WineQualityPrediction.ipynb

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    "# WINE QUALITY PREDICTION\n",
    "\n",
    "<div style=\"text-align: center\">\n",
    "    <it>\n",
    "    Group Project for CS-503 \n",
    "    <br>\n",
    "        Machine Learning Course </it>\n",
    "        <br>  </b>\n",
    "</div>\n",
    "\n",
    "\n",
    "<div style=\"text-align: center\">\n",
    "    <b>\n",
    "    Submitted by <br>\n",
    "Anuj Rai & Ajay Gangwar  <br> </b>\n",
    "2019-21 AI   <br>\n",
    "       </div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Introduction**\n",
    "\n",
    "### Objective:-\n",
    "> Predicting the quality of wine based on chemical attributes\n",
    "\n",
    "\n",
    "## Description of the dataset:-\n",
    "source : https://www.kaggle.com/sgus1318/winedata?select=winequality_red.csv\n",
    "\n",
    "### Input variables:\n",
    "   \n",
    "1. fixed acidity :\n",
    "> a measurement of the total concentration of titratable acids and free hydrogen ions present in  the wine.Theoretically,having a low acidity will result in a flat and boring wine while having too much acid can lead to tartness or even a sour wine.These acids either occur naturally in the grapes or are created through the fermentation process.\n",
    "\n",
    "2. volatile acidity :\n",
    ">a measure of steam distillable acids present in a wine. In theory,our palates are quite       sensitiveto the presence of volatile acids and for that reason a goodwine should keep their concentrations as low as possible.\n",
    "\n",
    "3. citric acid :\n",
    ">one of the many acids that are measured too btained fixed acidity.\n",
    "\n",
    "4. residual sugar :\n",
    ">measurement of any natural grape sugarsthat are leftover after fermentation ceases.In theory residual sugar can help wines age well.\n",
    "\n",
    "5. chlorides :\n",
    ">the amount of salt in the wine.\n",
    "\n",
    "6. free sulfur dioxide :\n",
    ">the free form of SO2 exists inequilibrium between molecular SO2 (as a dissolved gas)and  bisulfite ion;it prevents microbial growth and theoxidation of wine.\n",
    "\n",
    "7. total sulfur dioxide :\n",
    ">amount of free and bound forms ofSO2; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine.\n",
    "\n",
    "8. density:\n",
    ">measure of density of wine.\n",
    "\n",
    "9. pH :\n",
    ">value for pH.\n",
    "\n",
    "10. sulphates :\n",
    ">a wine additive which can contribute to sulfurdioxide gas(S02) levels, which acts as an antimicrobial and antioxidant.\n",
    "\n",
    "11. alcohol :\n",
    ">the percentage of alcohol present in the wine.\n",
    "\n",
    "### Output variable:\n",
    "\n",
    "12. quality : 1(Good) or 0(Bad)\n"
   ]
  },
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   "metadata": {},
   "source": [
    "## **Related Works**\n",
    "Wine Quality dataset is a popular dataset. It has widely been used by researchers and scholars for studying supervised learning methods. <br>\n",
    "It has red wine and white wine quality data. We have used red wine dataset here. <br>\n",
    "The popular problem is to classify wine as whether it is red wine or white wine. This problem has been solved up to the accuracy of 99.5% with the help of Random forests method."
