Red_Wine_Quality_Random Forest .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.svm import SVC\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_data = pd.read_csv('winequality-red.csv')\n",
    "wine_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.319637</td>\n",
       "      <td>0.527821</td>\n",
       "      <td>0.270976</td>\n",
       "      <td>2.538806</td>\n",
       "      <td>0.087467</td>\n",
       "      <td>15.874922</td>\n",
       "      <td>46.467792</td>\n",
       "      <td>0.996747</td>\n",
       "      <td>3.311113</td>\n",
       "      <td>0.658149</td>\n",
       "      <td>10.422983</td>\n",
       "      <td>5.636023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.741096</td>\n",
       "      <td>0.179060</td>\n",
       "      <td>0.194801</td>\n",
       "      <td>1.409928</td>\n",
       "      <td>0.047065</td>\n",
       "      <td>10.460157</td>\n",
       "      <td>32.895324</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>0.154386</td>\n",
       "      <td>0.169507</td>\n",
       "      <td>1.065668</td>\n",
       "      <td>0.807569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.990070</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.995600</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.996750</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.997835</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.900000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>1.003690</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
       "mean        8.319637          0.527821     0.270976        2.538806   \n",
       "std         1.741096          0.179060     0.194801        1.409928   \n",
       "min         4.600000          0.120000     0.000000        0.900000   \n",
       "25%         7.100000          0.390000     0.090000        1.900000   \n",
       "50%         7.900000          0.520000     0.260000        2.200000   \n",
       "75%         9.200000          0.640000     0.420000        2.600000   \n",
       "max        15.900000          1.580000     1.000000       15.500000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
       "mean      0.087467            15.874922             46.467792     0.996747   \n",
       "std       0.047065            10.460157             32.895324     0.001887   \n",
       "min       0.012000             1.000000              6.000000     0.990070   \n",
       "25%       0.070000             7.000000             22.000000     0.995600   \n",
       "50%       0.079000            14.000000             38.000000     0.996750   \n",
       "75%       0.090000            21.000000             62.000000     0.997835   \n",
       "max       0.611000            72.000000            289.000000     1.003690   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
       "mean      3.311113     0.658149    10.422983     5.636023  \n",
       "std       0.154386     0.169507     1.065668     0.807569  \n",
       "min       2.740000     0.330000     8.400000     3.000000  \n",
       "25%       3.210000     0.550000     9.500000     5.000000  \n",
       "50%       3.310000     0.620000    10.200000     6.000000  \n",
       "75%       3.400000     0.730000    11.100000     6.000000  \n",
       "max       4.010000     2.000000    14.900000     8.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1599 entries, 0 to 1598\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   fixed acidity         1599 non-null   float64\n",
      " 1   volatile acidity      1599 non-null   float64\n",
      " 2   citric acid           1599 non-null   float64\n",
      " 3   residual sugar        1599 non-null   float64\n",
      " 4   chlorides             1599 non-null   float64\n",
      " 5   free sulfur dioxide   1599 non-null   float64\n",
      " 6   total sulfur dioxide  1599 non-null   float64\n",
      " 7   density               1599 non-null   float64\n",
      " 8   pH                    1599 non-null   float64\n",
      " 9   sulphates             1599 non-null   float64\n",
      " 10  alcohol               1599 non-null   float64\n",
      " 11  quality               1599 non-null   int64  \n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 150.0 KB\n"
     ]
    }
   ],
   "source": [
    "wine_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    681\n",
       "6    638\n",
       "7    199\n",
       "4     53\n",
       "8     18\n",
       "3     10\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_data['quality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,10))\n",
    "\n",
    "plt.subplot(3,4,1)\n",
    "sns.barplot(x='quality',y='fixed acidity',data=wine_data)\n",
    "\n",
    "plt.subplot(3,4,2)\n",
    "sns.barplot(x='quality',y='volatile acidity',data=wine_data)\n",
    "\n",
    "plt.subplot(3,4,3)\n",
    "sns.barplot(x='quality',y='citric acid',data=wine_data)\n",
    "\n",
    "plt.subplot(3,4,4)\n",
    "sns.barplot(x='quality',y='residual sugar',data=wine_data)\n",
