Wine_for_Github.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from scipy import stats\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import sklearn as sk\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import accuracy_score# Importing libraries\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "import sklearn.linear_model as linear_model\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import make_scorer, r2_score\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import BaggingRegressor, GradientBoostingRegressor\n",
    "#import xgboost as xgb\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>index</th>\n",
       "      <th>quality</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>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.695</td>\n",
       "      <td>0.06</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.042</td>\n",
       "      <td>9.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.99432</td>\n",
       "      <td>3.44</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.2</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.067</td>\n",
       "      <td>10.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.99690</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.59</td>\n",
       "      <td>9.5</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.057</td>\n",
       "      <td>21.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>0.99176</td>\n",
       "      <td>3.05</td>\n",
       "      <td>0.52</td>\n",
       "      <td>10.9</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.31</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.046</td>\n",
       "      <td>29.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.99390</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.50</td>\n",
       "      <td>10.8</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.26</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.059</td>\n",
       "      <td>32.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.99550</td>\n",
       "      <td>3.04</td>\n",
       "      <td>0.43</td>\n",
       "      <td>10.9</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  quality  fixed acidity  volatile acidity  citric acid  \\\n",
       "0      0        5            5.6             0.695         0.06   \n",
       "1      1        5            8.8             0.610         0.14   \n",
       "2      2        5            7.9             0.210         0.39   \n",
       "3      3        6            7.0             0.210         0.31   \n",
       "4      4        6            7.8             0.400         0.26   \n",
       "\n",
       "   residual sugar  chlorides  free sulfur dioxide  total sulfur dioxide  \\\n",
       "0             6.8      0.042                  9.0                  84.0   \n",
       "1             2.4      0.067                 10.0                  42.0   \n",
       "2             2.0      0.057                 21.0                 138.0   \n",
       "3             6.0      0.046                 29.0                 108.0   \n",
       "4             9.5      0.059                 32.0                 178.0   \n",
       "\n",
       "   density    pH  sulphates  alcohol   type  \n",
       "0  0.99432  3.44       0.44     10.2  white  \n",
       "1  0.99690  3.19       0.59      9.5    red  \n",
       "2  0.99176  3.05       0.52     10.9  white  \n",
       "3  0.99390  3.26       0.50     10.8  white  \n",
       "4  0.99550  3.04       0.43     10.9  white  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('./wine/train.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>index</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>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.48</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.043</td>\n",
       "      <td>11.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.99380</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.38</td>\n",
       "      <td>11.6</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.070</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.00040</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.0</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.27</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.040</td>\n",
       "      <td>44.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>0.99480</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.69</td>\n",
       "      <td>9.4</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.043</td>\n",
       "      <td>21.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0.99480</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0.47</td>\n",
       "      <td>10.0</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.019</td>\n",
       "      <td>23.5</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.99041</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.47</td>\n",
       "      <td>11.8</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "0      0            9.0              0.31         0.48             6.6   \n",
       "1      1           13.3              0.43         0.58             1.9   \n",
       "2      2            6.5              0.28         0.27             5.2   \n",
       "3      3            7.2              0.15         0.39             1.8   \n",
       "4      4            6.8              0.26         0.26             2.0   \n",
       "\n",
       "   chlorides  free sulfur dioxide  total sulfur dioxide  density    pH  \\\n",
       "0      0.043                 11.0                  73.0  0.99380  2.90   \n",
       "1      0.070                 15.0                  40.0  1.00040  3.06   \n",
       "2      0.040                 44.0                 179.0  0.99480  3.19   \n",
       "3      0.043                 21.0                 159.0  0.99480  3.52   \n",
       "4      0.019                 23.5                  72.0  0.99041  3.16   \n",
       "\n",
       "   sulphates  alcohol   type  \n",
       "0       0.38     11.6  white  \n",
       "1       0.49      9.0    red  \n",
       "2       0.69      9.4  white  \n",
       "3       0.47     10.0  white  \n",
       "4       0.47     11.8  white  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('./wine/test.csv')\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>index</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  quality\n",
       "0      0        0\n",
       "1      1        0\n",
       "2      2        0\n",
       "3      3        0\n",
       "4      4        0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.read_csv('./wine/sample_submission.csv')\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                   0\n",
       "quality                 0\n",
       "fixed acidity           0\n",
       "volatile acidity        0\n",
       "citric acid             0\n",
       "residual sugar          0\n",
       "chlorides               0\n",
       "free sulfur dioxide     0\n",
       "total sulfur dioxide    0\n",
       "density                 0\n",
       "pH                      0\n",
       "sulphates               0\n",
       "alcohol                 0\n",
       "type                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5497 entries, 0 to 5496\n",
      "Data columns (total 14 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   index                 5497 non-null   int64  \n",
      " 1   quality               5497 non-null   int64  \n",
      " 2   fixed acidity         5497 non-null   float64\n",
      " 3   volatile acidity      5497 non-null   float64\n",
      " 4   citric acid           5497 non-null   float64\n",
      " 5   residual sugar        5497 non-null   float64\n",
      " 6   chlorides             5497 non-null   float64\n",
      " 7   free sulfur dioxide   5497 non-null   float64\n",
      " 8   total sulfur dioxide  5497 non-null   float64\n",
      " 9   density               5497 non-null   float64\n",
      " 10  pH                    5497 non-null   float64\n",
      " 11  sulphates             5497 non-null   float64\n",
      " 12  alcohol               5497 non-null   float64\n",
      " 13  type                  5497 non-null   object \n",
      "dtypes: float64(11), int64(2), object(1)\n",
      "memory usage: 601.4+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Playdata\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 51333 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Playdata\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 49549 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Playdata\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 48320 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Playdata\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 49688 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Playdata\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 51032 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\Playdata\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 51333 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Playdata\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 49549 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Playdata\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 48320 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Playdata\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 49688 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\Playdata\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 51032 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "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(x = 'quality', data = train )\n",