   ]
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  {
   "cell_type": "code",
   "execution_count": 118,
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   "source": [
    "#Importing all necessary library functions\n",
    "from __future__ import print_function\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score, auc,roc_curve,f1_score,classification_report, plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
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       "  <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",
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       "      <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",
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       "    <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",
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      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   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": 97,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data=pd.read_csv(\"winequality-red.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
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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",
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      "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  "
      ]
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     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observation:-\n",
    "1. Some wines are really weak i.e. alcohol<10.\n",
    "2. Some wines are really salty i.e chlorides >0.06.\n",
    "3. the fermentation is not over for some wines i.e density >1.\n",
    "4. Some wines are much more acidic than others as mean is 3.31 and max is 4.01\n",
    "5. Some wines are really sweet i.e residual sugar > 4.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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": 99,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data['quality'].value_counts()"
   ]
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  {
   "cell_type": "code",
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   "outputs": [
    {
     "data": {
      "image/png": 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eP0vygXH3NWpJ/rpl1yNJbkvyqqH+/KN1zj1JgJOq6udJjge+Bry/qu4bc2sjleSDwCRwclW9c9z9LIckTwCTVXVMPNySZDvwn1V1c7vj7MSqenbcfS2H9vUlP2T2wccfjLufUUmyitnMelNV/U+S24G7q+pTwzrGUXvlXrN+3jaPb6+j85NqQElWA5cBN4+7F41GkpOBtwK3AFTVr46VYG82AN/vOdjnWAG8OskK4ESG/DzQURvu8NspioeA/cDOqrp/3D2N2MeADwG/GXcjy6yAryR5oH1tRc9eD8wAn2zTbzcnOWncTS2jq4Dbxt3EqFXVD4F/BJ4EngKeq6qvDPMYR3W4V9Wvq+pcZp+CvSDJH427p1FJ8k5gf1U9MO5exuDCqjqf2W8avS7JW8fd0AitAM4Hbqqq84BfAFvH29LyaFNQlwOfH3cvo5bkFGa/UPFs4PeBk5L85TCPcVSH+wHt19Z7gYvH3MooXQhc3uafPwtclORfx9vS8qiqfW25H7iT2W8e7dU0MD3nt9A7mA37Y8ElwDer6ulxN7IM3gb8V1XNVNX/AV8A/nSYBzhqwz3JRJKVbf3VzP7D+u54uxqdqvpwVa2uqrXM/up6T1UN9ZP+5SjJSUlec2AdeAfwyHi7Gp2q+hGwN8k5rbQB+M4YW1pOV3MMTMk0TwLrk5zYbg7ZAOwe5gGO5v9B9lnA9vbX9VcAt1fVMXN74DHkTODO2f/+WQF8pqq+PN6WRu59wKfbNMXjwLVj7mfkkpwIvB14z7h7WQ5VdX+SO4BvAi8ADzLkryE4am+FlCQd3lE7LSNJOjzDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXo/wHlpFYQz8C4lQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['quality'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation:-\n",