    "\n",
    "plt.subplot(3,4,5)\n",
    "sns.barplot(x='quality',y='chlorides',data=wine_data)\n",
    "\n",
    "plt.subplot(3,4,6)\n",
    "sns.barplot(x='quality',y='free sulfur dioxide',data=wine_data)\n",
    "\n",
    "plt.subplot(3,4,7)\n",
    "sns.barplot(x='quality',y='total sulfur dioxide',data=wine_data)\n",
    "\n",
    "plt.subplot(3,4,8)\n",
    "sns.barplot(x='quality',y='density',data=wine_data)\n",
    "\n",
    "plt.subplot(3,4,9)\n",
    "sns.barplot(x='quality',y='pH',data=wine_data)\n",
    "\n",
    "plt.subplot(3,4,10)\n",
    "sns.barplot(x='quality',y='sulphates',data=wine_data)\n",
    "\n",
    "plt.subplot(3,4,11)\n",
    "sns.barplot(x='quality',y='alcohol',data=wine_data)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = wine_data[wine_data.columns].corr()\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "sns.heatmap(corr, cmap=\"YlGnBu\", annot = True)\n",
    "plt.title('Heatmap for Correlation of Parameters')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from 2 to 6. it is considered bad and above that it is good as 8 is the max value of quality\n",
    "ranges = (2,6.5,8) \n",
    "groups = ['bad','good']\n",
    "wine_data['quality'] = pd.cut(wine_data['quality'], bins=ranges, labels=groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        0  \n",
       "1      9.8        0  \n",
       "2      9.8        0  \n",
       "3      9.8        0  \n",
       "4      9.4        0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "wine_data['quality'] = le.fit_transform(wine_data['quality'])\n",
    "wine_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1382\n",
       "1     217\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_data['quality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>7.6</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.071</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.99538</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.71</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.50</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.107</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.99790</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.93</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.073</td>\n",
       "      <td>23.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.99800</td>\n",
       "      <td>2.92</td>\n",
       "      <td>0.68</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1540</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.08</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.071</td>\n",
       "      <td>11.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.99646</td>\n",
       "      <td>3.56</td>\n",
       "      <td>0.63</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1472</th>\n",
       "      <td>7.6</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.60</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.073</td>\n",
       "      <td>23.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.99656</td>\n",
       "      <td>3.38</td>\n",
       "      <td>0.79</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>9.6</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.31</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.096</td>\n",
       "      <td>16.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.99820</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.70</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>5.9</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.08</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.071</td>\n",
       "      <td>16.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.99376</td>\n",
       "      <td>3.56</td>\n",
       "      <td>0.77</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.060</td>\n",
       "      <td>19.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.99170</td>\n",
       "      <td>3.72</td>\n",
       "      <td>0.74</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.370</td>\n",
       "      <td>0.32</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.062</td>\n",
       "      <td>15.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.99470</td>\n",
       "      <td>3.23</td>\n",
       "      <td>0.73</td>\n",
       "      <td>11.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>11.9</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.51</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.121</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.99960</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.76</td>\n",
       "      <td>10.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>434 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "855             7.6             0.735         0.02             2.5      0.071   \n",
       "365            10.0             0.420         0.50             3.4      0.107   \n",
       "657            12.0             0.500         0.59             1.4      0.073   \n",
