{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c49b5055-cb25-4563-966d-430aebe0f434",
   "metadata": {},
   "source": [
    "# Webshop - Spend data\n",
    "Data from a webshop, with purchases distributed on the different age groups, from a webshop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "efeae42d-4556-483c-99ae-06e7f81216a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Module\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a0abee4-5bb6-4188-9613-94b9755bd8c7",
   "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>Age</th>\n",
       "      <th>Spend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>10070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>13278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>11468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>15234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>19482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Spend\n",
       "0   17  10070\n",
       "1   17  13278\n",
       "2   17  11468\n",
       "3   17  15234\n",
       "4   18  19482"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import data\n",
    "df = pd.read_excel('spend_age.xlsx', skiprows=2)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "672cbad3-ee2e-40ce-a6f5-a24e0d673cdb",
   "metadata": {},
   "source": [
    "# Spend pr. age group - MEAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "070be659-f98c-4c96-baff-135b146069c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Age</th>\n",
       "      <th>Spend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>12512.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>17790.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>20788.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>23932.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>27676.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>22</td>\n",
       "      <td>32052.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23</td>\n",
       "      <td>34245.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24</td>\n",
       "      <td>39489.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>25</td>\n",
       "      <td>37243.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>26</td>\n",
       "      <td>39468.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>27</td>\n",
       "      <td>41980.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>28</td>\n",
       "      <td>39081.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>29</td>\n",
       "      <td>41279.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30</td>\n",
       "      <td>41053.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age         Spend\n",
       "0    17  12512.500000\n",
       "1    18  17790.888889\n",
       "2    19  20788.916667\n",
       "3    20  23932.700000\n",
       "4    21  27676.416667\n",
       "5    22  32052.142857\n",
       "6    23  34245.428571\n",
       "7    24  39489.428571\n",
       "8    25  37243.727273\n",
       "9    26  39468.538462\n",
       "10   27  41980.500000\n",
       "11   28  39081.727273\n",
       "12   29  41279.666667\n",
       "13   30  41053.100000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_age_spend = df[['Age', 'Spend']].groupby(['Age'], as_index=False).mean().sort_values(by='Age')\n",
