wine-classification-with-ml.ipynb

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      "/kaggle/input/wine-dataset/wine_dataset.csv\n"
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    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
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   "source": [
    "arquivo = pd.read_csv('/kaggle/input/wine-dataset/wine_dataset.csv')"
   ]
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       "      <th>fixed_acidity</th>\n",
       "      <th>volatile_acidity</th>\n",
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       "   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  "
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    "arquivo.head()"
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    "Nossa variável 'target', isto é, a variável que desejamos prever, é a coluna _style_, que identifica se o vinho é tinto (red) ou branco (white). Iremos substituir esses valores por 0 e 1."
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   "source": [
    "arquivo['style'] = arquivo['style'].replace('red', 0)\n",
    "arquivo['style'] = arquivo['style'].replace('white', 1)"
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   "source": [
    "Verificando o código acima:"
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       "      <td>8.0</td>\n",
       "      <td>0.047</td>\n",
       "      <td>57.0</td>\n",
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       "      <td>0.99490</td>\n",
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       "      <td>9.6</td>\n",
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      "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",
       "6492            6.2              0.21         0.29             1.6      0.039   \n",
       "6493            6.6              0.32         0.36             8.0      0.047   \n",
       "6494            6.5              0.24         0.19             1.2      0.041   \n",
       "6495            5.5              0.29         0.30             1.1      0.022   \n",
       "6496            6.0              0.21         0.38             0.8      0.020   \n",
       "\n",
       "      free_sulfur_dioxide  total_sulfur_dioxide  density    pH  sulphates  \\\n",
       "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
       "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
       "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
       "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "...                   ...                   ...      ...   ...        ...   \n",
       "6492                 24.0                  92.0  0.99114  3.27       0.50   \n",
       "6493                 57.0                 168.0  0.99490  3.15       0.46   \n",
       "6494                 30.0                 111.0  0.99254  2.99       0.46   \n",
       "6495                 20.0                 110.0  0.98869  3.34       0.38   \n",
       "6496                 22.0                  98.0  0.98941  3.26       0.32   \n",
       "\n",
       "      alcohol  quality  style  \n",
       "0         9.4        5      0  \n",
       "1         9.8        5      0  \n",
       "2         9.8        5      0  \n",
       "3         9.8        6      0  \n",
       "4         9.4        5      0  \n",
       "...       ...      ...    ...  \n",
       "6492     11.2        6      1  \n",
       "6493      9.6        5      1  \n",
       "6494      9.4        6      1  \n",
       "6495     12.8        7      1  \n",
       "6496     11.8        6      1  \n",
       "\n",
       "[6497 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arquivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3a83a1e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T17:36:45.162599Z",
     "iopub.status.busy": "2022-01-10T17:36:45.157820Z",
     "iopub.status.idle": "2022-01-10T17:36:45.164363Z",
     "shell.execute_reply": "2022-01-10T17:36:45.164990Z",
     "shell.execute_reply.started": "2022-01-10T17:19:04.147236Z"
    },
    "papermill": {
     "duration": 0.026866,
     "end_time": "2022-01-10T17:36:45.165164",
     "exception": false,
     "start_time": "2022-01-10T17:36:45.138298",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Separando as variáveis entre preditoras e variável alvo\n",
    "y = arquivo['style'] # variável alvo\n",
    "x = arquivo.drop('style', axis = 1) #armazena todo o restante na variável x (variáveis preditoras)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5a8f40ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T17:36:45.192658Z",
     "iopub.status.busy": "2022-01-10T17:36:45.191945Z",
     "iopub.status.idle": "2022-01-10T17:36:46.286912Z",
     "shell.execute_reply": "2022-01-10T17:36:46.286201Z",
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    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Criando conjuntos de dados de treino e teste:\n",
    "x_treino, x_teste, y_treino, y_teste = train_test_split (x, y, test_size = 0.3) #test_size: quantos % para treino/teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cf64bd4f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T17:36:46.314262Z",
     "iopub.status.busy": "2022-01-10T17:36:46.313618Z",
     "iopub.status.idle": "2022-01-10T17:36:46.796144Z",
     "shell.execute_reply": "2022-01-10T17:36:46.796682Z",
     "shell.execute_reply.started": "2022-01-10T17:24:54.411988Z"
    },
    "papermill": {
     "duration": 0.497856,
     "end_time": "2022-01-10T17:36:46.796862",
     "exception": false,
     "start_time": "2022-01-10T17:36:46.299006",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "# Criação do modelo\n",
    "modelo = ExtraTreesClassifier() # algoritmo que cria várias árvores de decisão\n",
    "modelo.fit(x_treino, y_treino)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f053907d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T17:36:46.829888Z",
     "iopub.status.busy": "2022-01-10T17:36:46.829260Z",
     "iopub.status.idle": "2022-01-10T17:36:46.867829Z",
     "shell.execute_reply": "2022-01-10T17:36:46.868400Z",
     "shell.execute_reply.started": "2022-01-10T17:25:03.202771Z"
    },
    "papermill": {
     "duration": 0.058491,
     "end_time": "2022-01-10T17:36:46.868576",
     "exception": false,
     "start_time": "2022-01-10T17:36:46.810085",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Imprimindo resultados\n",
    "resultado = modelo.score(x_teste, y_teste) # irá prever de acordo com o modelo que ele 'aprendeu' com os dados de treino, utilizando agora dados não visto(os de teste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e3f093ef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T17:36:46.899823Z",
     "iopub.status.busy": "2022-01-10T17:36:46.897530Z",
     "iopub.status.idle": "2022-01-10T17:36:46.902692Z",
     "shell.execute_reply": "2022-01-10T17:36:46.902129Z",
     "shell.execute_reply.started": "2022-01-10T17:25:09.946002Z"
    },
    "papermill": {
     "duration": 0.021569,
     "end_time": "2022-01-10T17:36:46.902829",
     "exception": false,