    "plt.title('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27560278108>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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"
    }
   ],
   "source": [
    "sns.distplot(train['alcohol'], kde=True, rug=True)\n",
    "#sns.barplot(x=train['alcohol'], y=train['quality'])\n",
    "plt.title('Distplot of Alcohol')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### see corrleation between Type and Quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27560374d08>"
      ]
     },
     "execution_count": 16,
     "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=train['type'], y=train['quality'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. fill NaN in train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>index</th>\n",
       "      <th>quality</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>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.695</td>\n",
       "      <td>0.06</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.042</td>\n",
       "      <td>9.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.99432</td>\n",
       "      <td>3.44</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.067</td>\n",
       "      <td>10.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.99690</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.59</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.057</td>\n",
       "      <td>21.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>0.99176</td>\n",
       "      <td>3.05</td>\n",
       "      <td>0.52</td>\n",
       "      <td>10.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.31</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.046</td>\n",
       "      <td>29.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.99390</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.50</td>\n",
       "      <td>10.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.26</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.059</td>\n",
       "      <td>32.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.99550</td>\n",
       "      <td>3.04</td>\n",
       "      <td>0.43</td>\n",
       "      <td>10.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.37</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.032</td>\n",
       "      <td>17.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.99320</td>\n",
       "      <td>3.08</td>\n",
       "      <td>0.66</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.051</td>\n",
       "      <td>18.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.99280</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.42</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0.041</td>\n",
       "      <td>32.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>0.99624</td>\n",
       "      <td>3.04</td>\n",
       "      <td>0.49</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.31</td>\n",
       "      <td>18.3</td>\n",
       "      <td>0.046</td>\n",
       "      <td>40.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.41</td>\n",
       "      <td>8.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.35</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0.044</td>\n",
       "      <td>57.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>0.99775</td>\n",
       "      <td>3.07</td>\n",
       "      <td>0.53</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  quality  fixed acidity  volatile acidity  citric acid  \\\n",
       "0      0        5            5.6             0.695         0.06   \n",
       "1      1        5            8.8             0.610         0.14   \n",
       "2      2        5            7.9             0.210         0.39   \n",
       "3      3        6            7.0             0.210         0.31   \n",
       "4      4        6            7.8             0.400         0.26   \n",
       "5      5        6            6.0             0.190         0.37   \n",
       "6      6        5            6.1             0.220         0.49   \n",
       "7      7        6            7.1             0.380         0.42   \n",
       "8      8        5            6.8             0.240         0.31   \n",
       "9      9        5            6.8             0.390         0.35   \n",
       "\n",
       "   residual sugar  chlorides  free sulfur dioxide  total sulfur dioxide  \\\n",
       "0             6.8      0.042                  9.0                  84.0   \n",
       "1             2.4      0.067                 10.0                  42.0   \n",
       "2             2.0      0.057                 21.0                 138.0   \n",
       "3             6.0      0.046                 29.0                 108.0   \n",
       "4             9.5      0.059                 32.0                 178.0   \n",
       "5             9.7      0.032                 17.0                  50.0   \n",
       "6             1.5      0.051                 18.0                  87.0   \n",
       "7            11.8      0.041                 32.0                 193.0   \n",
       "8            18.3      0.046                 40.0                 142.0   \n",
       "9            11.6      0.044                 57.0                 220.0   \n",
       "\n",
       "   density    pH  sulphates  alcohol type  \n",
       "0  0.99432  3.44       0.44     10.2    0  \n",
       "1  0.99690  3.19       0.59      9.5    1  \n",
       "2  0.99176  3.05       0.52     10.9    0  \n",
       "3  0.99390  3.26       0.50     10.8    0  \n",
       "4  0.99550  3.04       0.43     10.9    0  \n",
       "5  0.99320  3.08       0.66     12.0    0  \n",
       "6  0.99280  3.30       0.46      9.6    0  \n",
       "7  0.99624  3.04       0.49     10.0    0  \n",
       "8  1.00000  3.30       0.41      8.7    0  \n",
       "9  0.99775  3.07       0.53      9.3    0  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[train['type'] == 'white', 'type'] = 0\n",
    "train.loc[train['type'] == 'red', 'type'] = 1\n",
    "train.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fill NaN in test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>index</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>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.48</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.043</td>\n",
       "      <td>11.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.99380</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.38</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.070</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.00040</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.27</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.040</td>\n",
       "      <td>44.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>0.99480</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.69</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.043</td>\n",
       "      <td>21.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0.99480</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0.47</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.019</td>\n",
       "      <td>23.5</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.99041</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.47</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.28</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.046</td>\n",
       "      <td>80.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>0.99685</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.41</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.069</td>\n",
       "      <td>11.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.99550</td>\n",
       "      <td>3.42</td>\n",
       "      <td>0.74</td>\n",
       "      <td>10.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.42</td>\n",
       "      <td>14.4</td>\n",
       "      <td>0.040</td>\n",
       "      <td>49.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.99940</td>\n",
       "      <td>3.28</td>\n",
       "      <td>0.82</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.23</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.047</td>\n",
       "      <td>37.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>0.99906</td>\n",
       "      <td>3.08</td>\n",
       "      <td>0.46</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.036</td>\n",
       "      <td>30.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>0.99080</td>\n",
       "      <td>3.28</td>\n",
       "      <td>0.43</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "0      0            9.0              0.31         0.48             6.6   \n",
