Predicting Revenue Using Simple Linear Regression.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# SIMPLE LINEAR REGRESSION\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PROBLEM STATEMENT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You own an ice cream business and you would like to create a model that could predict the daily revenue in dollars based on the outside air temperature (degC). You decide that a Linear Regression model might be a good candidate to solve this problem.  \n",
    "Data set:\n",
    "- Independant variable X: Outside Air Temperature\n",
    "- Dependant variable Y: Overall daily revenue generated in dollars "
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LIBRARIES IMPORT\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IMPORT DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "IceCream = pd.read_csv(\"IceCreamData.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.352514</td>\n",
       "      <td>367.940744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13.707780</td>\n",
       "      <td>308.894518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30.833985</td>\n",
       "      <td>696.716640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.976870</td>\n",
       "      <td>55.390338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>31.669465</td>\n",
       "      <td>737.800824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.455253</td>\n",
       "      <td>325.968408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.664670</td>\n",
       "      <td>71.160153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>18.811824</td>\n",
       "      <td>467.446707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13.624509</td>\n",
       "      <td>289.540934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>39.539909</td>\n",
       "      <td>905.477604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>18.483141</td>\n",
       "      <td>469.909033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25.935375</td>\n",
       "      <td>648.209998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>42.515280</td>\n",
       "      <td>921.508275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>29.589481</td>\n",
       "      <td>649.561175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>21.775948</td>\n",
       "      <td>534.622865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>25.457836</td>\n",
       "      <td>612.153949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>15.214569</td>\n",
       "      <td>353.325633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22.619316</td>\n",
       "      <td>524.236115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16.258721</td>\n",
       "      <td>374.231135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23.881725</td>\n",
       "      <td>523.124547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>18.978300</td>\n",
       "      <td>473.604335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>15.661464</td>\n",
       "      <td>402.455320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>29.185045</td>\n",
       "      <td>679.317791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>19.024611</td>\n",
       "      <td>517.534028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>35.120151</td>\n",
       "      <td>809.672053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>12.688430</td>\n",
       "      <td>276.787086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>27.887111</td>\n",
       "      <td>627.291295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>26.956720</td>\n",
       "      <td>643.648601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>27.375401</td>\n",
       "      <td>623.248701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>24.101616</td>\n",
       "      <td>586.150568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>28.790102</td>\n",
       "      <td>653.986736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>40.473989</td>\n",
       "      <td>918.391232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>25.545966</td>\n",
       "      <td>591.173390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>28.701276</td>\n",
       "      <td>651.186242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>29.463786</td>\n",
       "      <td>682.752869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>16.020975</td>\n",
       "      <td>372.990606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>14.739551</td>\n",
       "      <td>381.803014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>22.171199</td>\n",
       "      <td>515.459102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>29.035739</td>\n",
       "      <td>685.362388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>29.209715</td>\n",
       "      <td>654.747461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>16.364945</td>\n",
       "      <td>406.579249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>27.780500</td>\n",
       "      <td>643.944327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>13.330606</td>\n",
       "      <td>344.688765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>29.305039</td>\n",
       "      <td>642.227291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>14.384083</td>\n",
       "      <td>361.119144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>30.427792</td>\n",
       "      <td>704.281439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>9.073838</td>\n",
       "      <td>222.872317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>23.070616</td>\n",
       "      <td>543.599593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>8.586948</td>\n",
       "      <td>221.223291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>12.352081</td>\n",
       "      <td>337.119025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>9.018860</td>\n",