    "group_age_spend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5ae59ceb-adea-49a6-a0c3-8d912a0805e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tFG/LWIekhaX4REkbMn5d3vHMzMwapJ49g33AORExGTgJWCBpcrYtjogpOawGyLbZwGuBNuBSScPyTmmXANOBycAZpeVclMs6HtgDzOun7TMzszr0WAwiYmdE/CLHn6K4//HYbmaZCSyPiGcj4iGgg+KOaFOBjojYEhHPAcuBmZIETAOuz/mXAbN6uT1mZtYL+3XOQNIEiltgbsjQ2ZI2SVoqaVTGxgLbSrNtz1it+LHA3ojYVxGvtv75ktoltXd2du5P6mZm1o26i4GkI4AfAJ+OiCeBy4BXAFOAncA/DUSCZRGxJCJaI6K1paVloFdnZnbAqOsnrCUdTFEIromIGwAi4rFS+7eBG3NyBzC+NPu4jFEj/jgwUtLw3Dso9zczswao52oiAVcA90XEt0rxMaVu7wXuyfFVwGxJh0iaCEwCbgNuBybllUMjKE4yr4qIANYBp+f8c4GVfdssMzPbH/XsGbwZ+CBwt6SNGfsCxdVAU4AAtgIfBYiIzZJWAPdSXIm0ICKeB5B0NrAGGAYsjYjNubzPA8slfRW4k6L4mJlZg/RYDCLi54CqNK3uZp5FwKIq8dXV5ouILRRXG5mZWRP4tpcHMN+e0sy6+OcozMzMxcDMzFwMzMwMFwMzM8PFwMzMcDEwMzNcDMzMDBcDMzPDxcDMzHAxMDMzXAzMzAwXAzMzw8XAzMxwMTAzM/wT1kOCf2razAZaPbe9HC9pnaR7JW2W9KmMHyNpraQH8u+ojEvSxZI6JG2SdEJpWXOz/wOS5pbib5R0d85zcd5q08zMGqSew0T7gHMiYjJwErBA0mRgIXBLREwCbslpgOkU9z2eBMwHLoOieADnASdS3NXsvK4Ckn0+Upqvre+bZmZm9eqxGETEzoj4RY4/BdwHjAVmAsuy2zJgVo7PBK6KwnpgpKQxwGnA2ojYHRF7gLVAW7YdFRHrIyKAq0rLMjOzBtivE8iSJgBvADYAx0XEzmx6FDgux8cC20qzbc9Yd/HtVeLV1j9fUruk9s7Ozv1J3czMulF3MZB0BPAD4NMR8WS5LT/RRz/n9gIRsSQiWiOitaWlZaBXZ2Z2wKirGEg6mKIQXBMRN2T4sTzEQ/7dlfEdwPjS7OMy1l18XJW4mZk1SD1XEwm4ArgvIr5ValoFdF0RNBdYWYrPyauKTgKeyMNJa4BTJY3KE8enAmuy7UlJJ+W65pSWZWZmDVDP9wzeDHwQuFvSxox9AbgQWCFpHvAw8L5sWw3MADqAZ4CzACJit6QLgNuz3/kRsTvHPwFcCRwG3JSDmZk1SI/FICJ+DtS67v+UKv0DWFBjWUuBpVXi7cDresrFzMwGhn+OwszMXAzMzMzFwMzMcDEwMzNcDMzMDP+Edb/yT02b2VDlPQMzM3MxMDMzFwMzM8PFwMzMcDEwMzNcDMzMDBcDMzPDxcDMzHAxMDMzXAzMzIz6bnu5VNIuSfeUYl+WtEPSxhxmlNrOldQh6X5Jp5XibRnrkLSwFJ8oaUPGr5M0oj830MzMelbPnsGVQFuV+OKImJLDagBJk4HZwGtznkslDZM0DLgEmA5MBs7IvgAX5bKOB/YA8/qyQWZmtv96LAYR8TNgd0/90kxgeUQ8GxEPUdwHeWoOHRGxJSKeA5YDMyUJmAZcn/MvA2bt3yaYmVlf9eWcwdmSNuVhpFEZGwtsK/XZnrFa8WOBvRGxryJelaT5ktoltXd2dvYhdTMzK+ttMbgMeAUwBdgJ/FN/JdSdiFgSEa0R0drS0tKIVZqZHRB6dT+DiHisa1zSt4Ebc3IHML7UdVzGqBF/HBgpaXjuHZT7m5lZg/Rqz0DSmNLke4GuK41WAbMlHSJpIjAJuA24HZiUVw6NoDjJvCoiAlgHnJ7zzwVW9iYnMzPrvR73DCRdC5wMjJa0HTgPOFnSFCCArcBHASJis6QVwL3APmBBRDyfyzkbWAMMA5ZGxOZcxeeB5ZK+CtwJXNFfG2dmZvXpsRhExBlVwjXfsCNiEbCoSnw1sLpKfAvF1UZmZtYk/gaymZm5GJiZmYuBmZnhYmBmZrgYmJkZLgZmZkYvv4E81E1Y+KN+Wc7WC9/ZL8sxM2s27xmYmZmLgZmZuRiYmRkuBmZmhouBmZnhYmBmZrgYmJkZLgZmZkYdxSBveL9L0j2l2DGS1kp6IP+OyrgkXSypQ9ImSSeU5pmb/R+QNLcUf6Oku3OeiyWpvzfSzMy6V8+ewZVAW0VsIXBLREwCbslpgOkUt7qcBMwHLoOieFDcIe1EihvZnNdVQLLPR0rzVa7LzMwGWI/FICJ+BuyuCM8EluX4MmBWKX5VFNZT3Ox+DHAasDYidkfEHmAt0JZtR0XE+rwf8lWlZZmZWYP09pzBcRGxM8cfBY7L8bHAtlK/7RnrLr69SrwqSfMltUtq7+zs7GXqZmZWqc8nkPMTffRDLvWsa0lEtEZEa0tLSyNWaWZ2QOhtMXgsD/GQf3dlfAcwvtRvXMa6i4+rEjczswbqbTFYBXRdETQXWFmKz8mrik4CnsjDSWuAUyWNyhPHpwJrsu1JSSflVURzSssyM7MG6fF+BpKuBU4GRkvaTnFV0IXACknzgIeB92X31cAMoAN4BjgLICJ2S7oAuD37nR8RXSelP0FxxdJhwE05mJlZA/VYDCLijBpNp1TpG8CCGstZCiytEm8HXtdTHmZmNnD8DWQzM3MxMDMzFwMzM8PFwMzMcDEwMzNcDMzMDBcDMzPDxcDMzHAxMDMzXAzMzAwXAzMzw8XAzMxwMTAzM1wMzMwMFwMzM8PFwMzM6GMxkLRV0t2SNkpqz9gxktZKeiD/jsq4JF0sqUPSJkknlJYzN/s/IGlurfWZmdnA6I89g7dFxJSIaM3phcAtETEJuCWnAaYDk3KYD1wGRfGguJXmicBU4LyuAmJmZo0xEIeJZgLLcnwZMKsUvyoK64GRksYApwFrI2J3ROwB1gJtA5CXmZnV0NdiEMDNku6QND9jx0XEzhx/FDgux8cC20rzbs9YrfgLSJovqV1Se2dnZx9TNzOzLsP7OP9bImKHpBcDayX9stwYESEp+riO8vKWAEsAWltb+225ZmYHuj7tGUTEjvy7C/ghxTH/x/LwD/l3V3bfAYwvzT4uY7XiZmbWIL0uBpJeJOnIrnHgVOAeYBXQdUXQXGBljq8C5uRVRScBT+ThpDXAqZJG5YnjUzNmZmYN0pfDRMcBP5TUtZzvRcSPJd0OrJA0D3gYeF/2Xw3MADqAZ4CzACJit6QLgNuz3/kRsbsPeZmZ2X7qdTGIiC3An1WJPw6cUiUewIIay1oKLO1tLmZm1jf+BrKZmbkYmJmZi4GZmeFiYGZmuBiYmRkuBmZmhouBmZnhYmBmZrgYmJkZLgZmZoaLgZmZ4WJgZma4GJiZGS4GZmaGi4GZmeFiYGZmDKJiIKlN0v2SOiQtbHY+ZmYHkkFRDCQNAy4BpgOTgTMkTW5uVmZmB45BUQyAqUBHRGyJiOeA5cDMJudkZnbAUHFr4iYnIZ0OtEXEh3P6g8CJEXF2Rb/5wPycfBVwPzAa+FUD0+0r5zuwnO/Acr4DqxH5viwiWiqDwwd4pf0qIpYAS8oxSe0R0dqklPab8x1YzndgOd+B1cx8B8thoh3A+NL0uIyZmVkDDJZicDswSdJESSOA2cCqJudkZnbAGBSHiSJin6SzgTXAMGBpRGyuc/YlPXcZVJzvwHK+A8v5Dqym5TsoTiCbmVlzDZbDRGZm1kQuBmZmNnSKgaSlknZJuqci/klJv5S0WdLXm5VfNdVyljRF0npJGyW1S5razBy7SBovaZ2ke/Ox/FTGj5G0VtID+XdUs3OFbvP9Rv4/bJL0Q0kjm5zq79XKudR+jqSQNLpZOZZ1l+9gfN118z8xWF9zh0q6TdJdme9XMj5R0ob8aZ7r8qKagRcRQ2IA/hI4AbinFHsb8BPgkJx+cbPzrCPnm4HpOT4DuLXZeWYuY4ATcvxI4P9S/DTI14GFGV8IXNTsXHvI91RgeMYvGiz5dpdzTo+nuIDiYWB0s3Pt4TEelK+7bvIdrK85AUfk+MHABuAkYAUwO+OXAx9vRD5DZs8gIn4G7K4Ifxy4MCKezT67Gp5YN2rkHMBROX408EhDk6ohInZGxC9y/CngPmAsxc+CLMtuy4BZTUmwQq18I+LmiNiX3dZTfGdlUOjmMQZYDHyO4v9jUOgm30H5uusm38H6mouIeDonD84hgGnA9Rlv2GtuyBSDGl4J/EXuUv1PSX/e7ITq8GngG5K2Ad8Ezm1uOi8kaQLwBopPKsdFxM5sehQ4rll51VKRb9mHgJsanlAdyjlLmgnsiIi7mptVbRWP8aB/3VXk+2kG6WtO0jBJG4FdwFrgQWBv6QPNdv7wgWFADfViMBw4hmLX6rPACklqbko9+jjwdxExHvg74Iom5/NHJB0B/AD4dEQ8WW6LYr910Hxyhdr5SvoHYB9wTbNyq6WcM0WOXwC+1MyculPlMR7Ur7sq+Q7a11xEPB8RUyj2YKcCr25WLkO9GGwHbsjdrduA31H80NNgNhe4Ice/T/EPMChIOpjiRXRNRHTl+JikMdk+huITzKBQI18knQm8C/hAFrBBo0rOrwAmAndJ2krxpvALSX/SvCz/oMZjPGhfdzXyHbSvuS4RsRdYB7wJGCmp6wvBDftpnqFeDP6N4mQWkl4JjGDw/0LhI8Bbc3wa8EATc/m9/GR3BXBfRHyr1LSK4sVE/l3Z6NyqqZWvpDaKY+/viYhnmpVfNdVyjoi7I+LFETEhIiZQvNGeEBGPNjFVoNv/iX9jEL7uusl3sL7mWrqudpN0GPAOivMc64DTs1vjXnPNPJu+PwNwLbAT+C3FC2YexT/hd4F7gF8A05qdZx05vwW4A7iL4njmG5udZ+b6FopDQJuAjTnMAI4FbqF4Af0EOKbZufaQbwewrRS7vNm59pRzRZ+tDJ6riWo9xoPydddNvoP1Nfd64M7M9x7gSxl/OXBb/i9/n7xqa6AH/xyFmZkN+cNEZmbWD1wMzMzMxcDMzFwMzMwMFwMzM8PFwMzMcDEwMzPg/wM30Fsd9uW60gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Mean spend pr. age group')\n",
    "plt.bar(x='Age', height='Spend', data=group_age_spend)\n",
    "plt.savefig(\"./image/spend_3.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25e81c8a-4aea-4927-8ec5-969b7bf801a3",
   "metadata": {},
   "source": [
    "# Data Preprocessing\n",
    "Now that we have imported the dataset, we will perform data preprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "9e8485cf-7733-4a0f-829f-d28dd4194f28",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,:-1].values  # Independent variable array\n",
    "y = df.iloc[:,1].values    # Dependent variable vector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c87bbe5-42de-4181-b5d1-6dd66d855f41",
   "metadata": {},
   "source": [
    "# Splitting the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f5ecaa22-ae4d-424e-a294-dc34578ea3ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=1/3,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdc15e9d-7ce9-4e11-a83d-10172cf16c32",
   "metadata": {},
   "source": [
    "# Fitting linear regression model into the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "748273c6-8ef6-4f68-9c64-c3dd154acce5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train) # Produces the linear eqn for the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c62e817f-4400-408b-83bc-3f987f823f5d",
   "metadata": {},
   "source": [
    "# Predicting the test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "337819b1-a216-46db-8ff1-d600e5e9abe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25461.22770811, 44053.17263994, 31658.54268538, 37855.85766266,\n",
