regressao_linear_simples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importa todas as bibliotecas necessarias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# Sklearn libraries\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "## Comando para que o grafico plotado apareça logo abaixo do código que o produz\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lê o arquivo de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('test_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exibe o arquivo de dados na tela"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>77</td>\n",
       "      <td>79.775152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>23.177279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>25.609262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>17.857388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>41.849864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>9.805235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>62</td>\n",
       "      <td>58.874659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>95</td>\n",
       "      <td>97.617937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20</td>\n",
       "      <td>18.395127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>8.746748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4</td>\n",
       "      <td>2.811416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19</td>\n",
       "      <td>17.095372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>96</td>\n",
       "      <td>95.149072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>62</td>\n",
       "      <td>61.388007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>36</td>\n",
       "      <td>40.247017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>14.822486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>65</td>\n",
       "      <td>66.958069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>14</td>\n",
       "      <td>16.635080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>87</td>\n",
       "      <td>90.655137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>69</td>\n",
       "      <td>77.229826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>89</td>\n",
       "      <td>92.119063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>51</td>\n",
       "      <td>46.913877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>89</td>\n",
       "      <td>89.826344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>27</td>\n",
       "      <td>21.713803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>97</td>\n",
       "      <td>97.412070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>58</td>\n",
       "      <td>57.016314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>79</td>\n",
       "      <td>78.310565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>21</td>\n",
       "      <td>19.131510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>93</td>\n",
       "      <td>93.034834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>27</td>\n",
       "      <td>26.591124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>55</td>\n",
       "      <td>55.816285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>8</td>\n",
       "      <td>9.391417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>56</td>\n",
       "      <td>56.014481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>72</td>\n",
       "      <td>77.996948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>58</td>\n",
       "      <td>55.370500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>6</td>\n",
       "      <td>11.894578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>96</td>\n",
       "      <td>94.790817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>23</td>\n",
       "      <td>25.690415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>58</td>\n",
       "      <td>53.520423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>23</td>\n",
       "      <td>18.313968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>19</td>\n",
       "      <td>21.426378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>25</td>\n",
       "      <td>30.413033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>64</td>\n",
       "      <td>67.681421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>21</td>\n",
       "      <td>17.085478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>59</td>\n",
       "      <td>60.917927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>19</td>\n",
       "      <td>14.995143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>16</td>\n",
       "      <td>16.749239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>42</td>\n",
       "      <td>41.469239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>43</td>\n",
       "      <td>42.845261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>61</td>\n",
       "      <td>59.129130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>92</td>\n",
       "      <td>91.308637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>11</td>\n",