       "      <td>212.591740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>20.265012</td>\n",
       "      <td>474.749392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>19.363153</td>\n",
       "      <td>460.402500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>14.685945</td>\n",
       "      <td>343.362905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>9.954357</td>\n",
       "      <td>283.834327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\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\n",
       "5     14.352514  367.940744\n",
       "6     13.707780  308.894518\n",
       "7     30.833985  696.716640\n",
       "8      0.976870   55.390338\n",
       "9     31.669465  737.800824\n",
       "10    11.455253  325.968408\n",
       "11     3.664670   71.160153\n",
       "12    18.811824  467.446707\n",
       "13    13.624509  289.540934\n",
       "14    39.539909  905.477604\n",
       "15    18.483141  469.909033\n",
       "16    25.935375  648.209998\n",
       "17    42.515280  921.508275\n",
       "18    29.589481  649.561175\n",
       "19    21.775948  534.622865\n",
       "20    25.457836  612.153949\n",
       "21    15.214569  353.325633\n",
       "22    22.619316  524.236115\n",
       "23    16.258721  374.231135\n",
       "24    23.881725  523.124547\n",
       "25    18.978300  473.604335\n",
       "26    15.661464  402.455320\n",
       "27    29.185045  679.317791\n",
       "28    19.024611  517.534028\n",
       "29    35.120151  809.672053\n",
       "..          ...         ...\n",
       "70    12.688430  276.787086\n",
       "71    27.887111  627.291295\n",
       "72    26.956720  643.648601\n",
       "73    27.375401  623.248701\n",
       "74    24.101616  586.150568\n",
       "75    28.790102  653.986736\n",
       "76    40.473989  918.391232\n",
       "77    25.545966  591.173390\n",
       "78    28.701276  651.186242\n",
       "79    29.463786  682.752869\n",
       "80    16.020975  372.990606\n",
       "81    14.739551  381.803014\n",
       "82    22.171199  515.459102\n",
       "83    29.035739  685.362388\n",
       "84    29.209715  654.747461\n",
       "85    16.364945  406.579249\n",
       "86    27.780500  643.944327\n",
       "87    13.330606  344.688765\n",
       "88    29.305039  642.227291\n",
       "89    14.384083  361.119144\n",
       "90    30.427792  704.281439\n",
       "91     9.073838  222.872317\n",
       "92    23.070616  543.599593\n",
       "93     8.586948  221.223291\n",
       "94    12.352081  337.119025\n",
       "95     9.018860  212.591740\n",
       "96    20.265012  474.749392\n",
       "97    19.363153  460.402500\n",
       "98    14.685945  343.362905\n",
       "99     9.954357  283.834327\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IceCream.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "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>495</th>\n",
       "      <td>22.274899</td>\n",
       "      <td>524.746364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>32.893092</td>\n",
       "      <td>755.818399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>12.588157</td>\n",
       "      <td>306.090719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>22.362402</td>\n",
       "      <td>566.217304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>28.957736</td>\n",
       "      <td>655.660388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Temperature     Revenue\n",
       "495    22.274899  524.746364\n",
       "496    32.893092  755.818399\n",
       "497    12.588157  306.090719\n",
       "498    22.362402  566.217304\n",
       "499    28.957736  655.660388"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IceCream.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>count</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>22.232225</td>\n",
       "      <td>521.570777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.096388</td>\n",
       "      <td>175.404751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.122258</td>\n",
       "      <td>405.558681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.392791</td>\n",
       "      <td>529.368565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>27.740674</td>\n",
       "      <td>642.257922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Temperature      Revenue\n",
       "count   500.000000   500.000000\n",
       "mean     22.232225   521.570777\n",
       "std       8.096388   175.404751\n",
       "min       0.000000    10.000000\n",
       "25%      17.122258   405.558681\n",
       "50%      22.392791   529.368565\n",
       "75%      27.740674   642.257922\n",
       "max      45.000000  1000.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IceCream.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500 entries, 0 to 499\n",
      "Data columns (total 2 columns):\n",
      "Temperature    500 non-null float64\n",
      "Revenue        500 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 7.9 KB\n"
     ]
    }
   ],
   "source": [
