Wine Quality Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f04ac3a5",
   "metadata": {},
   "source": [
    "# Project Name:- Wine Quality Prediction\n",
    "\n",
    "## Dhrupad Chakraborty"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c0c61d2",
   "metadata": {},
   "source": [
    "### 1. Importing the Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "bbbf7e18",
   "metadata": {},
   "outputs": [],
   "source": [
    "#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.model_selection import train_test_split\n",
    "from sklearn.ensemble._forest import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8821f66",
   "metadata": {},
   "source": [
    "### 2.Data Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "36d71b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load the dataset\n",
    "df=pd.read_csv('winequality-red.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "89da62f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "b7b3b95f",
   "metadata": {},
   "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": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "32ef6d6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "quality                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0252c0e3",
   "metadata": {},
   "source": [
    "### 3.Data Analysis and Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "ae79607e",
   "metadata": {},
   "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>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": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical measures of the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "19b3bcb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2cfcb13f040>"
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     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# number of values for each quality\n",
    "sns.catplot(x='quality',data=df,kind='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "12ca24ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='volatile acidity'>"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# volatile acidity vs Quality\n",
    "plot=plt.figure(figsize=(5,5))\n",
    "sns.barplot(x='quality',y='volatile acidity',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "a9b0b4c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='citric acid'>"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# citric acid vs Quality\n",
    "plot=plt.figure(figsize=(5,5))\n",
    "sns.barplot(x='quality',y='citric acid',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93dda0f6",
   "metadata": {},
   "source": [
    "### 4. Correlation\n",
    "\n",
    "#### 1.Positive Correlation\n",
    "#### 2.Negative Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "d10dad0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation=df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "0b2e86a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# constructing a heatmap to understand the correlation between the columns\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(correlation,cbar=True,square=True,fmt='.1f',annot=True,annot_kws={'size':8},cmap='Greens')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d0ef4e",
   "metadata": {},
   "source": [
    "### 5.Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "e4c8c0a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#seperate the data and Label\n",
    "X=df.drop('quality',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "ae05734b",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.700</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>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.880</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>0.99680</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.760</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>0.99700</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.280</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>0.99800</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.700</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>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1594</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.08</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.090</td>\n",
       "      <td>32.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.99490</td>\n",
       "      <td>3.45</td>\n",
       "      <td>0.58</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>5.9</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.062</td>\n",
       "      <td>39.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.99512</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0.76</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.076</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.99574</td>\n",
       "      <td>3.42</td>\n",
       "      <td>0.75</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>5.9</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.075</td>\n",
       "      <td>32.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.99547</td>\n",
       "      <td>3.57</td>\n",
       "      <td>0.71</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.47</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.067</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.99549</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.66</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1599 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0               7.4             0.700         0.00             1.9      0.076   \n",
       "1               7.8             0.880         0.00             2.6      0.098   \n",
       "2               7.8             0.760         0.04             2.3      0.092   \n",
       "3              11.2             0.280         0.56             1.9      0.075   \n",
       "4               7.4             0.700         0.00             1.9      0.076   \n",
       "...             ...               ...          ...             ...        ...   \n",
       "1594            6.2             0.600         0.08             2.0      0.090   \n",
       "1595            5.9             0.550         0.10             2.2      0.062   \n",
       "1596            6.3             0.510         0.13             2.3      0.076   \n",
       "1597            5.9             0.645         0.12             2.0      0.075   \n",
       "1598            6.0             0.310         0.47             3.6      0.067   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
       "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
       "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
       "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "...                   ...                   ...      ...   ...        ...   \n",
       "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
       "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
       "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
       "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
       "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
       "\n",
       "      alcohol  \n",
       "0         9.4  \n",
       "1         9.8  \n",
       "2         9.8  \n",
       "3         9.8  \n",
       "4         9.4  \n",
       "...       ...  \n",
       "1594     10.5  \n",
       "1595     11.2  \n",
       "1596     11.0  \n",
       "1597     10.2  \n",