       "1540            6.2             0.520         0.08             4.4      0.071   \n",
       "1472            7.6             0.350         0.60             2.6      0.073   \n",
       "...             ...               ...          ...             ...        ...   \n",
       "389             9.6             0.380         0.31             2.5      0.096   \n",
       "829             5.9             0.610         0.08             2.1      0.071   \n",
       "588             5.0             0.420         0.24             2.0      0.060   \n",
       "1440            7.2             0.370         0.32             2.0      0.062   \n",
       "418            11.9             0.380         0.51             2.0      0.121   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "855                  10.0                  14.0  0.99538  3.51       0.71   \n",
       "365                   7.0                  21.0  0.99790  3.26       0.93   \n",
       "657                  23.0                  42.0  0.99800  2.92       0.68   \n",
       "1540                 11.0                  32.0  0.99646  3.56       0.63   \n",
       "1472                 23.0                  44.0  0.99656  3.38       0.79   \n",
       "...                   ...                   ...      ...   ...        ...   \n",
       "389                  16.0                  49.0  0.99820  3.19       0.70   \n",
       "829                  16.0                  24.0  0.99376  3.56       0.77   \n",
       "588                  19.0                  50.0  0.99170  3.72       0.74   \n",
       "1440                 15.0                  28.0  0.99470  3.23       0.73   \n",
       "418                   7.0                  20.0  0.99960  3.24       0.76   \n",
       "\n",
       "      alcohol  quality  \n",
       "855      11.7        1  \n",
       "365      11.8        0  \n",
       "657      10.5        1  \n",
       "1540     11.6        0  \n",
       "1472     11.1        0  \n",
       "...       ...      ...  \n",
       "389      10.0        1  \n",
       "829      11.1        0  \n",
       "588      14.0        1  \n",
       "1440     11.3        1  \n",
       "418      10.4        0  \n",
       "\n",
       "[434 rows x 12 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good_quality = wine_data[wine_data['quality'] == 1]\n",
    "bad_quality = wine_data[wine_data['quality'] == 0]\n",
    "\n",
    "bad_quality = bad_quality.sample(frac=1)\n",
    "bad_quality = bad_quality[:len(good_quality)]\n",
    "\n",
    "new_data_frame = pd.concat([good_quality, bad_quality])\n",
    "new_data_frame = new_data_frame.sample(frac=1)\n",
    "new_data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    217\n",
       "0    217\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data_frame['quality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quality                 1.000000\n",
       "alcohol                 0.520021\n",
       "sulphates               0.383034\n",
       "citric acid             0.304747\n",
       "fixed acidity           0.133581\n",
       "residual sugar          0.069911\n",
       "pH                     -0.074334\n",
       "free sulfur dioxide    -0.123549\n",
       "chlorides              -0.150334\n",
       "total sulfur dioxide   -0.209833\n",
       "density                -0.216078\n",
       "volatile acidity       -0.435022\n",
       "Name: quality, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data_frame.corr()['quality'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = new_data_frame.drop('quality', axis=1)\n",
    "y = new_data_frame['quality']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:  {'n_estimators': 200}\n",
      "[[34  9]\n",
      " [ 7 37]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.79      0.81        43\n",
      "           1       0.80      0.84      0.82        44\n",
      "\n",
      "    accuracy                           0.82        87\n",
      "   macro avg       0.82      0.82      0.82        87\n",
      "weighted avg       0.82      0.82      0.82        87\n",
      "\n",
      "\n",
      "\n",
      "0.8160919540229885\n"
     ]
    }
   ],
   "source": [
    "param = {'n_estimators':[100,200,300,400,500,600,700,800,900,1000]}\n",
    "\n",
    "grid_rf = GridSearchCV(RandomForestClassifier(),param,scoring='accuracy',cv=10,)\n",
    "grid_rf.fit(X_train, y_train)\n",
    "\n",
    "print('Best parameters: ', grid_rf.best_params_)\n",
    "\n",
    "pred = grid_rf.predict(X_test)\n",
    "\n",
    "print(confusion_matrix(y_test,pred))\n",
    "print('\\n')\n",
    "print(classification_report(y_test,pred))\n",
    "print('\\n')\n",
    "print(accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Accuracy of the Classification: 81.60919540229885 %\n"
     ]
    }
   ],
   "source": [
    "print(\"The Accuracy of the Classification: \"+str(accuracy_score(y_test,pred)*100), \"%\")"
   ]
  }
 ],
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