       "1      1           13.3              0.43         0.58             1.9   \n",
       "2      2            6.5              0.28         0.27             5.2   \n",
       "3      3            7.2              0.15         0.39             1.8   \n",
       "4      4            6.8              0.26         0.26             2.0   \n",
       "5      5            6.5              0.26         0.28            12.5   \n",
       "6      6            6.1              0.40         0.16             1.8   \n",
       "7      7            7.3              0.28         0.42            14.4   \n",
       "8      8            6.6              0.22         0.23            17.3   \n",
       "9      9            7.1              0.25         0.39             2.1   \n",
       "\n",
       "   chlorides  free sulfur dioxide  total sulfur dioxide  density    pH  \\\n",
       "0      0.043                 11.0                  73.0  0.99380  2.90   \n",
       "1      0.070                 15.0                  40.0  1.00040  3.06   \n",
       "2      0.040                 44.0                 179.0  0.99480  3.19   \n",
       "3      0.043                 21.0                 159.0  0.99480  3.52   \n",
       "4      0.019                 23.5                  72.0  0.99041  3.16   \n",
       "5      0.046                 80.0                 225.0  0.99685  3.18   \n",
       "6      0.069                 11.0                  25.0  0.99550  3.42   \n",
       "7      0.040                 49.0                 173.0  0.99940  3.28   \n",
       "8      0.047                 37.0                 118.0  0.99906  3.08   \n",
       "9      0.036                 30.0                 124.0  0.99080  3.28   \n",
       "\n",
       "   sulphates  alcohol type  \n",
       "0       0.38     11.6    0  \n",
       "1       0.49      9.0    1  \n",
       "2       0.69      9.4    0  \n",
       "3       0.47     10.0    0  \n",
       "4       0.47     11.8    0  \n",
       "5       0.41     10.0    0  \n",
       "6       0.74     10.1    1  \n",
       "7       0.82      9.0    0  \n",
       "8       0.46      8.8    0  \n",
       "9       0.43     12.2    0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>index</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>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.48</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.043</td>\n",
       "      <td>11.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.99380</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.38</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.070</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.00040</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.27</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.040</td>\n",
       "      <td>44.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>0.99480</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.69</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.043</td>\n",
       "      <td>21.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0.99480</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0.47</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.019</td>\n",
       "      <td>23.5</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.99041</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.47</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.28</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.046</td>\n",
       "      <td>80.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>0.99685</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.41</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.069</td>\n",
       "      <td>11.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.99550</td>\n",
       "      <td>3.42</td>\n",
       "      <td>0.74</td>\n",
       "      <td>10.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.42</td>\n",
       "      <td>14.4</td>\n",
       "      <td>0.040</td>\n",
       "      <td>49.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.99940</td>\n",
       "      <td>3.28</td>\n",
       "      <td>0.82</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.23</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.047</td>\n",
       "      <td>37.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>0.99906</td>\n",
       "      <td>3.08</td>\n",
       "      <td>0.46</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.036</td>\n",
       "      <td>30.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>0.99080</td>\n",
       "      <td>3.28</td>\n",
       "      <td>0.43</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "0      0            9.0              0.31         0.48             6.6   \n",
       "1      1           13.3              0.43         0.58             1.9   \n",
       "2      2            6.5              0.28         0.27             5.2   \n",
       "3      3            7.2              0.15         0.39             1.8   \n",
       "4      4            6.8              0.26         0.26             2.0   \n",
       "5      5            6.5              0.26         0.28            12.5   \n",
       "6      6            6.1              0.40         0.16             1.8   \n",
       "7      7            7.3              0.28         0.42            14.4   \n",
       "8      8            6.6              0.22         0.23            17.3   \n",
       "9      9            7.1              0.25         0.39             2.1   \n",
       "\n",
       "   chlorides  free sulfur dioxide  total sulfur dioxide  density    pH  \\\n",
       "0      0.043                 11.0                  73.0  0.99380  2.90   \n",
       "1      0.070                 15.0                  40.0  1.00040  3.06   \n",
       "2      0.040                 44.0                 179.0  0.99480  3.19   \n",
       "3      0.043                 21.0                 159.0  0.99480  3.52   \n",
       "4      0.019                 23.5                  72.0  0.99041  3.16   \n",
       "5      0.046                 80.0                 225.0  0.99685  3.18   \n",
       "6      0.069                 11.0                  25.0  0.99550  3.42   \n",
       "7      0.040                 49.0                 173.0  0.99940  3.28   \n",
       "8      0.047                 37.0                 118.0  0.99906  3.08   \n",
       "9      0.036                 30.0                 124.0  0.99080  3.28   \n",
       "\n",
       "   sulphates  alcohol type  \n",
       "0       0.38     11.6    0  \n",
       "1       0.49      9.0    1  \n",
       "2       0.69      9.4    0  \n",
       "3       0.47     10.0    0  \n",
       "4       0.47     11.8    0  \n",
       "5       0.41     10.0    0  \n",
       "6       0.74     10.1    1  \n",
       "7       0.82      9.0    0  \n",
       "8       0.46      8.8    0  \n",
       "9       0.43     12.2    0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.loc[test['type'] == 'white', 'type'] = 0\n",
    "test.loc[test['type'] == 'red', 'type'] = 1\n",
    "test.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-1) Alcohol data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train2 = train.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>index</th>\n",
       "      <th>quality</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>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.695</td>\n",
       "      <td>0.06</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.042</td>\n",
       "      <td>9.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.99432</td>\n",
       "      <td>3.44</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.067</td>\n",
       "      <td>10.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.99690</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.59</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.057</td>\n",
       "      <td>21.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>0.99176</td>\n",
       "      <td>3.05</td>\n",
       "      <td>0.52</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.31</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.046</td>\n",
       "      <td>29.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.99390</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.50</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.26</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.059</td>\n",
       "      <td>32.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.99550</td>\n",
       "      <td>3.04</td>\n",
       "      <td>0.43</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  quality  fixed acidity  volatile acidity  citric acid  \\\n",
       "0      0        5            5.6             0.695         0.06   \n",
       "1      1        5            8.8             0.610         0.14   \n",
       "2      2        5            7.9             0.210         0.39   \n",
       "3      3        6            7.0             0.210         0.31   \n",
       "4      4        6            7.8             0.400         0.26   \n",
       "\n",
       "   residual sugar  chlorides  free sulfur dioxide  total sulfur dioxide  \\\n",