       "1598     11.0  \n",
       "\n",
       "[1599 rows x 11 columns]"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaf944e0",
   "metadata": {},
   "source": [
    "#### 5.1 Label Binarizaton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "d96898f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=df['quality'].apply(lambda y_value: 1 if y_value >=7 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "51eafd2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "1594    0\n",
       "1595    0\n",
       "1596    0\n",
       "1597    0\n",
       "1598    0\n",
       "Name: quality, Length: 1599, dtype: int64"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d290c4c9",
   "metadata": {},
   "source": [
    "### 6.Train & Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "51dc5b46",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train , Y_test=train_test_split(X,Y,test_size=0.2,random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "0af51ed4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1279, 11)"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "fb1c2683",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(320, 11)"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "ed748c2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1279,)"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "acf29b38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(320,)"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "112a5102",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599,)"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33fa3639",
   "metadata": {},
   "source": [
    "### 7.Model Training:\n",
    "\n",
    "## Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "72b490b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "85b47498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20af76a4",
   "metadata": {},
   "source": [
    "### 8.Model Evaluation\n",
    "\n",
    "### Accuracy Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "21d9734c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# accuracy on test data\n",
    "X_test_pred=model.predict(X_test)\n",
    "test_data_accuracy=accuracy_score(X_test_pred,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "9049e197",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy:  0.93125\n"
     ]
    }
   ],
   "source": [
    "print('Model Accuracy: ',test_data_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e335fe6",
   "metadata": {},
   "source": [
    "### 9.Building a Predictive System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "f1d9fbe9",
   "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>909</th>\n",
       "      <td>7.3</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.059</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.99331</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0.52</td>\n",
       "      <td>11.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>7.5</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.078</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.99830</td>\n",
       "      <td>3.45</td>\n",
       "      <td>0.78</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1018</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.049</td>\n",
       "      <td>36.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.99007</td>\n",
       "      <td>2.89</td>\n",
       "      <td>0.44</td>\n",
       "      <td>12.7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>7.5</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.085</td>\n",
       "      <td>12.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.99680</td>\n",
       "      <td>3.38</td>\n",
       "      <td>0.62</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.415</td>\n",
       "      <td>14.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.99656</td>\n",
       "      <td>3.09</td>\n",
       "      <td>1.06</td>\n",
       "      <td>9.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "909             7.3             0.305         0.39             1.2      0.059   \n",
       "546             7.5             0.550         0.24             2.0      0.078   \n",
       "1018            8.0             0.180         0.37             0.9      0.049   \n",
       "62              7.5             0.520         0.16             1.9      0.085   \n",
       "754             7.8             0.480         0.68             1.7      0.415   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "909                   7.0                  11.0  0.99331  3.29       0.52   \n",
       "546                  10.0                  28.0  0.99830  3.45       0.78   \n",
       "1018                 36.0                 109.0  0.99007  2.89       0.44   \n",
       "62                   12.0                  35.0  0.99680  3.38       0.62   \n",
       "754                  14.0                  32.0  0.99656  3.09       1.06   \n",
       "\n",
       "      alcohol  quality  \n",
       "909      11.5        6  \n",
       "546       9.5        6  \n",
       "1018     12.7        6  \n",
       "62        9.5        7  \n",
       "754       9.1        6  "
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f73c0369",
   "metadata": {},
   "source": [
    "### Positive Result Checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "bc59c825",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n",
      "Good Quality Wine\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  message += \"Feature names unseen at fit time:\\n\"\n"
     ]
    }
   ],
   "source": [
    "# Positive Result\n",
    "\n",
    "input_data=(7.5,0.520,0.16,1.9,0.085,12.0,35.0,0.99680,3.38,0.62,9.5)\n",
    "\n",
    "#changing the input data to a numpy array\n",
    "\n",
    "input_data_as_np=np.asarray(input_data)\n",
    "\n",
    "#reshape the data \n",
    "\n",
    "input_data_reshaped=input_data_as_np.reshape(1,-1)\n",
    "\n",
    "prediction=model.predict(input_data_reshaped)\n",
    "\n",
    "print(prediction)\n",
    "\n",
    "if(prediction[0]==1):\n",
    "    print('Good Quality Wine')\n",
    "else:\n",
    "    print('Bad Quality Wine')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa1e6a95",
   "metadata": {},
   "source": [
    "### Negative Result Checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "942b50ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n",
      "Bad Quality Wine\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  message += \"Feature names unseen at fit time:\\n\"\n"
     ]
    }
   ],
   "source": [
    "# Negative Result\n",
    "\n",
    "input_data=(7.3,0.305,0.39,1.2,0.059,7.0,11.0,0.99331,3.29,0.52,11.5)\n",
    "\n",
    "#changing the input data to a numpy array\n",
    "\n",
    "input_data_as_np=np.asarray(input_data)\n",
    "\n",
    "#reshape the data \n",
    "\n",
    "input_data_reshaped=input_data_as_np.reshape(1,-1)\n",
    "\n",
    "prediction=model.predict(input_data_reshaped)\n",
    "\n",
    "print(prediction)\n",
    "\n",
    "if(prediction[0]==1):\n",
    "    print('Good Quality Wine')\n",
    "else:\n",
    "    print('Bad Quality Wine')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fc9776d",
   "metadata": {},
   "source": [
    "## Thank You"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e97b3f9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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