    "1. Most of the wines are rated between 5 and 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12.0, 0.0)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation matrix\n",
    "plt.figure(figsize=(14,14))\n",
    "\n",
    "mp=sns.heatmap(data.corr(), annot = True,cmap='coolwarm',\n",
    "            vmin=-1, vmax=1, center=0,linewidths=0.2,linecolor='black',\n",
    "            cbar=True,square=True)\n",
    "bottom, top = mp.get_ylim()\n",
    "mp.set_ylim(bottom + 0.5, top - 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observations:-\n",
    "\n",
    "1. The wine quality is highly correlated to the alcohol and the volatile acidity.\n",
    "2. quality and alcohol are positively correlated.\n",
    "3. volatile acidity and quality are negatively correlated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'fixed acidity', data = data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'volatile acidity', data = data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'citric acid', data = data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'residual sugar', data = data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAFzCAYAAAB7Ha4BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAdTElEQVR4nO3de7TdZX3n8feHhIAwchHONC2XCVXsTBwZL8fYlopWKg2tkqkTamhtweVaTFVax47N4GoHLdU/jBe6pmJHFC2KCpTWrrRmRFpX1fFCc0AqBkx7miI5wVPCcFFUxJDv/LF/qYfjgbOD+WU/Oef9Wmuv/fs9v+fZ+5u9ssKH53d5UlVIkiSpDQeNugBJkiR9n+FMkiSpIYYzSZKkhhjOJEmSGmI4kyRJaojhTJIkqSFLR13AvnLsscfWihUrRl2GJEnSvG688ca7q2psrmMLJpytWLGCiYmJUZchSZI0ryRfe7RjntaUJElqiOFMkiSpIYYzSZKkhhjOJEmSGmI4kyRJaojhTJIkqSGGM0mSpIYYziRJkhpiOJMkSWqI4UySJKkhhjNJkqSGGM4kSZIasmAWPj9QrV+/nunpaZYvX86GDRtGXY4kSRoxw9mITU9Ps2PHjlGXIUmSGuFpTUmSpIYYziRJkhpiOJMkSWpIr+EsyeokW5NMJrlwjuOnJbkpya4ka2cdOzHJJ5PcluTWJCv6rFWSJKkFvYWzJEuAS4EzgZXAOUlWzup2B3Ae8JE5PuKDwNuq6j8Aq4C7+qpVkiSpFX3erbkKmKyqbQBJrgLWALfu6VBVt3fHds8c2IW4pVV1fdfvgR7rlCRJakafpzWPA7bP2J/q2obxVOC+JH+e5EtJ3tbNxD1CkvOTTCSZ2Llz5z4oWZIkabT6DGeZo62GHLsUeB7weuA5wI8zOP35yA+ruqyqxqtqfGxs7PHWKUmS1Iw+w9kUcMKM/eOBO/di7JeqaltV7QL+AnjWPq5PkiSpOX2Gs83AyUlOSrIMWAds3IuxRyfZMx32QmZcqyZJkrRQ9RbOuhmvC4DrgNuAa6pqS5KLk5wFkOQ5SaaAs4H3JNnSjX2YwSnNv0lyC4NTpO/tq1ZJkqRW9Lq2ZlVtAjbNartoxvZmBqc75xp7PXBKn/VJkiS1xhUCJEmSGmI4kyRJaojhTJIkqSGGM0mSpIYYziRJkhpiOJMkSWqI4UySJKkhhjNJkqSGGM4kSZIaYjiTJElqiOFMkiSpIYYzSZKkhhjOJEmSGmI4kyRJaojhTJIkqSGGM0mSpIYYziRJkhpiOJMkSWqI4UySJKkhhjNJkqSGGM4kSZIaYjiTJElqiOFMkiSpIYYzSZKkhhjOJEmSGmI4kyRJaojhTJIkqSGGM0mSpIYYziRJkhrSazhLsjrJ1iSTSS6c4/hpSW5KsivJ2jmOH5FkR5J39VmnJElSK3oLZ0mWAJcCZwIrgXOSrJzV7Q7gPOAjj/IxfwB8uq8aJUmSWtPnzNkqYLKqtlXVQ8BVwJqZHarq9qr6MrB79uAkzwZ+BPhkjzVKkiQ1pc9wdhywfcb+VNc2ryQHAe8AfmeefucnmUgysXPnzsddqCRJUiv6DGeZo62GHPtqYFNVbX+sTlV1WVWNV9X42NjYXhcoSZLUmqU9fvYUcMKM/eOBO4cc+1PA85K8Gvg3wLIkD1TVD9xUIEmStJD0Gc42AycnOQnYAawDfmWYgVX1q3u2k5wHjBvMJEnSYtDbac2q2gVcAFwH3AZcU1Vbklyc5CyAJM9JMgWcDbwnyZa+6pEkSToQ9DlzRlVtAjbNartoxvZmBqc7H+sz/gT4kx7KkyRJao4rBEiSJDXEcCZJktQQw5kkSVJDDGeSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRwJkmS1BDDmSRJUkMMZ5IkSQ0xnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRwJkmS1BDDmSRJUkMMZ5IkSQ0xnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkNWTrqAva3Z//OB0ddwiM88e5vsgS44+5vNlXbjW/79VGXIEnSotTrzFmS1Um2JplMcuEcx09LclOSXUnWzmh/RpIvJNmS5MtJXtZnnZIkSa3oLZwlWQJcCpwJrATOSbJyVrc7gPOAj8xq/zbw61X1NGA18IdJjuqrVkmSpFb0eVpzFTBZVdsAklwFrAFu3dOhqm7vju2eObCq/mHG9p1J7gLGgPt6rFeSJGnk+jyteRywfcb+VNe2V5KsApYB/zTHsfOTTCSZ2Llz5+MuVJIkqRV9hrPM0VZ79QHJjwIfAl5RVbtnH6+qy6pqvKrGx8bGHmeZkiRJ7egznE0BJ8zYPx64c9jBSY4APg78XlV9cR/XJkmS1KQ+w9lm4OQkJyVZBqwDNg4zsOv/MeCDVfWnPdYoSZLUlN7CWVXtAi4ArgNuA66pqi1JLk5yFkCS5ySZAs4G3pNkSzf8l4HTgPOS3Ny9ntFXrZIkSa3o9SG0VbUJ2DSr7aIZ25sZnO6cPe5K4Mo+a5MkSWqRyzdJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDen1hgCpRevXr2d6eprly5ezYcOGUZcjSdIjGM606ExPT7Njx45RlyFJ0pw8rSlJktQQw5kkSVJDDGeSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRwJkmS1BDDmSRJUkMMZ5IkSQ0xnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRwJkmS1BDDmSRJUkMMZ5IkSQ3pNZwlWZ1ka5LJJBfOcfy0JDcl2ZVk7axj5yb5x+51bp91SpIktaK3cJZkCXApcCawEjgnycpZ3e4AzgM+Mmvsk4A3As8FVgFvTHJ0X7VKkiS1os+Zs1XAZFVtq6qHgKuANTM7VNXtVfVlYPessT8PXF9V91TVvcD1wOoea5UkSWpCn+HsOGD7jP2prm2fjU1yfpKJJBM7d+583IVKkiS1os9wljnaal+OrarLqmq8qsbHxsb2qjhJkqQW9RnOpoATZuwfD9y5H8ZKkiQdsPoMZ5uBk5OclGQZsA7YOOTY64Azkhzd3QhwRtcmSZK0oPUWzqpqF3ABg1B1G3BNVW1JcnGSswCSPCfJFHA28J4kW7qx9wB/wCDgbQYu7tokSZIWtKV9fnhVbQI2zWq7aMb2ZganLOca+37g/X3WJ2n/WL9+PdPT0yxfvpwNGzaMuhxJalqv4UySAKanp9mxY8eoy5CkA8JQpzWTbEhyRJKDk/xNkruTvLzv4iRJkhabYa85O6OqvgG8mMGdlE8Ffqe3qiRJkhapYcPZwd37LwAf9eJ8SZKkfgx7zdlfJvkq8B3g1UnGgAf7K0uSJGlxGmrmrKouBH4KGK+q7wHfZtY6mZIkSfrhDXtDwGHAa4A/7pp+DBjvqyhJkqTFathrzj4APAT8dLc/Bby5l4okSZIWsWGvOXtyVb0syTkAVfWdJHMtTi6pAaf+0amjLuERlt23jIM4iO33bW+qts/95udGXYIk/YBhZ84eSvIEoACSPBn4bm9VSZIkLVLDzpy9EfgEcEKSDwOnAuf1VZQkSdJiNVQ4q6rrk9wE/CQQ4LVVdXevlUmSJC1CjxnOkjxrVtPXu/cTk5xYVTf1U5YWkjsufvqoS3iEXfc8CVjKrnu+1lRtJ150y6hLkCQ1YL6Zs3d074cyeHTG3zOYOTsFuAH4mf5KkyRJWnwe84aAqvrZqvpZ4GvAs6pqvKqeDTwTmNwfBS50u5cdzsOHHMHuZYePuhRJktSAYW8I+PdV9a/nXKrqK0me0VNNi8q3Tj5j1CVIkqSGDBvObkvyPuBKBo/TeDlwW29VSVpQ6rBiN7upw2rUpSwa69evZ3p6muXLl7Nhw4ZRlyNpLwwbzl4BvAp4bbf/Gb6/lJMkPabvnfq9UZew6ExPT7Njx45RlyHpcRj2URoPApd0L0mSJPVkvkdpXFNVv5zkFrrVAWaqqlN6q0ySJGkRmm/mbM9pzBf3XYgkSZLmCWdV9fUkS4DLq+rn9lNNkiRJi9a8C59X1cPAt5McuR/qkSRJWtSGvVvzQeCWJNcD39rTWFW/1UtVknSA+fRpzx91CY/wnaVLIOE7U1NN1fb8z3x61CVIzRs2nH28e0mSJKlHwz5K44oky4Cndk1bq8oHF0mSJO1jQ4WzJC8ArgBuZ7Dw+QlJzq2qz/RXmiRJ0uIz7GnNdwBnVNVWgCRPBT4KPLuvwiRJkhajee/W7By8J5gBVNU/AAf3U5IkSdLiNWw4m0hyeZIXdK/3AjfONyjJ6iRbk0wmuXCO44ckubo7fkOSFV37wUmuSHJLktuSvGFv/lCStNgdVcWTqjiqXGxeOtAMe1rzVcBrgN9icM3ZZ4B3P9aA7uG1lwIvAqaAzUk2VtWtM7q9Eri3qp6SZB3wVuBlwNnAIVX19CSHAbcm+WhV3T78H02SFq+XP7x71CUsOuvXr2d6eprly5ezYcOGUZejA9iwd2t+F3hn9xrWKmCyqrYBJLkKWAPMDGdrgDd129cC70oSBut4Hp5kKfAE4CHgG3vx3ZIk7VfT09Ps2LFj1GVoAZhv4fM5FzzfY56Fz48Dts/YnwKe+2h9qmpXkvuBYxgEtTXA14HDgNdV1T1z1Hc+cD7AiSee+Fh/FEmSpAPCfDNnP8yC55mjbXbQe7Q+q4CHgR8DjgY+m+Sv98zC/WvHqsuAywDGx8e9sEKSJB3w5lv4/Gs/xGdPASfM2D8euPNR+kx1pzCPBO4BfgX4RPeg27uSfA4YB7YhSZK0gA11t2aSlyb5xyT3J/lGkm8mme8asM3AyUlO6lYXWAdsnNVnI3But70W+FRVFXAH8MIMHA78JPDVYf9QkiRJB6ph79bcALykqm4b9oO7a8guAK4DlgDvr6otSS4GJqpqI3A58KEkkwxmzNZ1wy8FPgB8hcGpzw9U1ZeH/W5JkqQD1bDh7F/2JpjtUVWbgE2z2i6asf0gg8dmzB73wFztkiRJC918d2u+tNucSHI18BfAd/ccr6o/77E2SZKkRWe+mbOXdO8FfBs4Y8axAgxnkqSReNd//8tRl/AI9939rX99b6m2C97xkvk7qSnz3a35CoAkVwCvrar7uv2jGSyGLkmSpH1o2GvOTtkTzACq6t4kz+ypJqlXxx66G9jVvUuS1JZhw9lBSY6uqnsBkjxpL8ZKTXn9KffN30mSpBEZNmC9A/h8kmsZXGv2y8BbeqtKkiRpkRp24fMPJpkAXsjguWMvrapb5xkmSZLUm/Xr1zM9Pc3y5cvZsGHDqMvZZ4Y+NdmFMQOZJElqwvT0NDt27Bh1GfvcUMs3SZIkaf/won5JkvaBw5cd8Yh36fEynEmStA+c+uSXzt9JGoKnNSVJkhpiOJMkSWqI4UySJKkhhjNJkqSGGM4kSZIa4t2akiRpKG95+dpRl/AI99x1/+B9+utN1fa7V177Q4135kySJKkhhjNJkqSGGM4kSZIaYjiTJElqiOFMkiSpIYYzSZKkhhjOJEmSGmI4kyRJaojhTJIkqSGGM0mSpIa4fJMkSTogHbrkoEe8LxSGM0mSdEB65jFPHHUJveg1aiZZnWRrkskkF85x/JAkV3fHb0iyYsaxU5J8IcmWJLckObTPWiVJklrQWzhLsgS4FDgTWAmck2TlrG6vBO6tqqcAlwBv7cYuBa4EfqOqnga8APheX7VKkiS1os+Zs1XAZFVtq6qHgKuANbP6rAGu6LavBU5PEuAM4MtV9fcAVfX/qurhHmuVJElqQp/h7Dhg+4z9qa5tzj5VtQu4HzgGeCpQSa5LclOS9XN9QZLzk0wkmdi5c+c+/wNIkiTtb32Gs8zRVkP2WQr8DPCr3fsvJTn9BzpWXVZV41U1PjY29sPWK0mSNHJ9hrMp4IQZ+8cDdz5an+46syOBe7r2T1fV3VX1bWAT8Kwea5UkSWpCn+FsM3BykpOSLAPWARtn9dkInNttrwU+VVUFXAeckuSwLrQ9H7i1x1olSZKa0NtzzqpqV5ILGAStJcD7q2pLkouBiaraCFwOfCjJJIMZs3Xd2HuTvJNBwCtgU1V9vK9aJUmSWtHrQ2irahODU5Iz2y6asf0gcPajjL2SweM0JEmSFo2Ftd6BJEnSAc5wJkmS1BDDmSRJUkMMZ5IkSQ0xnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRwJkmS1BDDmSRJUkMMZ5IkSQ0xnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRwJkmS1BDDmSRJUkMMZ5IkSQ0xnEmSJDWk13CWZHWSrUkmk1w4x/FDklzdHb8hyYpZx09M8kCS1/dZpyRJUit6C2dJlgCXAmcCK4Fzkqyc1e2VwL1V9RTgEuCts45fAvyfvmqUJElqTZ8zZ6uAyaraVlUPAVcBa2b1WQNc0W1fC5yeJABJ/jOwDdjSY42SJElN6TOcHQdsn7E/1bXN2aeqdgH3A8ckORz4H8DvP9YXJDk/yUSSiZ07d+6zwiVJkkalz3CWOdpqyD6/D1xSVQ881hdU1WVVNV5V42NjY4+zTEmSpHYs7fGzp4ATZuwfD9z5KH2mkiwFjgTuAZ4LrE2yATgK2J3kwap6V4/1SpIkjVyf4WwzcHKSk4AdwDrgV2b12QicC3wBWAt8qqoKeN6eDkneBDxgMJMkSYtBb+GsqnYluQC4DlgCvL+qtiS5GJioqo3A5cCHkkwymDFb11c9kiRJB4I+Z86oqk3AplltF83YfhA4e57PeFMvxUmSJDXIFQIkSZIaYjiTJElqiOFMkiSpIYYzSZKkhhjOJEmSGmI4kyRJaojhTJIkqSGGM0mSpIYYziRJkhpiOJMkSWqI4UySJKkhhjNJkqSGGM4kSZIaYjiTJElqiOFMkiSpIYYzSZKkhhjOJEmSGmI4kyRJaojhTJIkqSGGM0mSpIYYziRJkhpiOJMkSWqI4UySJKkhhjNJkqSGGM4kSZIaYjiTJElqiOFMkiSpIYYzSZKkhvQazpKsTrI1yWSSC+c4fkiSq7vjNyRZ0bW/KMmNSW7p3l/YZ52SJEmt6C2cJVkCXAqcCawEzkmycla3VwL3VtVTgEuAt3btdwMvqaqnA+cCH+qrTkmSpJb0OXO2Cpisqm1V9RBwFbBmVp81wBXd9rXA6UlSVV+qqju79i3AoUkO6bFWSZKkJvQZzo4Dts/Yn+ra5uxTVbuA+4FjZvX5L8CXquq7PdUpSZLUjKU9fnbmaKu96ZPkaQxOdZ4x5xck5wPnA5x44omPr0pJkqSG9DlzNgWcMGP/eODOR+uTZClwJHBPt3888DHg16vqn+b6gqq6rKrGq2p8bGxsH5cvSZK0//UZzjYDJyc5KckyYB2wcVafjQwu+AdYC3yqqirJUcDHgTdU1ed6rFGSJKkpvYWz7hqyC4DrgNuAa6pqS5KLk5zVdbscOCbJJPDbwJ7HbVwAPAX4n0lu7l7/tq9aJUmSWtHnNWdU1SZg06y2i2ZsPwicPce4NwNv7rM2SZKkFrlCgCRJUkMMZ5IkSQ0xnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRwJkmS1BDDmSRJUkMMZ5IkSQ0xnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRwJkmS1BDDmSRJUkMMZ5IkSQ0xnEmSJDXEcCZJktQQw5kkSVJDeg1nSVYn2ZpkMsmFcxw/JMnV3fEbkqyYcewNXfvWJD/fZ52SJEmt6C2cJVkCXAqcCawEzkmycla3VwL3VtVTgEuAt3ZjVwLrgKcBq4F3d58nSZK0oPU5c7YKmKyqbVX1EHAVsGZWnzXAFd32tcDpSdK1X1VV362qfwYmu8+TJEla0PoMZ8cB22fsT3Vtc/apql3A/cAxQ46VJElacJb2+NmZo62G7DPMWJKcD5zf7T6QZOteVdiOY4G7R13ETHn7uaMuoW/N/ea8ca6/9gtKc795fsvffL+Lv/n+9pvvHHUFvWvuN/+9Dw/19/zfPdqBPsPZFHDCjP3jgTsfpc9UkqXAkcA9Q46lqi4DLtuHNY9EkomqGh91HYuJv/n+52++//mb73/+5vvfQvzN+zytuRk4OclJSZYxuMB/46w+G4E9UzRrgU9VVXXt67q7OU8CTgb+rsdaJUmSmtDbzFlV7UpyAXAdsAR4f1VtSXIxMFFVG4HLgQ8lmWQwY7auG7slyTXArcAu4DVV9XBftUqSJLWiz9OaVNUmYNOstotmbD8InP0oY98CvKXP+hpywJ+aPQD5m+9//ub7n7/5/udvvv8tuN88g7OIkiRJaoHLN0mSJDXEcDZCSQ5N8ndJ/j7JliS/P+qaFoMkS5J8KclfjbqWxSLJ7UluSXJzkolR17MYJDkqybVJvprktiQ/NeqaFrIkP9H9/d7z+kaS/zbquhayJK/r/tv5lSQfTXLoqGvaVzytOULdagiHV9UDSQ4G/i/w2qr64ohLW9CS/DYwDhxRVS8edT2LQZLbgfGqaupZRAtZkiuAz1bV+7o75g+rqvtGXddi0C03uAN4blV9bdT1LERJjmPw38yVVfWd7ibCTVX1J6OtbN9w5myEauCBbvfg7mVa7lGS44FfBN436lqkviQ5AjiNwR3xVNVDBrP96nTgnwxmvVsKPKF7TuphzPE81AOV4WzEulNsNwN3AddX1Q2jrmmB+0NgPbB71IUsMgV8MsmN3coe6tePAzuBD3Sn8N+X5PBRF7WIrAM+OuoiFrKq2gG8HbgD+Dpwf1V9crRV7TuGsxGrqoer6hkMVkFYleQ/jrqmhSrJi4G7qurGUdeyCJ1aVc8CzgRek+S0URe0wC0FngX8cVU9E/gWcOFoS1oculPIZwF/OupaFrIkRwNrgJOAHwMOT/Ly0Va17xjOGtGdcvhbYPWIS1nITgXO6q5/ugp4YZIrR1vS4lBVd3bvdwEfA1aNtqIFbwqYmjETfy2DsKb+nQncVFX/MupCFrifA/65qnZW1feAPwd+esQ17TOGsxFKMpbkqG77CQz+sn11tFUtXFX1hqo6vqpWMDjt8KmqWjD/p9WqJIcneeKebeAM4CujrWphq6ppYHuSn+iaTmew4or6dw6e0twf7gB+Mslh3c11pwO3jbimfabXFQI0rx8Fruju7DkIuKaqfLyDFpofAT42+PeTpcBHquoToy1pUfhN4MPdabZtwCtGXM+Cl+Qw4EXAfx11LQtdVd2Q5FrgJgbLPH6JBbRSgI/SkCRJaoinNSVJkhpiOJMkSWqI4UySJKkhhjNJkqSGGM4kSZIaYjiTpCEkWZHkK932eJL/1W2/IMmCefilpNHzOWeStJeqagKY6HZfADwAfH5kBUlaUJw5k7TgJfndJFuT/HWSjyZ5fZK/TTLeHT+2W9ZrzwzZZ5Pc1L1+YFasmy37qyQrgN8AXpfk5iTPS/LPSQ7u+h2R5PY9+5I0DGfOJC1oSZ7NYLmuZzL4N+8m4MbHGHIX8KKqejDJyQyW4hmfq2NV3Z7kfwMPVNXbu+/7W+AXgb/ovvfPurX/JGkozpxJWuieB3ysqr5dVd8ANs7T/2DgvUluAf4UWLmX3/c+vr9U0iuAD+zleEmLnDNnkhaDudap28X3/wf10BntrwP+BfhP3fEH9+qLqj7XnRp9PrCkqlzkXdJeceZM0kL3GeCXkjwhyROBl3TttwPP7rbXzuh/JPD1qtoN/BqwZJ7P/ybwxFltH2RwOtRZM0l7zXAmaUGrqpuAq4GbgT8DPtsdejvwqiSfB46dMeTdwLlJvgg8FfjWPF/xlwzC381Jnte1fRg4mkFAk6S9kqq5ZvslaWFK8iZmXMDf03esBdZU1a/19R2SFi6vOZOkfSjJHwFnAr8w6lokHZicOZMkSWqI15xJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRwJkmS1JD/D6PLSy6rQm1SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'chlorides', data = data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'free sulfur dioxide', data = data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23840eafb08>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'total sulfur dioxide', data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'sulphates', data = data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23840fb0588>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'alcohol', data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'density', data = data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'pH', data = data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews = []\n",
    "for i in data['quality']:\n",
    "    if i <= 5:\n",
    "        reviews.append('1')\n",
    "#     elif i >= 4 and i <= 7:\n",
    "#         reviews.append('2')\n",
    "    else:\n",
    "        reviews.append('2')\n",
    "data['Reviews'] = reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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.countplot(data['Reviews']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    855\n",
       "1    744\n",
       "Name: Reviews, dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Reviews'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here we have converted a 10 class dataset into 2 class dataset, because it was a biased dataset.**\n",
    "\n",
    "**Converting it into two classes which do nto have much difference to create a bias.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.iloc[:,:10]\n",
    "y = data['Reviews']\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code for plotting classification report in form of a heatmap\n",
    "\n",
    "def show_values(pc, fmt=\"%.2f\", **kw):\n",
    "    '''\n",
    "    Heatmap with text in each cell with matplotlib's pyplot\n",
    "    Source: https://stackoverflow.com/a/25074150/395857 \n",
    "    By HYRY\n",
    "    '''\n",
    "    pc.update_scalarmappable()\n",