       "0             6.8      0.042                  9.0                  84.0   \n",
       "1             2.4      0.067                 10.0                  42.0   \n",
       "2             2.0      0.057                 21.0                 138.0   \n",
       "3             6.0      0.046                 29.0                 108.0   \n",
       "4             9.5      0.059                 32.0                 178.0   \n",
       "\n",
       "   density    pH  sulphates  alcohol type  \n",
       "0  0.99432  3.44       0.44     10.0    0  \n",
       "1  0.99690  3.19       0.59     10.0    1  \n",
       "2  0.99176  3.05       0.52     11.0    0  \n",
       "3  0.99390  3.26       0.50     11.0    0  \n",
       "4  0.99550  3.04       0.43     11.0    0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train2['alcohol'] = round(train2['alcohol'])\n",
    "train2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-2) pH data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>index</th>\n",
       "      <th>quality</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>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.695</td>\n",
       "      <td>0.06</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.042</td>\n",
       "      <td>9.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.99432</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.067</td>\n",
       "      <td>10.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.99690</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.59</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.057</td>\n",
       "      <td>21.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>0.99176</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.31</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.046</td>\n",
       "      <td>29.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.99390</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.50</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.26</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.059</td>\n",
       "      <td>32.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.99550</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.43</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  quality  fixed acidity  volatile acidity  citric acid  \\\n",
       "0      0        5            5.6             0.695         0.06   \n",
       "1      1        5            8.8             0.610         0.14   \n",
       "2      2        5            7.9             0.210         0.39   \n",
       "3      3        6            7.0             0.210         0.31   \n",
       "4      4        6            7.8             0.400         0.26   \n",
       "\n",
       "   residual sugar  chlorides  free sulfur dioxide  total sulfur dioxide  \\\n",
       "0             6.8      0.042                  9.0                  84.0   \n",
       "1             2.4      0.067                 10.0                  42.0   \n",
       "2             2.0      0.057                 21.0                 138.0   \n",
       "3             6.0      0.046                 29.0                 108.0   \n",
       "4             9.5      0.059                 32.0                 178.0   \n",
       "\n",
       "   density   pH  sulphates  alcohol type  \n",
       "0  0.99432  3.4       0.44     10.0    0  \n",
       "1  0.99690  3.2       0.59     10.0    1  \n",
       "2  0.99176  3.0       0.52     11.0    0  \n",
       "3  0.99390  3.3       0.50     11.0    0  \n",
       "4  0.99550  3.0       0.43     11.0    0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train2['pH'] = round(train2['pH'],1)\n",
    "train2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x275606f9f48>"
      ]
     },
     "execution_count": 23,
     "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(x = 'pH', data=train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x275603e0bc8>"
      ]
     },
     "execution_count": 24,
     "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(x = 'pH', data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sulphates \n",
    "#### quality= if 3~4 , trunc,  if 5~6 , round,  if, 7~8 up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>index</th>\n",
       "      <th>quality</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>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.695</td>\n",
       "      <td>0.06</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.042</td>\n",
       "      <td>9.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.99432</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.067</td>\n",
       "      <td>10.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.99690</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.057</td>\n",
       "      <td>21.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>0.99176</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.31</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.046</td>\n",
       "      <td>29.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.99390</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.26</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.059</td>\n",
       "      <td>32.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.99550</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  quality  fixed acidity  volatile acidity  citric acid  \\\n",
       "0      0        5            5.6             0.695         0.06   \n",
       "1      1        5            8.8             0.610         0.14   \n",
       "2      2        5            7.9             0.210         0.39   \n",
       "3      3        6            7.0             0.210         0.31   \n",
       "4      4        6            7.8             0.400         0.26   \n",
       "\n",
       "   residual sugar  chlorides  free sulfur dioxide  total sulfur dioxide  \\\n",
       "0             6.8      0.042                  9.0                  84.0   \n",
       "1             2.4      0.067                 10.0                  42.0   \n",
       "2             2.0      0.057                 21.0                 138.0   \n",
       "3             6.0      0.046                 29.0                 108.0   \n",
       "4             9.5      0.059                 32.0                 178.0   \n",
       "\n",
       "   density   pH  sulphates  alcohol type  \n",
       "0  0.99432  3.4        4.4     10.0    0  \n",
       "1  0.99690  3.2        5.9     10.0    1  \n",
       "2  0.99176  3.0        5.2     11.0    0  \n",
       "3  0.99390  3.3        5.0     11.0    0  \n",
       "4  0.99550  3.0        4.3     11.0    0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train2['sulphates'] = train2['sulphates'] * 10\n",
    "train2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>index</th>\n",
       "      <th>quality</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>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.695</td>\n",
       "      <td>0.06</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.042</td>\n",
       "      <td>9.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.99432</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.067</td>\n",
       "      <td>10.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.99690</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.057</td>\n",
       "      <td>21.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>0.99176</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.31</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.046</td>\n",
       "      <td>29.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.99390</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.26</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.059</td>\n",
       "      <td>32.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.99550</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  quality  fixed acidity  volatile acidity  citric acid  \\\n",
       "0      0        5            5.6             0.695         0.06   \n",
       "1      1        5            8.8             0.610         0.14   \n",
       "2      2        5            7.9             0.210         0.39   \n",
       "3      3        6            7.0             0.210         0.31   \n",
       "4      4        6            7.8             0.400         0.26   \n",
       "\n",
       "   residual sugar  chlorides  free sulfur dioxide  total sulfur dioxide  \\\n",
       "0             6.8      0.042                  9.0                  84.0   \n",
       "1             2.4      0.067                 10.0                  42.0   \n",
       "2             2.0      0.057                 21.0                 138.0   \n",
       "3             6.0      0.046                 29.0                 108.0   \n",
       "4             9.5      0.059                 32.0                 178.0   \n",
       "\n",
       "   density   pH  sulphates  alcohol type  \n",
       "0  0.99432  3.4        4.0     10.0    0  \n",
       "1  0.99690  3.2        6.0     10.0    1  \n",
       "2  0.99176  3.0        5.0     11.0    0  \n",