       "       23395.45604901, 21329.68438992, 27526.9993672 , 37855.85766266,\n",
       "       37855.85766266, 29592.77102629, 33724.31434447, 35790.08600357,\n",
       "       33724.31434447, 44053.17263994, 37855.85766266, 27526.9993672 ,\n",
       "       25461.22770811, 37855.85766266, 31658.54268538, 39921.62932175,\n",
       "       29592.77102629, 21329.68438992, 44053.17263994, 33724.31434447,\n",
       "       27526.9993672 , 35790.08600357, 44053.17263994, 29592.77102629,\n",
       "       35790.08600357, 29592.77102629, 23395.45604901, 41987.40098084,\n",
       "       37855.85766266, 35790.08600357, 23395.45604901, 31658.54268538,\n",
       "       35790.08600357, 44053.17263994, 25461.22770811, 46118.94429903,\n",
       "       46118.94429903, 27526.9993672 , 23395.45604901, 29592.77102629,\n",
       "       25461.22770811, 46118.94429903, 31658.54268538, 19263.91273083,\n",
       "       44053.17263994, 27526.9993672 , 21329.68438992])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = regressor.predict(X_test) \n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "9d2862a3-dc06-4863-9fcc-2608d465038a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([26406, 49005, 32489, 36158, 20050, 18393, 30071, 38745, 42642,\n",
       "       28975, 39254, 39897, 38734, 49919, 36548, 24514, 22712, 41263,\n",
       "       34656, 47833, 33351, 14198, 33910, 39666, 29684, 39849, 38680,\n",
       "       30793, 37845, 33147, 18255, 39082, 42891, 33357, 21503, 35738,\n",
       "       33427, 45351, 26334, 44254, 33655, 29463, 22794, 34693, 22134,\n",
       "       47623, 31656, 11468, 32666, 25676, 19044], dtype=int64)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1b68452-54ed-49d9-8683-cdca10fa0835",
   "metadata": {},
   "source": [
    "- y_test is the real salary of the test set.\n",
    "- y_pred are the predicted salaries."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0823a81e-d6ff-40a0-ac10-7244c908d165",
   "metadata": {},
   "source": [
    "# Visualizing the results\n",
    "Let’s see what the results of our code will look like when we visualize it.\n",
    "\n",
    "- Plotting the points (observations)\n",
    "- Plotting the regression line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "0a0e3eb3-402c-42f0-a4f0-0d2e3a226b76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for the Training set\n",
    "plt.scatter(X_train, y_train, color='red') # Plotting the observation \n",
    "plt.plot(X_train, regressor.predict(X_train), color='blue') # Plotting the regression line\n",
    "plt.title('Age vs. spend (Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Spend')\n",
    "plt.savefig(\"./image/spend_4.jpg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "c03f74cd-293b-4b3b-89a4-533f80c719ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for the Testing set\n",
    "plt.scatter(X_test, y_test, color='red') # Plotting the observation \n",
    "plt.plot(X_train, regressor.predict(X_train), color='blue') # Plotting the regression line\n",
    "plt.title('Age vs. spend (Testing sett)')\n",
    "plt.xlabel('Age') \n",
    "plt.ylabel('Spend') \n",
    "plt.savefig(\"./image/spend_5.jpg\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95902b9d-ebfe-4726-9a1e-87ab258aa80a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}