    "    ax = pc.axes\n",
    "    #ax = pc.axes# FOR LATEST MATPLOTLIB\n",
    "    #Use zip BELOW IN PYTHON 3\n",
    "    for p, color, value in zip(pc.get_paths(), pc.get_facecolors(), pc.get_array()):\n",
    "        x, y = p.vertices[:-2, :].mean(0)\n",
    "        if np.all(color[:3] > 0.5):\n",
    "            color = (0.0, 0.0, 0.0)\n",
    "        else:\n",
    "            color = (1.0, 1.0, 1.0)\n",
    "        ax.text(x, y, fmt % value, ha=\"center\", va=\"center\", color=color, **kw)\n",
    "\n",
    "\n",
    "def cm2inch(*tupl):\n",
    "    '''\n",
    "    Specify figure size in centimeter in matplotlib\n",
    "    Source: https://stackoverflow.com/a/22787457/395857\n",
    "    By gns-ank\n",
    "    '''\n",
    "    inch = 2.54\n",
    "    if type(tupl[0]) == tuple:\n",
    "        return tuple(i/inch for i in tupl[0])\n",
    "    else:\n",
    "        return tuple(i/inch for i in tupl)\n",
    "\n",
    "\n",
    "def heatmap(AUC, title, xlabel, ylabel, xticklabels, yticklabels, figure_width=40, figure_height=20, correct_orientation=False, cmap='RdBu'):\n",
    "    '''\n",
    "    Inspired by:\n",
    "    - https://stackoverflow.com/a/16124677/395857 \n",
    "    - https://stackoverflow.com/a/25074150/395857\n",
    "    '''\n",
    "\n",
    "    # Plot it out\n",
    "    fig, ax = plt.subplots()    \n",
    "    #c = ax.pcolor(AUC, edgecolors='k', linestyle= 'dashed', linewidths=0.2, cmap='RdBu', vmin=0.0, vmax=1.0)\n",
    "    c = ax.pcolor(AUC, edgecolors='k', linestyle= 'dashed', linewidths=0.2, cmap=cmap)\n",
    "\n",
    "    # put the major ticks at the middle of each cell\n",
    "    ax.set_yticks(np.arange(AUC.shape[0]) + 0.5, minor=False)\n",
    "    ax.set_xticks(np.arange(AUC.shape[1]) + 0.5, minor=False)\n",
    "\n",
    "    # set tick labels\n",
    "    #ax.set_xticklabels(np.arange(1,AUC.shape[1]+1), minor=False)\n",
    "    ax.set_xticklabels(xticklabels, minor=False)\n",
    "    ax.set_yticklabels(yticklabels, minor=False)\n",
    "\n",
    "    # set title and x/y labels\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)      \n",
    "\n",
    "    # Remove last blank column\n",
    "    plt.xlim( (0, AUC.shape[1]) )\n",
    "\n",
    "    # Turn off all the ticks\n",
    "    ax = plt.gca()    \n",
    "    for t in ax.xaxis.get_major_ticks():\n",
    "        t.tick1line.set_visible = False\n",
    "        t.tick2line.set_visible = False\n",
    "    for t in ax.yaxis.get_major_ticks():\n",
    "        t.tick1line.set_visible = False\n",
    "        t.tick2line.set_visible = False\n",
    "\n",
    "    # Add color bar\n",
    "    plt.colorbar(c)\n",
    "\n",
    "    # Add text in each cell \n",
    "    show_values(c)\n",
    "\n",
    "    # Proper orientation (origin at the top left instead of bottom left)\n",
    "    if correct_orientation:\n",
    "        ax.invert_yaxis()\n",
    "        ax.xaxis.tick_top()       \n",
    "\n",
    "    # resize \n",
    "    fig = plt.gcf()\n",
    "    fig.set_size_inches(cm2inch(figure_width, figure_height))\n",
    "\n",
    "\n",
    "\n",
    "def plot_classification_report(classification_report, title='Classification report ', cmap='RdBu'):\n",
    "    '''\n",
    "    Plot scikit-learn classification report.\n",