       "3  0.99390  3.3        5.0     11.0    0  \n",
       "4  0.99550  3.0        4.0     11.0    0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train2.loc[(train2['quality'] == 5)|(train2['quality'] == 6) , 'sulphates'] = round(train2['sulphates'])\n",
    "train2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def upanddown(sulphates, act) :\n",
    "    nt = sulphates.astype(int)\n",
    "    if act == 'up' :\n",
    "        answer = nt + 1\n",
    "    else :\n",
    "        answer = nt\n",
    "    return answer\n",
    "\n",
    "train2.loc[(train2['quality'] == 7)|(train2['quality'] == 8) , 'sulphates'] = upanddown(train2['sulphates'], 'up')\n",
    "train2.loc[(train2['quality'] == 3)|(train2['quality'] == 4) , 'sulphates'] = upanddown(train2['sulphates'], 'down')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>index</th>\n",
       "      <th>quality</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>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.695</td>\n",
       "      <td>0.06</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.042</td>\n",
       "      <td>9.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.99432</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.067</td>\n",
       "      <td>10.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.99690</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.057</td>\n",
       "      <td>21.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>0.99176</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.31</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.046</td>\n",
       "      <td>29.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.99390</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.26</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.059</td>\n",
       "      <td>32.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.99550</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  quality  fixed acidity  volatile acidity  citric acid  \\\n",
       "0      0        5            5.6             0.695         0.06   \n",
       "1      1        5            8.8             0.610         0.14   \n",
       "2      2        5            7.9             0.210         0.39   \n",
       "3      3        6            7.0             0.210         0.31   \n",
       "4      4        6            7.8             0.400         0.26   \n",
       "\n",
       "   residual sugar  chlorides  free sulfur dioxide  total sulfur dioxide  \\\n",
       "0             6.8      0.042                  9.0                  84.0   \n",
       "1             2.4      0.067                 10.0                  42.0   \n",
       "2             2.0      0.057                 21.0                 138.0   \n",
       "3             6.0      0.046                 29.0                 108.0   \n",
       "4             9.5      0.059                 32.0                 178.0   \n",
       "\n",
       "   density   pH  sulphates  alcohol type  \n",
       "0  0.99432  3.4        0.4     10.0    0  \n",
       "1  0.99690  3.2        0.6     10.0    1  \n",
       "2  0.99176  3.0        0.5     11.0    0  \n",
       "3  0.99390  3.3        0.5     11.0    0  \n",
       "4  0.99550  3.0        0.4     11.0    0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train2['sulphates'] = train2['sulphates'] / 10\n",
    "train2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train2[['quality']]\n",
    "X_train = train2.drop(['index', 'quality'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4397, 12)\n",
      "(1100, 12)\n",
      "(4397, 1)\n",
      "(1100, 1)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, \n",
    "                                                    test_size=0.2, \n",
    "                                                    random_state=10)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Decision Tree is 0.6072727272727273\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Playdata\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Random Forests is 0.6954545454545454\n"
     ]
    }
   ],
   "source": [
    "# Decision tree\n",
    "\n",
    "model=DecisionTreeClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "prediction4=model.predict(X_test)\n",
    "print('The accuracy of the Decision Tree is', metrics.accuracy_score(prediction4, y_test))\n",
    "\n",
    "# Random forest\n",
    "\n",
    "model = RandomForestClassifier(n_estimators = 100)\n",
    "model.fit(X_train, y_train)\n",
    "prediction6 = model.predict(X_test)\n",
    "print('The accuracy of the Random Forests is', metrics.accuracy_score(prediction6, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-1-a78b4a512570>, line 7)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-1-a78b4a512570>\"\u001b[1;36m, line \u001b[1;32m7\u001b[0m\n\u001b[1;33m    'max_depth' :\u001b[0m\n\u001b[1;37m                ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "import time\n",
    "\n",
    "start = time.time()\n",
    "params = {\n",
    "    'n_estimators':\n",
    "    'max_depth' :  \n",
    "    'min_samples_leaf' : \n",
    "    'min_samples_split' : \n",
    "}\n",
    "# RandomForestClassifier 객체 생성 후 GridSearchCV 수행\n",
    "rf_clf = RandomForestClassifier(random_state=0, n_jobs=-1)\n",
    "grid_cv = GridSearchCV(rf_clf , param_grid=params , cv=2, n_jobs=-1, verbose=2 )\n",
    "grid_cv.fit(X_train , y_train)\n",
    "\n",
    "#print('best parameter:\\n', grid_cv.best_params_)\n",
    "print('best predict accuracy: {0:.4f}'.format(grid_cv.best_score_))\n",
    "\n",
    "print('time  : ', (time.time()-start) / 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Playdata\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Random Forests is 0.6236363636363637\n"
     ]
    }
   ],
   "source": [
    "rf_clf1 = RandomForestClassifier(n_estimators=200, max_depth=25, min_samples_leaf=2, \\\n",
    "                                 min_samples_split=5, random_state=0)\n",
    "rf_clf1.fit(X_train , y_train)\n",
    "rf_clf1.predict(X_test)\n",
    "prediction6 = model.predict(X_test)\n",
    "print('The accuracy of the Random Forests is', metrics.accuracy_score(prediction6, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## -for secure, copy data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train[['quality']]\n",
    "X_train = train.drop(['index', 'quality'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quality\n",
       "0        5\n",
       "1        5\n",
       "2        5\n",
       "3        6\n",
       "4        6"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.6</td>\n",
       "      <td>0.695</td>\n",
       "      <td>0.06</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.042</td>\n",
       "      <td>9.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.99432</td>\n",
       "      <td>3.44</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.8</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.067</td>\n",
       "      <td>10.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.99690</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.59</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.9</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.057</td>\n",
       "      <td>21.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>0.99176</td>\n",
       "      <td>3.05</td>\n",
       "      <td>0.52</td>\n",
       "      <td>10.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.31</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.046</td>\n",
       "      <td>29.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.99390</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.50</td>\n",
       "      <td>10.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.26</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.059</td>\n",
       "      <td>32.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.99550</td>\n",
       "      <td>3.04</td>\n",
       "      <td>0.43</td>\n",
       "      <td>10.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            5.6             0.695         0.06             6.8      0.042   \n",
       "1            8.8             0.610         0.14             2.4      0.067   \n",
       "2            7.9             0.210         0.39             2.0      0.057   \n",
       "3            7.0             0.210         0.31             6.0      0.046   \n",
       "4            7.8             0.400         0.26             9.5      0.059   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                  9.0                  84.0  0.99432  3.44       0.44   \n",
       "1                 10.0                  42.0  0.99690  3.19       0.59   \n",
       "2                 21.0                 138.0  0.99176  3.05       0.52   \n",
       "3                 29.0                 108.0  0.99390  3.26       0.50   \n",
       "4                 32.0                 178.0  0.99550  3.04       0.43   \n",
       "\n",
       "   alcohol type  \n",
       "0     10.2    0  \n",
       "1      9.5    1  \n",
       "2     10.9    0  \n",
       "3     10.8    0  \n",
       "4     10.9    0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5497, 12)\n",
