ML-Lista05.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lista-05 Aprendizagem de Máquina"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelo de predição de preços de imóveis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### O modelo pega as informações obtidas pelo dataset [Boston House Price Dataset]( https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data) e utiliza o algoritmo de Regressão Linear para a predição."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descrição das colunas\n",
    "\n",
    "|Nome da Coluna | Descição |\n",
    "|---------------|----------|\n",
    "|CRIM|taxa de criminalidade per capita por cidade|\n",
    "|ZN|proporção de terrenos residenciais zoneada para lotes com mais de 25.000 pés quadrados|\n",
    "|INDUS|proporção de acres de negócios não varejistas por cidade|\n",
    "|CHAS|Variável fictícia de Charles River (= 1 se o setor contiver rio; 0 caso contrário)|\n",
    "|NOX|concentração de óxidos nítricos (partes por 10 milhões)|\n",
    "|RM |número médio de quartos por habitação|\n",
    "|AGE|proporção de unidades ocupadas pelo proprietário construídas antes de 1940|\n",
    "|DIS|distâncias ponderadas para cinco centros de emprego de Boston|\n",
    "|RAD|índice de acessibilidade a rodovias radiais|\n",
    "|TAX|taxa de imposto predial de valor integral por US $ 10.000|\n",
    "|PTRATIO|relação aluno-professor por cidade|\n",
    "|B| 1000 (Bk - 0,63)² onde Bk é a proporção de negros por cidade|\n",
    "|LSTAT|menor estado da população|\n",
    "|MEDV|Valor mediano de residências ocupadas pelo proprietário em 1000 dolares|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "import matplotlib.pyplot as pplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "colunas = ['CRIM','ZN','INDUS','CHAS','NOX','RM','AGE','DIS','RAD','TAX','PTRATIO','B','LSTAT','MEDV']\n",
    "df = pd.read_csv('housing.data', sep=\"\\s+\",names=colunas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análise dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      "CRIM       506 non-null float64\n",
      "ZN         506 non-null float64\n",
      "INDUS      506 non-null int64\n",
      "CHAS       506 non-null int64\n",
      "NOX        506 non-null float64\n",
      "RM         506 non-null float64\n",
      "AGE        506 non-null float64\n",
      "DIS        506 non-null float64\n",
      "RAD        506 non-null int64\n",
      "TAX        506 non-null float64\n",
      "PTRATIO    506 non-null float64\n",
      "B          506 non-null float64\n",
      "LSTAT      506 non-null float64\n",
      "MEDV       506 non-null float64\n",
      "dtypes: float64(11), int64(3)\n",
      "memory usage: 55.4 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
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       "      <th>TAX</th>\n",
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       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2310</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7070</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7070</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2180</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2180</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2310     0  0.538  6.575  65.2  4.0900    1  296.0   \n",
       "1  0.02731   0.0   7070     0  0.469  6.421  78.9  4.9671    2  242.0   \n",
       "2  0.02729   0.0   7070     0  0.469  7.185  61.1  4.9671    2  242.0   \n",
       "3  0.03237   0.0   2180     0  0.458  6.998  45.8  6.0622    3  222.0   \n",
       "4  0.06905   0.0   2180     0  0.458  7.147  54.2  6.0622    3  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  MEDV  \n",
       "0     15.3  396.90   4.98  24.0  \n",
       "1     17.8  396.90   9.14  21.6  \n",
       "2     17.8  392.83   4.03  34.7  \n",
       "3     18.7  394.63   2.94  33.4  \n",
       "4     18.7  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
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       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
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       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11930</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</td>\n",
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       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11930</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.6</td>\n",
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       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11930</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "      <td>23.9</td>\n",
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       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11930</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "      <td>22.0</td>\n",
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       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11930</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
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      ],
      "text/plain": [
       "        CRIM   ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "501  0.06263  0.0  11930     0  0.573  6.593  69.1  2.4786    1  273.0   \n",
       "502  0.04527  0.0  11930     0  0.573  6.120  76.7  2.2875    1  273.0   \n",
       "503  0.06076  0.0  11930     0  0.573  6.976  91.0  2.1675    1  273.0   \n",
       "504  0.10959  0.0  11930     0  0.573  6.794  89.3  2.3889    1  273.0   \n",
       "505  0.04741  0.0  11930     0  0.573  6.030  80.8  2.5050    1  273.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  MEDV  \n",
       "501     21.0  391.99   9.67  22.4  \n",
       "502     21.0  396.90   9.08  20.6  \n",
       "503     21.0  396.90   5.64  23.9  \n",
       "504     21.0  393.45   6.48  22.0  \n",
       "505     21.0  396.90   7.88  11.9  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11136.778656</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6860.352941</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>460.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5190.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9690.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677082</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27740.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN         INDUS        CHAS         NOX  \\\n",
       "count  506.000000  506.000000    506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.363636  11136.778656    0.069170    0.554695   \n",
       "std      8.601545   23.322453   6860.352941    0.253994    0.115878   \n",
       "min      0.006320    0.000000    460.000000    0.000000    0.385000   \n",
       "25%      0.082045    0.000000   5190.000000    0.000000    0.449000   \n",
       "50%      0.256510    0.000000   9690.000000    0.000000    0.538000   \n",
       "75%      3.677082   12.500000  18100.000000    0.000000    0.624000   \n",
       "max     88.976200  100.000000  27740.000000    1.000000    0.871000   \n",
       "\n",
       "               RM         AGE         DIS         RAD         TAX     PTRATIO  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     6.284634   68.574901    3.795043    9.549407  408.237154   18.455534   \n",
       "std      0.702617   28.148861    2.105710    8.707259  168.537116    2.164946   \n",
       "min      3.561000    2.900000    1.129600    1.000000  187.000000   12.600000   \n",
       "25%      5.885500   45.025000    2.100175    4.000000  279.000000   17.400000   \n",
       "50%      6.208500   77.500000    3.207450    5.000000  330.000000   19.050000   \n",
       "75%      6.623500   94.075000    5.188425   24.000000  666.000000   20.200000   \n",
       "max      8.780000  100.000000   12.126500   24.000000  711.000000   22.000000   \n",
       "\n",
       "                B       LSTAT        MEDV  \n",
       "count  506.000000  506.000000  506.000000  \n",