    "IceCream.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VISUALIZE DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x142fac6c2b0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='Temperature', y='Revenue', data = IceCream)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x142fadba588>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(IceCream)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x142fbff5358>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='Temperature', y='Revenue', data=IceCream)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CREATE TESTING AND TRAINING DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = IceCream['Revenue']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = IceCream[['Temperature']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.566884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26.005191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.790554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.595335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.503498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.352514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13.707780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30.833985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.976870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>31.669465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.455253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.664670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>18.811824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13.624509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>39.539909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>18.483141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25.935375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>42.515280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>29.589481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>21.775948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>25.457836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>15.214569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22.619316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16.258721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23.881725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>18.978300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>15.661464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>29.185045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>19.024611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>35.120151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>9.309346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>19.494743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>22.225122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>18.887162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>21.752092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>18.159217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>14.866103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>28.829759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>25.333420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>18.508360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>22.482798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>30.085190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>16.997889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>27.281064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>4.865874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>23.407257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>12.301615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>32.632858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>16.703852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>26.964217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>23.824922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>34.472169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>23.056214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>14.931506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>25.112066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>22.274899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>32.893092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>12.588157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>22.362402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>28.957736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Temperature\n",
       "0      24.566884\n",
       "1      26.005191\n",
       "2      27.790554\n",
       "3      20.595335\n",
       "4      11.503498\n",
       "5      14.352514\n",
       "6      13.707780\n",
       "7      30.833985\n",
       "8       0.976870\n",
       "9      31.669465\n",
       "10     11.455253\n",
       "11      3.664670\n",
       "12     18.811824\n",
       "13     13.624509\n",
       "14     39.539909\n",
       "15     18.483141\n",
       "16     25.935375\n",
       "17     42.515280\n",
       "18     29.589481\n",
       "19     21.775948\n",
       "20     25.457836\n",
       "21     15.214569\n",
       "22     22.619316\n",
       "23     16.258721\n",
       "24     23.881725\n",
       "25     18.978300\n",
       "26     15.661464\n",
       "27     29.185045\n",
       "28     19.024611\n",
       "29     35.120151\n",
       "..           ...\n",
       "470     9.309346\n",
       "471    19.494743\n",
       "472    22.225122\n",
       "473    18.887162\n",
       "474    21.752092\n",
       "475    18.159217\n",
       "476    14.866103\n",
       "477    28.829759\n",
       "478    25.333420\n",
       "479    18.508360\n",
       "480    22.482798\n",
       "481    30.085190\n",
       "482    16.997889\n",
       "483    27.281064\n",
       "484     4.865874\n",
       "485    23.407257\n",
       "486    12.301615\n",
       "487    32.632858\n",
       "488    16.703852\n",
       "489    26.964217\n",
       "490    23.824922\n",
       "491    34.472169\n",
       "492    23.056214\n",
       "493    14.931506\n",
       "494    25.112066\n",
       "495    22.274899\n",
       "496    32.893092\n",
       "497    12.588157\n",
       "498    22.362402\n",
       "499    28.957736\n",
       "\n",
       "[500 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TRAIN THE MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(375, 1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = LinearRegression(fit_intercept =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Model Coefficient (m):  [21.3711441]\n",
      "Linear Model Coefficient (b):  46.45360860093808\n"
     ]
    }
   ],
   "source": [
    "print('Linear Model Coefficient (m): ', regressor.coef_)\n",