       "      <td>8.673336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>41</td>\n",
       "      <td>39.314853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>1</td>\n",
       "      <td>5.313686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>8</td>\n",
       "      <td>5.405221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>71</td>\n",
       "      <td>68.545888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>46</td>\n",
       "      <td>47.334876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>55</td>\n",
       "      <td>54.090637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>62</td>\n",
       "      <td>63.297171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>47</td>\n",
       "      <td>52.459467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      x          y\n",
       "0    77  79.775152\n",
       "1    21  23.177279\n",
       "2    22  25.609262\n",
       "3    20  17.857388\n",
       "4    36  41.849864\n",
       "5    15   9.805235\n",
       "6    62  58.874659\n",
       "7    95  97.617937\n",
       "8    20  18.395127\n",
       "9     5   8.746748\n",
       "10    4   2.811416\n",
       "11   19  17.095372\n",
       "12   96  95.149072\n",
       "13   62  61.388007\n",
       "14   36  40.247017\n",
       "15   15  14.822486\n",
       "16   65  66.958069\n",
       "17   14  16.635080\n",
       "18   87  90.655137\n",
       "19   69  77.229826\n",
       "20   89  92.119063\n",
       "21   51  46.913877\n",
       "22   89  89.826344\n",
       "23   27  21.713803\n",
       "24   97  97.412070\n",
       "25   58  57.016314\n",
       "26   79  78.310565\n",
       "27   21  19.131510\n",
       "28   93  93.034834\n",
       "29   27  26.591124\n",
       "..   ..        ...\n",
       "270  55  55.816285\n",
       "271   8   9.391417\n",
       "272  56  56.014481\n",
       "273  72  77.996948\n",
       "274  58  55.370500\n",
       "275   6  11.894578\n",
       "276  96  94.790817\n",
       "277  23  25.690415\n",
       "278  58  53.520423\n",
       "279  23  18.313968\n",
       "280  19  21.426378\n",
       "281  25  30.413033\n",
       "282  64  67.681421\n",
       "283  21  17.085478\n",
       "284  59  60.917927\n",
       "285  19  14.995143\n",
       "286  16  16.749239\n",
       "287  42  41.469239\n",
       "288  43  42.845261\n",
       "289  61  59.129130\n",
       "290  92  91.308637\n",
       "291  11   8.673336\n",
       "292  41  39.314853\n",
       "293   1   5.313686\n",
       "294   8   5.405221\n",
       "295  71  68.545888\n",
       "296  46  47.334876\n",
       "297  55  54.090637\n",
       "298  62  63.297171\n",
       "299  47  52.459467\n",
       "\n",
       "[300 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separa as variáveis independentes e dependentes do problema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:,0].values\n",
    "y = dataset.iloc[:,-1].values\n",
    "X = X.reshape((-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        38.14753871,  34.31141446,  95.28503937,  87.84749912,\n",
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       "        14.05572877,  68.11367147,  75.59701346,  59.225745  ,\n",
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       "        73.9308764 ,  74.45114379,  52.01932286,  83.68820499,\n",
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       "        41.97607107,  12.59244741,   0.27530726,  98.13258005,\n",
       "        87.45721555,  -2.34473854,  39.3294153 ,  16.68715211,\n",
       "        96.58888601,  97.70342201,  67.01715955,  25.63476257,\n",
       "        13.41310757,  95.15647284,   9.74416426,  -3.46788379,\n",
       "        62.82816355,  97.27405461,  95.58017185,   7.46850184,\n",
       "        45.44599591,  46.69013968,  74.4993599 ,  21.63500655,\n",
       "        91.59548851,  26.49487961,  67.38654703,  74.25362837,\n",
       "        12.07991648,  21.32273728,  29.31770045,  26.48713683,\n",
       "        68.94699774,  59.10598995,  64.37521087,  60.20758349,\n",
       "        70.34329706,  97.1082562 ,  75.7584178 ,  10.80462727,\n",
       "        12.11219941,  63.28312382,  98.03017721,  63.19354354,\n",
       "        34.8534823 ,  -2.81991397,  59.8313966 ,  29.38505024,\n",
       "        97.00148372,  85.18657275,  61.74063192,  18.84798163,\n",
       "        78.79008525,  95.12400481,  30.48881287,  10.41468095,\n",
       "        38.98317436,  46.11021062,  52.45103628,  21.16523945,\n",
       "        52.28620611,  44.18863945,  97.13832018,  67.22008001,\n",
       "        18.98322306,  24.3884599 ,  79.44769523,  40.03504862,\n",
       "        53.32005764,  54.55446979,  -2.7611826 ,  37.80182795,\n",
       "        57.48741435,  36.06292994,  49.83538167,  74.68953276,\n",
       "        14.86159401, 101.0697879 ,  99.43577876,  91.69240746,\n",
       "        34.12473248,   6.07939007,  59.07247174,  56.43046022,\n",
       "        30.49412933,  48.35172635,  89.73153611,  72.86282528,\n",