      "(5497, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y_train\n",
    "X = X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression #logistic regression\n",
    "from sklearn import svm #support vector Machine\n",
    "from sklearn.ensemble import RandomForestClassifier #Random Forest\n",
    "from sklearn.neighbors import KNeighborsClassifier #KNN\n",
    "from sklearn.naive_bayes import GaussianNB #Naive bayes\n",
    "from sklearn.tree import DecisionTreeClassifier #Decision Tree\n",
    "from sklearn.model_selection import train_test_split #training and testing data split\n",
    "from sklearn import metrics #accuracy measure\n",
    "from sklearn.metrics import confusion_matrix #for confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4397, 12)\n",
      "(1100, 12)\n",
      "(4397, 1)\n",
      "(1100, 1)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, \n",
    "                                                    test_size=0.2, \n",
    "                                                    random_state=10)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Playdata\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for rbf SVM is  0.5618181818181818\n"
     ]
    }
   ],
   "source": [
    "# radial SVM\n",
    "model=svm.SVC(kernel='rbf',C=1,gamma=0.1)\n",
    "model.fit(X_train, y_train)\n",
    "prediction1=model.predict(X_test)\n",
    "print('Accuracy for rbf SVM is ',metrics.accuracy_score(prediction1,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Decision Tree is 0.6154545454545455\n"
     ]
    }
   ],
   "source": [
    "# Decision tree\n",
    "\n",
    "model=DecisionTreeClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "prediction4=model.predict(X_test)\n",
    "print('The accuracy of the Decision Tree is', metrics.accuracy_score(prediction4, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the KNN is 0.4636363636363636\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Playdata\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "# KNN 군집분석\n",
    "\n",
    "model=KNeighborsClassifier() \n",
    "model.fit(X_train, y_train)\n",
    "prediction4=model.predict(X_test)\n",
    "print('The accuracy of the KNN is', metrics.accuracy_score(prediction4, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Playdata\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "# Random forest\n",
    "\n",
    "model = RandomForestClassifier(random_state = 156)\n",
    "model.fit(X_train, y_train)\n",
    "prediction6 = model.predict(test2)\n",
    "rf = model.predict_proba(test2)\n",
    "#print('The accuracy of the Random Forests is', metrics.accuracy_score(prediction6, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GridGV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-1-0ea82aedc7f8>, line 7)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-1-0ea82aedc7f8>\"\u001b[1;36m, line \u001b[1;32m7\u001b[0m\n\u001b[1;33m    'max_depth' :\u001b[0m\n\u001b[1;37m                ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "import time\n",
    "\n",
    "start = time.time()\n",
    "params = {\n",
    "    'n_estimators':\n",
    "    'max_depth' : \n",
    "    'min_samples_leaf' : \n",
    "    'min_samples_split' : \n",
    "}\n",
    "# RandomForestClassifier 객체 생성 후 GridSearchCV 수행\n",
    "rf_clf = RandomForestClassifier(random_state=0, n_jobs=-1)\n",
    "grid_cv = GridSearchCV(rf_clf , param_grid=params , cv=2, n_jobs=-1, verbose=2 )\n",
    "grid_cv.fit(X_train , y_train)\n",
    "\n",
    "#print('best parameter :\\n', grid_cv.best_params_)\n",
    "print('best predict accuracy: {0:.4f}'.format(grid_cv.best_score_))\n",
    "\n",
    "print('time : ', (time.time()-start) / 60)\n",
    "\n",
    "#0.6273"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GBM 정확도: 0.5936\n",
      "GBM 수행 시간: 6.2 초 \n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "import time\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "#X_train, X_test, y_train, y_test = get_human_dataset()\n",
    "\n",
    "# GBM 수행 시간 측정을 위함. 시작 시간 설정.\n",
    "start_time = time.time()\n",
    "\n",
    "gb_clf = GradientBoostingClassifier(random_state=0)\n",
    "gb_clf.fit(X_train , y_train)\n",
    "gb_pred = gb_clf.predict(X_test)\n",
    "gb_accuracy = accuracy_score(y_test, gb_pred)\n",
    "\n",
    "print('GBM 정확도: {0:.4f}'.format(gb_accuracy))\n",
    "print(\"GBM 수행 시간: {0:.1f} 초 \".format(time.time() - start_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 07/20 Regression apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4397 entries, 3302 to 1289\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   fixed acidity         4397 non-null   float64\n",
      " 1   volatile acidity      4397 non-null   float64\n",
      " 2   citric acid           4397 non-null   float64\n",
      " 3   residual sugar        4397 non-null   float64\n",
      " 4   chlorides             4397 non-null   float64\n",
      " 5   free sulfur dioxide   4397 non-null   float64\n",
      " 6   total sulfur dioxide  4397 non-null   float64\n",
      " 7   density               4397 non-null   float64\n",
      " 8   pH                    4397 non-null   float64\n",
      " 9   sulphates             4397 non-null   float64\n",
      " 10  alcohol               4397 non-null   float64\n",
      " 11  type                  4397 non-null   object \n",
      "dtypes: float64(11), object(1)\n",
      "memory usage: 446.6+ KB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE : 0.520 , RMSE : 0.721\n",
      "Variance score = R제곱 -> 1에 가까울 수록 좋다 (0과 1사이)\n",
      "Variance score : 0.307\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error , r2_score\n",
    "\n",
    "y_target = y_train\n",
    "X_data = X_train\n",
    "\n",
    "X_train , X_test , y_train , y_test = train_test_split(X_data , y_target ,test_size=0.3, random_state=156)\n",
    "\n",
    "# Linear Regression OLS로 학습/예측/평가 수행. \n",
    "lr = LinearRegression()\n",
    "lr.fit( ,y_train )\n",
    "y_preds = lr.predict(X_test)\n",
    "# accuracy score가 아니라 mse 와 rmse 로 확인 0에 가까울 수록 좋다. \n",
    "mse = mean_squared_error(y_test, y_preds)\n",
    "rmse = np.sqrt(mse) #루트 씌워서... 따로 제공해 주는게 없어서..\n",
    "\n",
    "print('MSE : {0:.3f} , RMSE : {1:.3F}'.format(mse , rmse))\n",
    "print('Variance score = R제곱 -> 1에 가까울 수록 좋다 (0과 1사이)')\n",
    "print('Variance score : {0:.3f}'.format(r2_score(y_test, y_preds)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 5 folds 의 개별 Negative MSE scores:  [-0.56 -0.57 -0.56 -0.53 -0.54]\n",
      " 5 folds 의 개별 RMSE scores :  [0.75 0.76 0.75 0.73 0.74]\n",
      " 5 folds 의 평균 RMSE : 0.743 \n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "y_target = y_train\n",
    "X_data = X_train\n",
    "lr = LinearRegression()\n",
    "\n",
    "# cross_val_score( )로 5 Fold 셋으로 MSE 를 구한 뒤 이를 기반으로 다시  RMSE 구함. \n",
    "# scoring - neg = negative 결과 값에 -마이너스를 붙인다 . 왜?\n",
    "# RSE에서는 점수가 낮은게 좋은거. \n",
    "# 기존 내장 분류함수는 높을 수록 좋다고 되어 있으니까 마이너스를 붙인다. \n",
    "neg_mse_scores = cross_val_score(lr, X_data, y_target, scoring=\"neg_mean_squared_error\", cv = 5)\n",
    "\n",
    "\n",
    "rmse_scores  = np.sqrt(-1*neg_mse_scores)\n",
    "avg_rmse = np.mean(rmse_scores)\n",
    "# 교차검등해서 객관적으로 잘 나오게 나누기 5로 해준다 \n",
    "\n",
    "# cross_val_score(scoring=\"neg_mean_squared_error\")로 반환된 값은 모두 음수\n",
    "# 12.46 제일 잘 나온다. 80.76  grid에서는 제일 높게 그래서 마이너스를 붙이는 거다.\n",
    "print(' 5 folds 의 개별 Negative MSE scores: ', np.round(neg_mse_scores, 2))\n",
    "print(' 5 folds 의 개별 RMSE scores : ', np.round(rmse_scores, 2))\n",
    "print(' 5 folds 의 평균 RMSE : {0:.3f} '.format(avg_rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# multi Dimension & Degree (1~7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Degree 1 회귀 계수는 [[   0.   -1.   -0.    0.    0.    0.   -0. -107.    1.    1.    0.    0.]] 입니다. w 계수들 \n",
      "Degree 1 MSE 는 0.55 입니다.\n",
      "\n",
      "Degree 2 회귀 계수는 [[-4.400e+01  1.960e+02  5.650e+02 -1.600e+01 -1.853e+03 -5.000e+00\n",
      "   2.000e+00 -7.556e+03  2.530e+02 -1.220e+02  2.200e+01 -9.000e+00\n",
      "  -0.000e+00  0.000e+00  1.000e+00 -0.000e+00 -3.000e+00 -0.000e+00\n",
      "   0.000e+00  4.500e+01 -0.000e+00 -0.000e+00  0.000e+00  0.000e+00\n",
      "  -0.000e+00  1.000e+00 -0.000e+00  5.000e+00  0.000e+00  0.000e+00\n",
      "  -2.080e+02  1.000e+00 -0.000e+00  0.000e+00  1.000e+00 -2.000e+00\n",
      "   0.000e+00  9.000e+00  0.000e+00 -0.000e+00 -5.830e+02  4.000e+00\n",
      "  -0.000e+00 -0.000e+00 -1.000e+00 -0.000e+00 -1.000e+00 -0.000e+00\n",
      "   0.000e+00  1.600e+01 -0.000e+00 -0.000e+00  0.000e+00  0.000e+00\n",
      "  -8.000e+00  0.000e+00  0.000e+00  1.919e+03 -1.300e+01 -6.000e+00\n",
      "   1.000e+00  2.000e+00 -0.000e+00 -0.000e+00  5.000e+00 -0.000e+00\n",
      "   0.000e+00  0.000e+00 -0.000e+00 -0.000e+00 -2.000e+00  0.000e+00\n",
      "  -0.000e+00 -0.000e+00 -0.000e+00  4.090e+03 -2.550e+02  1.210e+02\n",
      "  -2.200e+01  2.000e+01  1.000e+00  1.000e+00 -0.000e+00 -0.000e+00\n",
      "  -0.000e+00  0.000e+00 -0.000e+00 -0.000e+00  0.000e+00 -9.000e+00]] 입니다. w 계수들 \n",
      "Degree 2 MSE 는 0.53 입니다.\n",
      "\n",