       "mean   356.674032   12.653063   22.532806  \n",
       "std     91.294864    7.141062    9.197104  \n",
       "min      0.320000    1.730000    5.000000  \n",
       "25%    375.377500    6.950000   17.025000  \n",
       "50%    391.440000   11.360000   21.200000  \n",
       "75%    396.225000   16.955000   25.000000  \n",
       "max    396.900000   37.970000   50.000000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dividindo os dados de treino e de teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['RM']].values\n",
    "y = df['MEDV'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
       "       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
       "       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
       "       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
       "       21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
       "       35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
       "       19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
       "       20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
       "       23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
       "       33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
       "       21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
       "       20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
       "       23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
       "       15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
       "       17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
       "       25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
       "       23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
       "       32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
       "       34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
       "       20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
       "       26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
       "       31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
       "       22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
       "       42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
       "       36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
       "       32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
       "       20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
       "       20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
       "       22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
       "       21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
       "       19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
       "       32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
       "       18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
       "       16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
       "       13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
       "        7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
       "       12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
       "       27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
       "        8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
       "        9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
       "       10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
       "       15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
       "       19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
       "       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
       "       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
       "       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standartização"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc_x = StandardScaler()\n",
    "sc_y = StandardScaler()\n",
    "X_std = sc_x.fit_transform(X)\n",
    "y_std = sc_y.fit_transform(y[:, np.newaxis]).flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribuindo os dados de treino e teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_std, y_std, test_size = 0.3, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Funções Auxiliares"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Média quadrática"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmse(predictions, targets):\n",
    "    differences = predictions - targets\n",
    "    differences_squared = differences ** 2\n",
    "    mean_of_differences_squared = differences_squared.mean()\n",
    "    rmse_val = np.sqrt(mean_of_differences_squared)\n",
    "    return rmse_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotando o Gráfico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lin_regplot(X, y, model):\n",
    "    pplot.scatter(X, y, c='steelblue', edgecolor='white', s=70)\n",
    "    pplot.plot(X, model.predict(X), color='black', lw=2)\n",
    "    return None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Auxiliar para a função de plotar o gráfico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph_plot(X_test, y_test, model):\n",
    "    lin_regplot(X_test, y_test, model)\n",
    "    pplot.xlabel('Média por número de quartos')\n",
    "    pplot.ylabel('Preço em 1000 dolares')\n",
    "    pplot.show()\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Algoritmo 1 - Regressão Linear com Gradiente Descendente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearRegressionGD(object):\n",
    "\n",
    "    def __init__(self, eta=0.001, n_iter=20):\n",
    "        self.eta = eta\n",
    "        self.n_iter = n_iter\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        self.w_ = np.zeros(1 + X.shape[1])\n",
    "        self.cost_ = []\n",
    "\n",
    "        for i in range(self.n_iter):\n",
    "            output = self.net_input(X)\n",
    "            errors = (y - output)\n",
    "            self.w_[1:] += self.eta * X.T.dot(errors)\n",
    "            self.w_[0] += self.eta * errors.sum()\n",
    "            cost = (errors**2).sum() / 2.0\n",
    "            self.cost_.append(cost)\n",
    "        return self\n",
    "\n",
    "    def net_input(self, X):\n",
    "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.net_input(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = LinearRegressionGD(eta = 0.0009, n_iter = 500)\n",
    "model1.fit(X_train, y_train)\n",
    "pred_y1 = model1.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Algoritmo 2 - Regressão Linear do Sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = LinearRegression()\n",
    "model2.fit(X_train, y_train)\n",
    "pred_y2 = model2.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Algoritmo 3 - Regressão Linear com Gradiente Descendente Estocrástico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wendel/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:152: DeprecationWarning: n_iter parameter is deprecated in 0.19 and will be removed in 0.21. Use max_iter and tol instead.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "model3 = SGDRegressor(eta0=0.0009, n_iter=500)\n",
    "model3.fit(X_train, y_train)\n",
    "pred_y3 = model3.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparação dos Gráficos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "graph_plot(X_test,y_test,model1) #Algoritmo 1\n",
    "graph_plot(X_test,y_test,model2) #Algoritmo 2\n",
    "graph_plot(X_test,y_test,model3) #Algoritmo 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparação das médias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7369008581036092 \n",
      " 0.6913660331691844 \n",
      " 0.6913683975283615\n"
     ]
    }
   ],
   "source": [
    "print(  rmse(pred_y, y_test),\"\\n\", #Algoritmo 1\n",
    "        rmse(pred_y2, y_test),\"\\n\", #Algoritmo 2\n",
    "        rmse(pred_y3, y_test) #Algoritmo 3\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "241-0 48827-5"
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}