       "        80.97144285,  91.36566374,  60.07137496,  99.87382707,\n",
       "         8.65571417,  69.39858505,  19.38780134,  53.11628433,\n",
       "        78.39683006,  25.75612514,  75.07484683,  92.88772282,\n",
       "        69.45498498,  13.12109842,  48.09843134,  79.3142548 ,\n",
       "        68.48820749,  73.2300846 ,  24.68362712,  41.90368917,\n",
       "        62.22635684,  45.96396877,  23.52647153,  51.80035866,\n",
       "        51.10774273,  95.79747345,   9.24113898,   7.64652976,\n",
       "         9.28169975, 103.5266162 ,  47.41006725,  42.03835773,\n",
       "        96.11982476,  38.05766408, 105.4503788 ,  88.80306911,\n",
       "        15.49301141,  12.42624606,  40.00709598,   5.6340309 ,\n",
       "        87.36938931,  89.73951993,  66.61499643,  72.9138853 ,\n",
       "        57.19103506,  11.21710477,   0.67607675,  28.15668543,\n",
       "        95.3958003 ,  52.05490703,  59.70864577,  36.79224762,\n",
       "        37.08457698,  24.18437976,  67.28725332,  82.870594  ,\n",
       "        89.899991  ,  36.94173178,  19.87562242,  90.71481654,\n",
       "        61.09367762,  60.11134958,  64.83296316,  81.40381769,\n",
       "        92.40217686,   2.57662538,  63.80768172,  38.67780759,\n",
       "        16.82839701,  99.78687252,  44.68913433,  71.00377824,\n",
       "        51.57326718,  19.87846479,  79.50341495,  34.58876491,\n",
       "        55.7383467 ,  68.19721905,  55.81628509,   9.3914168 ,\n",
       "        56.01448111,  77.9969477 ,  55.37049953,  11.89457829,\n",
       "        94.79081712,  25.69041546,  53.52042319,  18.31396758,\n",
       "        21.42637785,  30.41303282,  67.68142149,  17.0854783 ,\n",
       "        60.91792707,  14.99514319,  16.74923937,  41.46923883,\n",
       "        42.84526108,  59.12912974,  91.30863673,   8.67333636,\n",
       "        39.31485292,   5.3136862 ,   5.40522052,  68.5458879 ,\n",
       "        47.33487629,  54.09063686,  63.29717058,  52.45946688])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Treina o regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = LinearRegression()\n",
    "regressor.fit(X, y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualiza o resultado do treinamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 77],\n",
       "       [ 21],\n",
       "       [ 22],\n",
       "       [ 20],\n",
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       "       [ 62],\n",
       "       [ 95],\n",
       "       [ 20],\n",
       "       [  5],\n",
       "       [  4],\n",
       "       [ 19],\n",
       "       [ 96],\n",
       "       [ 62],\n",
       "       [ 36],\n",
       "       [ 15],\n",
       "       [ 65],\n",
       "       [ 14],\n",
       "       [ 87],\n",
       "       [ 69],\n",
       "       [ 89],\n",
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       "       [ 89],\n",
       "       [ 27],\n",
       "       [ 97],\n",
       "       [ 58],\n",
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       "       [ 27],\n",
       "       [ 99],\n",
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       "       [ 80],\n",
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       "       [ 47],\n",
       "       [ 53],\n",
       "       [ 69],\n",
       "       [ 28],\n",
       "       [ 33],\n",
       "       [ 91],\n",
       "       [ 71],\n",
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       "       [ 40],\n",
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       "       [ 31],\n",
       "       [ 59],\n",
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       "       [ 39],\n",
       "       [ 64],\n",
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       "       [ 72],\n",
       "       [ 76],\n",
       "       [ 61],\n",
       "       [ 82],\n",
       "       [ 18],\n",
       "       [ 41],\n",
       "       [ 50],\n",
       "       [ 55],\n",
       "       [ 13],\n",
       "       [ 46],\n",
       "       [ 13],\n",
       "       [ 79],\n",
       "       [ 53],\n",
       "       [ 15],\n",
       "       [ 28],\n",
       "       [ 81],\n",
       "       [ 69],\n",
       "       [ 52],\n",
       "       [ 84],\n",
       "       [ 68],\n",
       "       [ 27],\n",
       "       [ 56],\n",
       "       [ 48],\n",
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       "       [100],\n",
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       "       [ 43],\n",
       "       [ 67],\n",
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       "       [ 63],\n",
       "       [ 91],\n",
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       "       [ 12],\n",
       "       [  1],\n",
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       "       [ 89],\n",
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       "       [ 94],\n",
       "       [ 97],\n",
       "       [ 66],\n",
       "       [ 24],\n",
       "       [ 17],\n",
       "       [ 90],\n",