      "Degree 3 회귀 계수는 [[-1.476000e+04 -2.908720e+05  2.449970e+05 -7.353000e+03  4.199700e+05\n",
      "   2.134000e+03  3.200000e+01  8.854084e+06 -7.732200e+04  1.517030e+05\n",
      "   9.256700e+04 -1.617000e+03 -7.000000e+00 -3.080000e+02  4.860000e+02\n",
      "  -1.900000e+01 -1.831000e+03  8.000000e+00 -3.000000e+00  2.772400e+04\n",
      "  -9.600000e+01  7.820000e+02  2.050000e+02 -1.610000e+02  1.860000e+03\n",
      "   1.258000e+03 -2.640000e+02 -1.517800e+04  2.100000e+01 -5.000000e+00\n",
      "   5.898680e+05 -6.960000e+02 -3.450000e+02  7.000000e+00 -8.750000e+02\n",
      "   2.310000e+02  1.440000e+02  5.376000e+03 -4.000000e+00  1.200000e+01\n",
      "  -4.994620e+05  2.248000e+03 -1.462000e+03 -5.790000e+02  4.900000e+02\n",
      "  -5.000000e+00  5.090000e+02  2.000000e+00  0.000000e+00  1.430300e+04\n",
      "  -1.380000e+02  1.670000e+02  9.100000e+01 -2.900000e+01  1.275000e+04\n",
      "   2.140000e+02  1.400000e+01 -7.641650e+05 -2.255700e+04 -1.094900e+04\n",
      "  -6.030000e+02  1.113600e+04 -0.000000e+00  0.000000e+00 -4.355000e+03\n",
      "  -6.000000e+00  2.200000e+01 -3.000000e+00  3.000000e+00 -0.000000e+00\n",
      "  -9.600000e+01  1.200000e+01 -8.000000e+00  1.000000e+00 -2.000000e+00\n",
      "  -8.116120e+06  1.467190e+05 -3.252600e+05 -1.877770e+05  4.086000e+03\n",
      "  -4.680000e+02  4.632000e+03  9.940000e+02 -4.470000e+02  1.870000e+03\n",
      "  -2.120000e+02 -1.820000e+02 -1.570000e+02  3.350000e+02 -2.017000e+03\n",
      "  -0.000000e+00  0.000000e+00  1.000000e+00 -0.000000e+00 -3.000000e+00\n",
      "   0.000000e+00 -0.000000e+00  6.000000e+00 -0.000000e+00  1.000000e+00\n",
      "   0.000000e+00 -0.000000e+00  4.000000e+00 -1.000000e+00 -0.000000e+00\n",
      "  -1.100000e+01  0.000000e+00 -0.000000e+00  2.970000e+02  2.000000e+00\n",
      "   2.000000e+00  0.000000e+00 -4.000000e+00 -3.000000e+00  0.000000e+00\n",
      "   1.800000e+01  0.000000e+00  0.000000e+00 -5.050000e+02  2.000000e+00\n",
      "   1.000000e+00 -0.000000e+00 -2.000000e+00 -0.000000e+00 -2.000000e+00\n",
      "   0.000000e+00 -0.000000e+00  1.900000e+01 -0.000000e+00  0.000000e+00\n",
      "   0.000000e+00 -0.000000e+00  5.000000e+00  0.000000e+00 -0.000000e+00\n",
      "   2.016000e+03 -4.200000e+01 -1.300000e+01  1.000000e+00  1.300000e+01\n",
      "  -0.000000e+00  0.000000e+00 -8.000000e+00  0.000000e+00  0.000000e+00\n",
      "  -0.000000e+00 -0.000000e+00 -0.000000e+00  3.000000e+00 -0.000000e+00\n",
      "  -0.000000e+00  0.000000e+00  0.000000e+00 -1.290100e+04  8.200000e+01\n",
      "  -8.110000e+02 -2.090000e+02  3.000000e+02 -0.000000e+00  6.000000e+00\n",
      "   1.000000e+00 -1.000000e+00  0.000000e+00 -0.000000e+00  2.000000e+00\n",
      "  -0.000000e+00  1.000000e+00 -1.410000e+02 -5.000000e+00 -7.000000e+00\n",
      "   1.000000e+00  7.700000e+01 -0.000000e+00 -0.000000e+00 -1.890000e+03\n",
      "   1.000000e+01 -1.400000e+01 -3.000000e+00  1.000000e+00  3.000000e+00\n",
      "   1.000000e+00  1.400000e+01  0.000000e+00 -0.000000e+00 -1.210000e+03\n",
      "  -2.000000e+00 -2.200000e+01 -2.000000e+00  4.000000e+00 -0.000000e+00\n",
      "  -7.000000e+00  0.000000e+00 -0.000000e+00  2.640000e+02  0.000000e+00\n",
      "   1.000000e+00  0.000000e+00 -1.000000e+00 -1.770000e+02  1.000000e+00\n",
      "  -0.000000e+00  1.572400e+04 -1.580000e+02  3.900000e+01  1.300000e+01\n",
      "  -6.400000e+01 -0.000000e+00 -0.000000e+00 -2.200000e+01  0.000000e+00\n",
      "  -0.000000e+00  0.000000e+00 -0.000000e+00  0.000000e+00  5.000000e+00\n",
      "  -0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00 -2.989760e+05\n",
      "   6.590000e+02  3.350000e+02 -1.000000e+00  1.793000e+03  9.000000e+00\n",
      "   1.000000e+00 -2.000000e+00 -1.100000e+01 -6.000000e+00  1.000000e+00\n",
      "   8.000000e+00  0.000000e+00  0.000000e+00 -8.500000e+02  6.000000e+00\n",
      "   0.000000e+00  4.000000e+00  0.000000e+00 -0.000000e+00 -1.820000e+02\n",
      "  -8.000000e+00 -5.000000e+00 -1.000000e+00  6.000000e+00  0.000000e+00\n",
      "  -2.000000e+00 -0.000000e+00  0.000000e+00 -1.480000e+02  1.000000e+00\n",
      "   0.000000e+00 -0.000000e+00 -0.000000e+00  4.000000e+00  1.000000e+00\n",
      "   0.000000e+00 -6.029000e+03  1.450000e+02  3.400000e+01 -1.000000e+00\n",
      "  -2.000000e+01  0.000000e+00  0.000000e+00  4.000000e+00  0.000000e+00\n",
      "   0.000000e+00  0.000000e+00 -0.000000e+00 -0.000000e+00 -1.200000e+01\n",
      "   0.000000e+00 -0.000000e+00 -0.000000e+00  0.000000e+00  2.545690e+05\n",
      "  -2.273000e+03  1.466000e+03  5.870000e+02 -9.320000e+02  0.000000e+00\n",
      "   2.000000e+00 -1.000000e+00 -6.000000e+00  4.000000e+00 -1.000000e+00\n",
      "  -6.000000e+00  0.000000e+00 -1.000000e+00  4.800000e+02 -0.000000e+00\n",
      "   0.000000e+00  0.000000e+00  0.000000e+00  5.000000e+00 -0.000000e+00\n",
      "   0.000000e+00  0.000000e+00 -0.000000e+00  9.000000e+00  0.000000e+00\n",
      "   0.000000e+00 -4.570000e+02 -1.500000e+01 -7.000000e+00  0.000000e+00\n",
      "   1.100000e+01 -0.000000e+00  0.000000e+00 -2.000000e+00  0.000000e+00\n",
      "   0.000000e+00 -0.000000e+00  0.000000e+00 -0.000000e+00 -0.000000e+00\n",
      "   0.000000e+00 -0.000000e+00  0.000000e+00  0.000000e+00 -6.935000e+03\n",
      "   1.360000e+02 -1.760000e+02 -9.200000e+01  5.800000e+01 -0.000000e+00\n",
      "   2.000000e+00  0.000000e+00 -1.000000e+00  1.000000e+00 -0.000000e+00\n",
      "  -0.000000e+00 -0.000000e+00  0.000000e+00 -2.800000e+01  8.100000e+01\n",
      "  -2.000000e+00 -0.000000e+00 -1.272100e+04  1.300000e+02 -3.400000e+01\n",
      "  -3.900000e+01 -4.600000e+01  0.000000e+00 -0.000000e+00 -2.180000e+02\n",
      "   2.000000e+00  1.000000e+00 -0.000000e+00  0.000000e+00 -0.000000e+00\n",
      "  -1.200000e+01 -0.000000e+00  0.000000e+00 -0.000000e+00  0.000000e+00\n",
      "   3.423830e+05  2.335400e+04  1.123500e+04  5.120000e+02 -2.239400e+04\n",
      "  -1.010000e+02 -8.900000e+01  3.300000e+01  3.000000e+01 -2.200000e+01\n",
      "   8.000000e+00  6.600000e+01  0.000000e+00 -2.300000e+01  1.113900e+04\n",
      "   0.000000e+00 -0.000000e+00  0.000000e+00 -0.000000e+00 -0.000000e+00\n",
      "   0.000000e+00 -0.000000e+00  0.000000e+00 -0.000000e+00  0.000000e+00\n",
      "  -0.000000e+00 -0.000000e+00  0.000000e+00  2.222000e+03  6.000000e+00\n",
      "  -2.200000e+01  3.000000e+00 -2.000000e+00 -0.000000e+00 -0.000000e+00\n",
      "   0.000000e+00 -0.000000e+00 -0.000000e+00  0.000000e+00  0.000000e+00\n",
      "  -0.000000e+00 -0.000000e+00 -1.000000e+00 -0.000000e+00  0.000000e+00\n",
      "   0.000000e+00  0.000000e+00 -0.000000e+00  0.000000e+00  6.400000e+01\n",
      "  -1.200000e+01  8.000000e+00 -1.000000e+00 -7.000000e+00  0.000000e+00\n",
      "  -0.000000e+00 -0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00\n",
      "  -0.000000e+00 -0.000000e+00 -0.000000e+00  8.000000e+00  2.443370e+06\n",
      "  -6.910700e+04  1.738760e+05  9.522200e+04 -2.472000e+03  4.580000e+02\n",
      "  -4.754000e+03 -1.019000e+03  9.080000e+02 -1.915000e+03  2.300000e+02\n",
      "   3.050000e+02  1.610000e+02 -6.730000e+02  4.097000e+03 -4.000000e+00\n",
      "   1.400000e+01  4.000000e+00 -6.000000e+00  1.100000e+01 -5.000000e+00\n",
      "   7.000000e+00 -1.000000e+00  4.000000e+00 -4.530000e+02  5.000000e+00\n",
      "  -2.000000e+00  4.000000e+00  0.000000e+00  1.000000e+00 -1.830000e+02\n",
      "   0.000000e+00 -1.000000e+00  3.300000e+02 -2.017000e+03]] 입니다. w 계수들 \n",
      "Degree 3 MSE 는 1.12 입니다.\n",
      "\n",
      "Degree 4 회귀 계수는 [[  -408. -18279.    409. ...    195.     67.  -8479.]] 입니다. w 계수들 \n",
      "Degree 4 MSE 는 224.95 입니다.\n",
      "\n",
      "Degree 5 회귀 계수는 [[ 5. -0. -1. ...  3.  2.  0.]] 입니다. w 계수들 \n",
      "Degree 5 MSE 는 1933299.27 입니다.\n",
      "\n",
      "Degree 6 회귀 계수는 [[-0. -0. -0. ...  0.  0.  0.]] 입니다. w 계수들 \n",
      "Degree 6 MSE 는 799986213.03 입니다.\n",
      "\n",
      "Degree 7 회귀 계수는 [[-0.  0. -0. ...  0.  0. -0.]] 입니다. w 계수들 \n",
      "Degree 7 MSE 는 1326609498.94 입니다.\n",
      " 뒤로 갈 수록 overfitting\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "degrees = [1,2,3,4,5,6,7] #7차식까지 다항식을 만들어간다.\n",
    "\n",
    "# 다항 회귀의 차수(degree)를 1~7까지로 각각 변화시키면서 비교합니다.\n",
    "\n",
    "for i in range(len(degrees)):\n",
    "    # 개별 degree별로 Polynomial 변환합니다.\n",
    "    polynomial_features = PolynomialFeatures(degree=degrees[i], include_bias=False)\n",
    "    linear_regression = LinearRegression()\n",
    "    pipeline = Pipeline([(\"polynomial_features\", polynomial_features),\n",
    "                         (\"linear_regression\", linear_regression)])\n",
    "    pipeline.fit(X_data, y_target)\n",
    "    \n",
    "    # 교차 검증으로 다항 회귀를 평가합니다. \n",
    "    scores = cross_val_score(pipeline, X_data, y_target,scoring=\"neg_mean_squared_error\", cv=10)\n",
    "    coefficients = pipeline.named_steps['linear_regression'].coef_\n",
    "    print('\\nDegree {0} 회귀 계수는 {1} 입니다. w 계수들 '.format(degrees[i], np.round(coefficients),2))\n",
    "    print('Degree {0} MSE 는 {1:.2f} 입니다.'.format(degrees[i] , -1*np.mean(scores)))\n",
    "print(' 뒤로 갈 수록 overfitting')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Degree 2 MSE 는 0.53 입니다.\n",
