WineClassifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <td>0</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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",
       "    </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  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine = pd.read_csv(\"wine.csv\")\n",
    "wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1599 entries, 0 to 1598\n",
      "Data columns (total 12 columns):\n",
      "fixed acidity           1599 non-null float64\n",
      "volatile acidity        1599 non-null float64\n",
      "citric acid             1599 non-null float64\n",
      "residual sugar          1599 non-null float64\n",
      "chlorides               1599 non-null float64\n",
      "free sulfur dioxide     1599 non-null float64\n",
      "total sulfur dioxide    1599 non-null float64\n",
      "density                 1599 non-null float64\n",
      "pH                      1599 non-null float64\n",
      "sulphates               1599 non-null float64\n",
      "alcohol                 1599 non-null float64\n",
      "quality                 1599 non-null int64\n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 150.0 KB\n"
     ]
    }
   ],
   "source": [
    "wine.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cd3cf94c48>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.barplot(x='quality',y='fixed acidity' , data=wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cd3cf94588>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'volatile acidity', data = wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cd3d4146c8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'citric acid', data = wine)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = (2,6.5,8)\n",
    "group_names = ['bad','good']\n",
    "wine['quality'] = pd.cut(wine['quality'] , bins=bins,labels=group_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_quality = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine['quality'] = label_quality.fit_transform(wine['quality'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1382\n",
       "1     217\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine['quality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cd3d538388>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.countplot(wine['quality'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = wine.drop('quality',axis=1)\n",
    "y = wine['quality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = sc.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "803     0\n",
      "124     0\n",
      "350     0\n",
      "682     0\n",
      "1326    0\n",
      "       ..\n",
      "1259    0\n",
      "1295    0\n",
      "1155    0\n",
      "963     0\n",
      "704     0\n",
      "Name: quality, Length: 320, dtype: int32 [0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0\n",
      " 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
      " 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0\n",
      " 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "#randomforest\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators=200)\n",
    "rfc.fit(X_train,y_train)\n",
    "pred_rfc = rfc.predict(X_test)\n",
    "print(y_test, pred_rfc)"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}