       "       [ 13],\n",
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       "       [ 98],\n",
       "       [ 12],\n",
       "       [ 41],\n",
       "       [ 47],\n",
       "       [ 78],\n",
       "       [ 20],\n",
       "       [ 89],\n",
       "       [ 29],\n",
       "       [ 64],\n",
       "       [ 75],\n",
       "       [ 12],\n",
       "       [ 25],\n",
       "       [ 28],\n",
       "       [ 30],\n",
       "       [ 65],\n",
       "       [ 59],\n",
       "       [ 64],\n",
       "       [ 53],\n",
       "       [ 71],\n",
       "       [ 97],\n",
       "       [ 73],\n",
       "       [  9],\n",
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       "       [ 63],\n",
       "       [ 99],\n",
       "       [ 60],\n",
       "       [ 35],\n",
       "       [  2],\n",
       "       [ 60],\n",
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       "       [ 84],\n",
       "       [ 63],\n",
       "       [ 22],\n",
       "       [ 81],\n",
       "       [ 93],\n",
       "       [ 33],\n",
       "       [  7],\n",
       "       [ 42],\n",
       "       [ 46],\n",
       "       [ 54],\n",
       "       [ 16],\n",
       "       [ 49],\n",
       "       [ 43],\n",
       "       [ 95],\n",
       "       [ 66],\n",
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       "       [ 35],\n",
       "       [ 80],\n",
       "       [ 37],\n",
       "       [ 54],\n",
       "       [ 56],\n",
       "       [  1],\n",
       "       [ 32],\n",
       "       [ 58],\n",
       "       [ 32],\n",
       "       [ 46],\n",
       "       [ 72],\n",
       "       [ 17],\n",
       "       [ 97],\n",
       "       [ 93],\n",
       "       [ 91],\n",
       "       [ 37],\n",
       "       [  4],\n",
       "       [ 54],\n",
       "       [ 51],\n",
       "       [ 27],\n",
       "       [ 46],\n",
       "       [ 92],\n",
       "       [ 73],\n",
       "       [ 77],\n",
       "       [ 91],\n",
       "       [ 61],\n",
       "       [ 99],\n",
       "       [  4],\n",
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       "       [ 19],\n",
       "       [ 57],\n",
       "       [ 78],\n",
       "       [ 26],\n",
       "       [ 74],\n",
       "       [ 90],\n",
       "       [ 66],\n",
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       "       [ 40],\n",
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       "       [ 67],\n",
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       "       [ 55],\n",
       "       [ 55],\n",
       "       [ 95],\n",
       "       [ 12],\n",
       "       [  4],\n",
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       "       [100],\n",
       "       [ 48],\n",
       "       [ 42],\n",
       "       [ 96],\n",
       "       [ 39],\n",
       "       [100],\n",
       "       [ 87],\n",
       "       [ 14],\n",
       "       [ 14],\n",
       "       [ 37],\n",
       "       [  5],\n",
       "       [ 88],\n",
       "       [ 91],\n",
       "       [ 65],\n",
       "       [ 74],\n",
       "       [ 56],\n",
       "       [ 16],\n",
       "       [  5],\n",
       "       [ 28],\n",
       "       [ 92],\n",
       "       [ 46],\n",
       "       [ 54],\n",
       "       [ 39],\n",
       "       [ 44],\n",
       "       [ 31],\n",
       "       [ 68],\n",
       "       [ 86],\n",
       "       [ 90],\n",
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       "       [ 21],\n",
       "       [ 95],\n",
       "       [ 56],\n",
       "       [ 60],\n",
       "       [ 65],\n",
       "       [ 78],\n",
       "       [ 89],\n",
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       "       [ 36],\n",
       "       [ 16],\n",
       "       [100],\n",
       "       [ 45],\n",
       "       [ 73],\n",
       "       [ 57],\n",
       "       [ 20],\n",
       "       [ 76],\n",
       "       [ 34],\n",
       "       [ 55],\n",
       "       [ 72],\n",
       "       [ 55],\n",
       "       [  8],\n",
       "       [ 56],\n",
       "       [ 72],\n",
       "       [ 58],\n",
       "       [  6],\n",
       "       [ 96],\n",
       "       [ 23],\n",
       "       [ 58],\n",
       "       [ 23],\n",
       "       [ 19],\n",
       "       [ 25],\n",
       "       [ 64],\n",
       "       [ 21],\n",
       "       [ 59],\n",
       "       [ 19],\n",
       "       [ 16],\n",
       "       [ 42],\n",
       "       [ 43],\n",
       "       [ 61],\n",
       "       [ 92],\n",
       "       [ 11],\n",
       "       [ 41],\n",
       "       [  1],\n",
       "       [  8],\n",
       "       [ 71],\n",
       "       [ 46],\n",
       "       [ 55],\n",
       "       [ 62],\n",
       "       [ 47]], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "plt.scatter(X, y, color = 'orange', s=60)\n",
    "plt.plot(X, regressor.predict(X), color = 'purple')\n",
    "plt.title('Gráfio X vs Y')\n",
    "plt.xlabel('Eixo X')\n",
    "plt.ylabel('Eixo Y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}