    "# 2일때 가장 낮은 것"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>index</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  quality\n",
       "0      0        0\n",
       "1      1        0\n",
       "2      2        0\n",
       "3      3        0\n",
       "4      4        0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 4, 8, 9, 3], dtype=int64)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train['quality'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3302</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>3.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4287</th>\n",
       "      <td>8.80</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.096</td>\n",
       "      <td>22.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.99016</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216</th>\n",
       "      <td>7.80</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.31</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.046</td>\n",
       "      <td>28.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>0.99434</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>7.90</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.36</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0.058</td>\n",
       "      <td>31.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.99500</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5128</th>\n",
       "      <td>7.40</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.36</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.028</td>\n",
       "      <td>42.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.98930</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "3302           6.15              0.21         0.37             3.2      0.021   \n",
       "4287           8.80              0.39         0.35             1.8      0.096   \n",
       "1216           7.80              0.28         0.31             2.1      0.046   \n",
       "467            7.90              0.18         0.36             5.9      0.058   \n",
       "5128           7.40              0.28         0.36             1.1      0.028   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density   pH  sulphates  \\\n",
       "3302                 20.0                  80.0  0.99076  3.4        0.5   \n",
       "4287                 22.0                  80.0  0.99016  3.0        0.5   \n",
       "1216                 28.0                 208.0  0.99434  3.2        0.6   \n",
       "467                  31.0                 132.0  0.99500  3.2        0.5   \n",
       "5128                 42.0                 105.0  0.98930  3.0        0.4   \n",
       "\n",
       "      alcohol type  \n",
       "3302     12.0    0  \n",
       "4287     13.0    0  \n",
       "1216     10.0    0  \n",
       "467      11.0    0  \n",
       "5128     12.0    0  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.DataFrame(test['index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>index</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  quality\n",
       "0      0        0\n",
       "1      1        0\n",
       "2      2        0"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub['quality'] = 0\n",
    "sub.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub['quality'] = arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>index</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>995</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>996</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>997</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>998</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>999</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  quality\n",
       "0        0        5\n",
       "1        1        5\n",
       "2        2        6\n",
       "3        3        5\n",
       "4        4        6\n",
       "..     ...      ...\n",
       "995    995        5\n",
       "996    996        6\n",
       "997    997        5\n",
       "998    998        6\n",
       "999    999        6\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub.to_csv('./wine/just_rf2.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sub Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "test2 = test.drop('index', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 4, 8, 9, 3], dtype=int64)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train['quality'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Playdata\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "# Random forest\n",
    "\n",
    "model = RandomForestClassifier(n_estimators = 1)\n",
    "model.fit(X_train, y_train)\n",
    "model.predict(test2)\n",
    "rf = model.predict_proba(test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.02, 0.19, 0.36, ..., 0.09, 0.06, 0.  ],\n",
       "       [0.1 , 0.12, 0.36, ..., 0.02, 0.  , 0.  ],\n",
       "       [0.02, 0.01, 0.31, ..., 0.15, 0.01, 0.  ],\n",
       "       ...,\n",
       "       [0.  , 0.03, 0.62, ..., 0.03, 0.  , 0.  ],\n",
       "       [0.  , 0.01, 0.13, ..., 0.01, 0.01, 0.  ],\n",
       "       [0.06, 0.  , 0.33, ..., 0.18, 0.01, 0.  ]])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## find highest probabilty on one row, = that is expected quality of wine quality.  save best number in one row, save in new array, -> to_csv and submit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "arr = []\n",
    "for i in range(len(rf)) :\n",
    "    index = 0\n",
    "    index_max = np.argmax(rf[i]) + 3\n",
    "    arr.append(index_max)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### check columns of X_train, test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4397 entries, 3302 to 1289\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   fixed acidity         4397 non-null   float64\n",
      " 1   volatile acidity      4397 non-null   float64\n",
      " 2   citric acid           4397 non-null   float64\n",
      " 3   residual sugar        4397 non-null   float64\n",
      " 4   chlorides             4397 non-null   float64\n",
      " 5   free sulfur dioxide   4397 non-null   float64\n",
      " 6   total sulfur dioxide  4397 non-null   float64\n",
      " 7   density               4397 non-null   float64\n",
      " 8   pH                    4397 non-null   float64\n",
      " 9   sulphates             4397 non-null   float64\n",
      " 10  alcohol               4397 non-null   float64\n",
      " 11  type                  4397 non-null   object \n",
      "dtypes: float64(11), object(1)\n",
      "memory usage: 446.6+ KB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 13 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   index                 1000 non-null   int64  \n",
      " 1   fixed acidity         1000 non-null   float64\n",
      " 2   volatile acidity      1000 non-null   float64\n",
      " 3   citric acid           1000 non-null   float64\n",
      " 4   residual sugar        1000 non-null   float64\n",
      " 5   chlorides             1000 non-null   float64\n",
      " 6   free sulfur dioxide   1000 non-null   float64\n",
      " 7   total sulfur dioxide  1000 non-null   float64\n",
      " 8   density               1000 non-null   float64\n",
      " 9   pH                    1000 non-null   float64\n",
      " 10  sulphates             1000 non-null   float64\n",
      " 11  alcohol               1000 non-null   float64\n",
      " 12  type                  1000 non-null   object \n",
      "dtypes: float64(11), int64(1), object(1)\n",
      "memory usage: 101.7+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Playdata\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier \n",
    "rf_clf1 = RandomForestClassifier(n_estimators=250, max_depth=27, min_samples_leaf=2, \\\n",
    "                                 min_samples_split=3, random_state=0)\n",
    "rf_clf1.fit(X_train , y_train)\n",
    "pred = rf_clf1.predict(test2)\n",
    "rf_gridSearch = model.predict_proba(test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_rf_gridSearch = []\n",
    "for i in range(len(rf)) :\n",
    "    index = 0\n",
    "    index_max = np.argmax(rf[i]) + 3\n",
    "    arr_rf_gridSearch.append(index_max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "import time\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "#X_train, X_test, y_train, y_test = get_human_dataset()\n",
    "\n",
    "# GBM 수행 시간 측정을 위함. 시작 시간 설정.\n",
    "start_time = time.time()\n",
    "\n",
    "gb_clf = GradientBoostingClassifier(random_state=0)\n",
    "gb_clf.fit(X_train , y_train)\n",
    "gb_clf.predict(test2)\n",
    "gbm = gb_clf.predict_proba(test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_gbm = []\n",
    "for i in range(len(gbm)) :\n",
    "    index = 0\n",
    "    index_max = np.argmax(gbm[i]) + 3\n",
    "    arr_gbm.append(index_max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -> find out  GBM is not best for result so, submit random forest Grid Search GV\n",
    "# I submited to Dacon, accuracy was 0.698, rank 2. (and same accuracy with rank1)"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}