     "start_time": "2022-01-10T17:36:46.881260",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia:  0.9974358974358974\n"
     ]
    }
   ],
   "source": [
    "print(\"Acurácia: \", resultado)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f449e74",
   "metadata": {
    "papermill": {
     "duration": 0.012521,
     "end_time": "2022-01-10T17:36:46.929038",
     "exception": false,
     "start_time": "2022-01-10T17:36:46.916517",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Devemos lembrar que uma acurácia alta nem sempre é indício de sucesso! Muitas vezes ela pode indicar algum tipo de _overfitting_, quando o algoritmo aprende 'demais' o modelo!\n",
    "\n",
    "Quantos mais complexo o dataset, mais improvável uma acurácia acima de 70%. Neste caso específico, o dataset é mais simples e a solução já foi validada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "681fb5cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T17:36:46.960017Z",
     "iopub.status.busy": "2022-01-10T17:36:46.956456Z",
     "iopub.status.idle": "2022-01-10T17:36:46.962666Z",
     "shell.execute_reply": "2022-01-10T17:36:46.963122Z",
     "shell.execute_reply.started": "2022-01-10T17:25:40.400415Z"
    },
    "papermill": {
     "duration": 0.021902,
     "end_time": "2022-01-10T17:36:46.963317",
     "exception": false,
     "start_time": "2022-01-10T17:36:46.941415",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1857    1\n",
       "6127    1\n",
       "5284    1\n",
       "4213    1\n",
       "2270    1\n",
       "Name: style, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_teste[205:210] #filtro de 5 amostras aleatórias que o modelo não teve contato"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1d7fc6dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T17:36:46.992645Z",
     "iopub.status.busy": "2022-01-10T17:36:46.991938Z",
     "iopub.status.idle": "2022-01-10T17:36:47.010263Z",
     "shell.execute_reply": "2022-01-10T17:36:47.010677Z",
     "shell.execute_reply.started": "2022-01-10T17:26:20.038150Z"
    },
    "papermill": {
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     "exception": false,
     "start_time": "2022-01-10T17:36:46.976311",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>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>1857</th>\n",
       "      <td>6.40</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.032</td>\n",
       "      <td>23.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>0.99120</td>\n",
       "      <td>3.32</td>\n",
       "      <td>0.50</td>\n",
       "      <td>11.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6127</th>\n",
       "      <td>6.40</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.19</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.068</td>\n",
       "      <td>18.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.99340</td>\n",
       "      <td>3.03</td>\n",
       "      <td>0.38</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5284</th>\n",
       "      <td>6.80</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.41</td>\n",
       "      <td>12.9</td>\n",
       "      <td>0.044</td>\n",
       "      <td>79.5</td>\n",
       "      <td>183.0</td>\n",
       "      <td>0.99742</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.78</td>\n",
       "      <td>10.2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4213</th>\n",
       "      <td>6.15</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.37</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.021</td>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.99076</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.47</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>2270</th>\n",
       "      <td>6.60</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.36</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.038</td>\n",
       "      <td>22.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.99120</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0.57</td>\n",
       "      <td>11.6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed_acidity  volatile_acidity  citric_acid  residual_sugar  chlorides  \\\n",
       "1857           6.40              0.23         0.39             1.8      0.032   \n",
       "6127           6.40              0.37         0.19             3.5      0.068   \n",
       "5284           6.80              0.15         0.41            12.9      0.044   \n",
       "4213           6.15              0.21         0.37             3.2      0.021   \n",
       "2270           6.60              0.28         0.36             1.7      0.038   \n",
       "\n",
       "      free_sulfur_dioxide  total_sulfur_dioxide  density    pH  sulphates  \\\n",
       "1857                 23.0                 118.0  0.99120  3.32       0.50   \n",
       "6127                 18.0                 101.0  0.99340  3.03       0.38   \n",
       "5284                 79.5                 183.0  0.99742  3.24       0.78   \n",
       "4213                 20.0                  80.0  0.99076  3.39       0.47   \n",
       "2270                 22.0                 101.0  0.99120  3.29       0.57   \n",
       "\n",
       "      alcohol  quality  \n",
       "1857     11.8        6  \n",
       "6127      9.0        6  \n",
       "5284     10.2        6  \n",
       "4213     12.0        5  \n",
       "2270     11.6        6  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_teste[205:210] # filtro de 5 amostras aleatórias que o modelo não teve contato"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "663e433d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T17:36:47.042453Z",
     "iopub.status.busy": "2022-01-10T17:36:47.041845Z",
     "iopub.status.idle": "2022-01-10T17:36:47.058284Z",
     "shell.execute_reply": "2022-01-10T17:36:47.058772Z",
     "shell.execute_reply.started": "2022-01-10T17:33:34.977988Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "previsoes = modelo.predict(x_teste[205:210]) # criação de variável para checar se o modelo treinado acerta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d3e3282f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T17:36:47.088364Z",
     "iopub.status.busy": "2022-01-10T17:36:47.087750Z",
     "iopub.status.idle": "2022-01-10T17:36:47.092663Z",
     "shell.execute_reply": "2022-01-10T17:36:47.093195Z",
     "shell.execute_reply.started": "2022-01-10T17:33:40.556953Z"
    },
    "papermill": {
     "duration": 0.021215,
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     "exception": false,
     "start_time": "2022-01-10T17:36:47.072166",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "previsoes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "928dd870",
   "metadata": {
    "papermill": {
     "duration": 0.012922,
     "end_time": "2022-01-10T17:36:47.119850",
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     "start_time": "2022-01-10T17:36:47.106928",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "O modelo previu corretamente."
   ]
  }
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