1n0kT3CzF67Bl2fvXsfF7/6D3zsdjb/sH/wvqYx2VVmh7B/lw/JpJn1u022x3ayP+3ZEBZCNG5ySYqp1lT5+m72rXWPLRqW53CaT8+cgXLU9/mlnt+b7at++P9JIWPb/C1GyvJcKoDykKIjq+xTVROuQyDUipSKbVOKWVTSm1TSv3Z2d5DKZWplNrl/Lu7s10ppZ5WSv2glNqqlDrvVF+7M2lKyiQ1cwcL0201ZRZqfRD7V1eyp3ekGfB1jx7okhI+j7+hwdftEexvlkbYvWgySbFRHo9LwBfCN7UkvVMF3K21/kYpFQpsVkplAonAWq3135RS9wP3A/cBk4Ghzj9jgOedf/u0Jz+ys3a7Uedea412aFZs3GM+/putmfz9w6fM4+qPPuLhsv5sSdtiLI5ybjtY+5vB0dJKYiJCSY6PNr5B1KqzL6tnhfBNpxz0tdZ5QJ7z52KllB3oD1wFXOw8bQXwKUbQvwp4VRvd2Y1KqXClVITzOl6lsZSO+8/XvpDFroMlHHMGaPfA3bu0gK+eqenJfzx0LLdO+ysx9iBseTmMjgwnMXYQ86fGkJq509yX1iUmIpRLR/RBKWWWbEiMHcSCqSNl9awQPqxVBnKVUlHAucAmoK9bIM8H+jp/7g/sdXvaPmebR9BXSt0K3AowcODA1ri9NtXUzcm11hSXV3GstJIgf4tH7j3l4+e4cctq8/jS21/hx1DjbXSVL37vtnFmsC4ur8KeV2zOznEF9TFDegJITXshhKnFQV8pFQK8C8zWWhfV6uFqpVSzRoq11suAZWAM5Lb0/tpSo6UXaqVh5ifEMCaqB/a8YsoqjVk1Z+f/QPqK2eb1/nZRIi+M/XWd13FtRehysqAuc/CFEC4tCvpKKX+MgP+61to1h/CAK22jlIoADjrb9wORbk8f4GzzGicrveDi/tiNYwbwRlYOGWmzGH7YyOWfCOjCz+94ldKALvW+TnF5lUcQb0pQlzn4Qgho2ewdBbwC2LXWi90eWglMd/48HfjArf0m5yyesUChN+bz3QO/iysAL1mzq86WhidWvM4PT1xtBvw7b3iY6Dn/R1B4mMd5MRGhxiycuChj4xT32T5IUBdCNE1LevpxwI3Ad0qpbGfbA8DfgLeVUjOAXOBa52OrgSnAD0ApkNSC1+6wGiq9kBwfbex05Sx41r20kC3PXG+e47jsMhImP4Atv4RAq4WjpZUkxUURGmhljf0AtrxiUjLsJMdHA5KPF0KcmpbM3vkCGtyJ+9J6ztfAHaf6ep1Bo6UXdM2K2OS1LzHj6w/M5y15diUFAwYzMcgKSlFQWsnlZ/czvyHMnjiUlAw7YUH+WCwWyccLIU6ZlGFoRScrvdDrx+3kPHaVef6T46+n8O572bq/iOysXJLiolh1Z5y5WYkrsNcO9BLwhRCnSsownAa15+k7KiuxjBsHmzcDUKUsjP7zm5QEBpvnSFkEIURrOS1lGETD3AN3xrwlWAICzIA/41fJXJm6jpsnn+PxHNfKWSGEOJ0kvXO6HDsGPXoQ7zz86ZwL+fff08hzllyoLSXDLj19IcRpJ0G/heotuTB3Ljz2mNm29LmVPJFrAWeZY1f1y6S4KJLjo0nJsEtZBCFEm5Cg3wJ1Si58/z3qHLe0zV//Cg8/zB1a88TcmrIKE6P7MmZITymLIIRocxL0T5FHyYXqauY/cTtq/fqax48eRXXvXu+8/eLyKo8cvpRFEEK0FQn6p8gVqANWfcDcaQlm+1v3p7J93CTCvj7E7InhTd4yUQK+EKItSNA/VYWFqPBw5joPN58xgt9c/xjTx53J8iwjyINUuBRCdCwyT/9ULFgACxeah5fdvJSdvaPMY1eJ4/pq6Nd3LIQQramxefrS02+OHTtgxAjzcP20JK4f9isSYwex020DlNq7VEkxNCFERyFBvykcDpg0CT75pKbtyBG+3HyYxBMVdSpnZu8tbOMbFEKIppEVuSeTkQF+fjUB/403jI3Le/Rg9sShKJSZw//p0SkkxUaRvbegTuljIYToCKSn35DiYujVCyoqjONzz4UvvwRrzVumlCKsS62B2qkxoGSgVgjRMclAbn0WLTIWVrls2QKjRzd4ugzUCiE6EhnIbaoff4Szzqo5njULnnrqpE+TgVohRGchQR+MHH18PHz4YU3bwYPQu3cDp0vPXgjROclA7scfg8VSE/BffdX4EGgg4Kdm7vQYpHWVWUjN3NlWdyyEEKfMd3v6x49DRIQxYAsQEwPZ2eDv3+BTPOrtgEdZhaS4KOnxCyE6PJ8K+mZQfuIJuOeemge++grOr3fMw4N7GYXl63PM4C+7XgkhOgufSe+kZu7kqVcyQSkz4G++/DekfryjSQHfxT3wu0jAF0J0Fj4R9LXDwRXzb2f2Hy43255Y8Sm/Gj2dorLKZi2iqq9UsizEEkJ0Ft6f3lm3DjVhAtHOw3uvmMXboy4DW0mz0zKugN+UUslCCNEReW3Q16WlqEGD4PBh4/jMM2HbNt5esMY8p7lBWiklpZKFEJ2aV67Ife79r7n9mgvMY71+PQsPd2PL3mMexdBOdQBW5ukLITqyxlbkel1OX2tNafFxALInXIV2OFh4uBvLs3LI3ltIYuwgozBaXBTL1+ecUj5eVuAKITorr0vvKKW4+8Zf8lD3741cu3ND8tGR4YyO7MaCqSMlLSOE8Flemd4Bo8c/2BnwAX56dAqApGWEEF7Pp9I7AA6Ho95plbVJwBdC+BqvS++kZu4g03YAW14xSXFRJMdHk/DMFzKtUggh8LKevlEbpwpbXjExEaEkx0eTkmE3j0MDrRLwhRA+rc17+kqpK4CnAD/gZa3131rx2h61cYY8YFTOdPX4LRav+owTQohma9MoqJTyA54FJgMxwHVKqZjGn9Xs16i3No4EfCGEaPv0zoXAD1rr3VrrCuBN4KrWfAGpjSOEEA1r66DfH9jrdrzP2WZSSt2qlPpaKfX1oUOHmnXx2rVxWroISwghvE2Hm72jtV4GLANjnn5zniu1cYQQonFtHfT3A5FuxwOcba1mzqRhHouuXIFfAr4QQrR9eucrYKhSarBSKgD4HbCytV9EauMIIUT92rSnr7WuUkrdCXyEMWXzH1rrbW15D0II4cvaPKevtV4NrD7piUIIIVqdTF4XQggfIkFfCCF8SIcurayUOgTktuASvYDDrXQ7nZ28F57k/agh74Unb3g/Bmmte9f3QIcO+i2llPq6oZrSvkbeC0/yftSQ98KTt78fkt4RQggfIkFfCCF8iLcH/WXtfQMdiLwXnuT9qCHvhSevfj+8OqcvhBDCk7f39IUQQriRoC+EED7EK4O+UuoKpdQOpdQPSqn72/t+2ppS6h9KqYNKqe/d2noopTKVUrucf3dvz3tsK0qpSKXUOqWUTSm1TSn1Z2e7r74fQUqpL5VS3zrfj4ec7YOVUpuc/2bechZE9AlKKT+l1BalVLrz2KvfC68L+m2xJWMnkAZcUavtfmCt1noosNZ57AuqgLu11jHAWOAO5/8Pvvp+lAMTtNajgNHAFUqpscBjQKrW+izgGDCj/W6xzf0ZsLsde/V74XVBnzbYkrGj01p/Dhyt1XwVsML58wrg6ra8p/aitc7TWn/j/LkY4x93f3z3/dBa6xLnob/zjwYmAO84233m/VBKDQDigZedxwovfy+8MeifdEtGH9VXa53n/Dkf6NueN9MelFJRwLnAJnz4/XCmM7KBg0Am8CNQoLWucp7iS/9mlgD3Ag7ncU+8/L3wxqAvTkIb83R9aq6uUioEeBeYrbUucn/M194PrXW11no0xs51FwIj2veO2odSKgE4qLXe3N730pY63B65reC0b8nYSR1QSkVorfOUUhEYvTyfoJTyxwj4r2ut33M2++z74aK1LlBKrQPGAeFKKauzh+sr/2bigCuVUlOAICAMeAovfy+8saffJlsydkIrgenOn6cDH7TjvbQZZ472FcCutV7s9pCvvh+9lVLhzp+7AJMwxjnWAb92nuYT74fWeq7WeoDWOgojTnyitb4eL38vvHJFrvOTewk1WzI+0r531LaUUm8AF2OUiD0ALAD+DbwNDMQoV32t1rr2YK/XUUqNB/4LfEdN3vYBjLy+L74fP8MYnPTD6PS9rbVeqJQagjHpoQewBbhBa13efnfatpRSFwN/0VonePt74ZVBXwghRP28Mb0jhBCiARL0hRDCh0jQF0IIHyJBXwghfIgEfSGE8CES9IUQwodI0BdCCB/y/9276dyXP5e5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.Temperature, data.Revenue, marker='x') #Original Data\n",
    "plt.plot(data.Temperature, predList, color='red') #Best Fit Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46ab2315",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}