modelos-machine-learning/notebooks/ML-Wine-Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lendo o arquivo\n",
    "dados = pd.read_csv('wine_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed_acidity</th>\n",
       "      <th>volatile_acidity</th>\n",
       "      <th>citric_acid</th>\n",
       "      <th>residual_sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free_sulfur_dioxide</th>\n",
       "      <th>total_sulfur_dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>style</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed_acidity  volatile_acidity  citric_acid  residual_sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free_sulfur_dioxide  total_sulfur_dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality style  \n",
       "0      9.4        5   red  \n",
       "1      9.8        5   red  \n",
       "2      9.8        5   red  \n",
       "3      9.8        6   red  \n",
       "4      9.4        5   red  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#visualiando as 5 primeiras linhas do DataFrame\n",
    "dados.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['red', 'white'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tipos de vinhos\n",
    "dados['style'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#substituindo valores\n",
    "dados['style'] = dados['style'].replace('red', 0)\n",
    "dados['style'] = dados['style'].replace('white', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "6492    1\n",
       "6493    1\n",
       "6494    1\n",
       "6495    1\n",
       "6496    1\n",
       "Name: style, Length: 6497, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados['style']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#separando as variáveis entre preditoras(X) e variável alvo(y)\n",
    "X = dados.drop('style', axis = 1)\n",
    "y = dados['style']"
   ]
  },
  {
   "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": [
    "#separando os dados de treino e teste\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n",
       "                     criterion='gini', max_depth=None, max_features='auto',\n",
       "                     max_leaf_nodes=None, max_samples=None,\n",
       "                     min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                     min_samples_leaf=1, min_samples_split=2,\n",
       "                     min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                     n_jobs=None, oob_score=False, random_state=None, verbose=0,\n",
       "                     warm_start=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#criação do modelo\n",
    "modelo = ExtraTreesClassifier()\n",
    "modelo.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia: 0.9969230769230769\n"
     ]
    }
   ],
   "source": [
    "#imprimindo os resultados\n",
    "resultado = modelo.score(X_test, y_test)\n",
    "print(f'Acurácia: {resultado}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed_acidity</th>\n",
       "      <th>volatile_acidity</th>\n",
       "      <th>citric_acid</th>\n",
       "      <th>residual_sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free_sulfur_dioxide</th>\n",
       "      <th>total_sulfur_dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1485</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.16</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.074</td>\n",
       "      <td>8.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.99606</td>\n",
       "      <td>3.37</td>\n",
       "      <td>0.55</td>\n",
       "      <td>9.7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2333</th>\n",
       "      <td>6.9</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.25</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0.040</td>\n",
       "      <td>28.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>0.99710</td>\n",
       "      <td>3.28</td>\n",
       "      <td>0.61</td>\n",
       "      <td>9.2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3626</th>\n",
       "      <td>6.9</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.15</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.046</td>\n",
       "      <td>51.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>0.99580</td>\n",
       "      <td>3.13</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3550</th>\n",
       "      <td>9.9</td>\n",
       "      <td>1.005</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.046</td>\n",
       "      <td>34.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>0.99660</td>\n",
       "      <td>3.02</td>\n",
       "      <td>0.49</td>\n",
       "      <td>10.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1370</th>\n",
       "      <td>8.7</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.415</td>\n",
       "      <td>12.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.99623</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.17</td>\n",
       "      <td>9.2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed_acidity  volatile_acidity  citric_acid  residual_sugar  chlorides  \\\n",
       "1485            7.0             0.655         0.16             2.1      0.074   \n",
       "2333            6.9             0.380         0.25             9.8      0.040   \n",
       "3626            6.9             0.320         0.15             8.1      0.046   \n",
       "3550            9.9             1.005         0.46             1.4      0.046   \n",
       "1370            8.7             0.780         0.51             1.7      0.415   \n",
       "\n",
       "      free_sulfur_dioxide  total_sulfur_dioxide  density    pH  sulphates  \\\n",
       "1485                  8.0                  25.0  0.99606  3.37       0.55   \n",
       "2333                 28.0                 191.0  0.99710  3.28       0.61   \n",
       "3626                 51.0                 180.0  0.99580  3.13       0.45   \n",
       "3550                 34.0                 185.0  0.99660  3.02       0.49   \n",
       "1370                 12.0                  66.0  0.99623  3.00       1.17   \n",
       "\n",
       "      alcohol  quality  \n",
       "1485      9.7        5  \n",
       "2333      9.2        5  \n",
       "3626      8.9        5  \n",
       "3550     10.2        4  \n",
       "1370      9.2        5  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#escolhendo 5 linhas dos dados aleatórios de teste\n",
    "X_test[100:105]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1485    0\n",
       "2333    1\n",
       "3626    1\n",
       "3550    1\n",
       "1370    0\n",
       "Name: style, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[100:105]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "previsoes = modelo.predict(X_test[100:105])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(previsoes)"
   ]
  }
 ],
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