    "print('Linear Model Coefficient (b): ', regressor.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TEST THE MODEL "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([664.08377038, 580.48684879, 550.32349334, 474.22664355,\n",
       "       682.63460242, 661.73101642, 391.5083427 , 699.61264605,\n",
       "       388.84018272, 758.43322344, 872.8373359 , 267.14036386,\n",
       "       620.80210057, 640.3695402 , 576.00224157, 231.05917326,\n",
       "       569.90723594, 444.01566486, 911.42906368, 203.22046089,\n",
       "       367.64292172, 379.58831246, 516.82980411, 839.90157886,\n",
       "       609.80809397, 294.25421926, 410.82600794, 447.66547095,\n",
       "       705.41113967, 685.86346963, 576.85876754, 509.77781211,\n",
       "       791.46666264, 939.05340326, 245.40497488, 469.1613441 ,\n",
       "       548.6227529 , 613.43473695, 523.26688632, 208.04349928,\n",
       "       584.82317211, 256.15818384, 599.23841016, 368.04494628,\n",
       "       285.32213099, 701.83384138, 723.26629972, 360.30904476,\n",
       "       394.80592819, 262.23045485, 763.48581284, 309.35319308,\n",
       "       423.37512559, 124.77179021, 450.09386187, 522.49369051,\n",
       "       547.06511898, 803.94411344, 546.6934703 , 558.5891306 ,\n",
       "       415.3661688 , 622.70978613, 409.71794233, 531.12038449,\n",
       "       774.39053913, 339.40454771, 519.87977369, 458.46087957,\n",
       "       570.66325427, 638.8924973 , 530.57703854, 650.4626413 ,\n",
       "       520.27749453, 721.35410485, 385.03711496, 611.84968257,\n",
       "       732.61863957, 473.51035568, 663.12206355, 574.90118869,\n",
       "       907.79123826, 353.3736736 , 200.82544284, 525.12400861,\n",
       "       258.03420258, 539.49906484, 539.86339797, 514.53550579,\n",
       "       670.69863358, 357.42966158, 587.85778118, 378.15859553,\n",
       "       609.30655233, 642.43307333, 575.99206057, 630.15963703,\n",
       "       591.34563653, 285.54877176, 371.96156748, 579.65756719,\n",
       "       469.07116129, 739.0761994 , 614.82034106, 600.50417387,\n",
       "       671.38330263, 467.92102572, 451.4906589 , 474.94902092,\n",
       "       447.70449166, 546.22090173, 446.27514638, 860.62172308,\n",
       "       431.07040548, 605.71206822, 539.19127121, 454.93423709,\n",
       "       332.06427116, 218.13127817, 234.32632784, 890.89493369,\n",
       "       624.94071751, 736.68452944, 666.98056591, 730.42350614,\n",
       "       441.45947813])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = regressor.predict( X_test)\n",
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "461    636.298374\n",
       "452    608.629992\n",
       "261    570.990932\n",
       "415    477.315188\n",
       "239    695.851298\n",
       "75     653.986736\n",
       "205    395.273750\n",
       "123    702.901717\n",
       "80     372.990606\n",
       "308    756.037702\n",
       "460    891.413646\n",
       "54     219.303993\n",
       "181    634.584751\n",
       "2      660.632289\n",
       "170    540.977511\n",
       "145    241.278548\n",
       "302    558.636932\n",
       "221    472.549343\n",
       "76     918.391232\n",
       "396    192.341996\n",
       "171    366.247714\n",
       "107    362.515215\n",
       "183    520.853456\n",
       "456    892.947720\n",
       "457    646.266946\n",
       "463    257.078777\n",
       "39     501.732990\n",
       "69     430.343903\n",
       "7      696.716640\n",
       "237    696.640178\n",
       "          ...    \n",
       "55     623.598861\n",
       "119    596.236690\n",
       "187    293.926393\n",
       "31     356.098007\n",
       "272    607.542148\n",
       "328    458.860905\n",
       "142    739.387272\n",
       "104    627.901841\n",
       "116    572.081292\n",
       "331    678.751388\n",
       "232    448.930443\n",
       "425    450.708589\n",
       "420    473.499631\n",
       "42     421.621505\n",
       "241    506.432135\n",
       "350    436.951311\n",
       "389    819.117588\n",
       "406    441.002944\n",
       "404    658.600456\n",
       "492    552.819351\n",
       "424    445.772400\n",
       "224    268.929179\n",
       "343    249.884252\n",
       "32     237.763911\n",
       "468    898.805423\n",
       "313    594.651009\n",
       "129    751.054570\n",
       "83     685.362388\n",
       "238    675.807151\n",
       "15     469.909033\n",
       "Name: Revenue, Length: 125, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Revenue Generated vs. Temperature @Ice Cream Stand(Training dataset)')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train, y_train, color = 'red')\n",
    "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n",
    "plt.ylabel('Revenue [dollars]')\n",
    "plt.xlabel('Temperature [degC]')\n",
    "plt.title('Revenue Generated vs. Temperature @Ice Cream Stand(Training dataset)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Revenue Generated vs. Hours @Ice Cream Stand(Test dataset)')"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# VISUALIZE TEST SET RESULTS\n",
    "plt.scatter(X_test, y_test, color = 'red')\n",
    "plt.plot(X_test, regressor.predict(X_test), color = 'blue')\n",
    "plt.ylabel('Revenue [dollars]')\n",
    "plt.xlabel('Hours')\n",
    "plt.title('Revenue Generated vs. Hours @Ice Cream Stand(Test dataset)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([687.58793161])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = regressor.predict(30)\n",
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sample_T = 35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([794.44365211])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = regressor.predict(Sample_T)\n",
    "y_predict"
   ]
  }
 ],
 "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": "3.7.3"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}