    "    Extension based on https://stackoverflow.com/a/31689645/395857 \n",
    "    '''\n",
    "    lines = classification_report.split('\\n')\n",
    "\n",
    "    classes = []\n",
    "    plotMat = []\n",
    "    support = []\n",
    "    class_names = []\n",
    "    for line in lines[2 : (len(lines) - 1)]:\n",
    "        t = line.strip().split()\n",
    "        s=len(t)\n",
    "        if s < 4: continue\n",
    "        r=1\n",
    "        if s>5:\n",
    "            r=2\n",
    "        classes.append(t[0]+t[1])\n",
    "        v = [float(x) for x in t[r: len(t) - 1]]\n",
    "        support.append(int(t[-1]))\n",
    "        class_names.append(t[0])\n",
    "        #print(v)\n",
    "        plotMat.append(v)\n",
    "\n",
    "    #print('plotMat: {0}'.format(plotMat))\n",
    "    #print('support: {0}'.format(support))\n",
    "\n",
    "    xlabel = 'Metrics'\n",
    "    ylabel = 'Classes'\n",
    "    xticklabels = ['Precision', 'Recall', 'F1-score']\n",
    "    yticklabels = ['{0} ({1})'.format(class_names[idx], sup) for idx, sup  in enumerate(support)]\n",
    "    figure_width = 25\n",
    "    figure_height = len(class_names) + 7\n",
    "    correct_orientation = False\n",
    "    heatmap(np.array(plotMat), title, xlabel, ylabel, xticklabels, yticklabels, figure_width, figure_height, correct_orientation, cmap=cmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Methodology**\n",
    "1. The dataset was previously biased dataset hence classification could not have been accurate. \n",
    "2. We then divided the dataset into two classes to make it balanced or unbiased.\n",
    "3. We used 4 models to test the accuracy over the test set and compared their classification report to find the more suitable model.\n",
    "4. We plotted a confusion matrix and classification report(as heatmaps).\n",
    "5. We then plotted the bar plots for each of the following parameter comparing all the models:<br>\n",
    "    *Training Accuracy*<br>\n",
    "    *Test Accuracy*<br>\n",
    "    *F1- Score*<br>\n",
    "    *Recall* <br>\n",
    "    *Precision Score*\n",
    "6. Following algorithms are used for classification task:<br>\n",
    "    *Decision Tree*<br>\n",
    "    *Random Forest*<br>\n",
    "    *Random Gradient Boost*<br>\n",
    "    *Support Vector Machine*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Decision Tree Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuracy : 68.4375\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.69      0.65      0.67       156\n",
      "           2       0.68      0.72      0.70       164\n",
      "\n",
      "    accuracy                           0.68       320\n",
      "   macro avg       0.68      0.68      0.68       320\n",
      "weighted avg       0.68      0.68      0.68       320\n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 708.661x311.811 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Decision Tree classifier training and performance evaluation\n",
    "\n",
    "dt = DecisionTreeClassifier(random_state=73)\n",
    "dt.fit(x_train,y_train)\n",
    "dt_predict = dt.predict(x_test)\n",
    "#print confusion matrix and accuracy score\n",
    "dt_acc_score = accuracy_score(y_test, dt_predict)\n",
    "print(\"Acuracy :\", dt_acc_score*100)\n",
    "plot_confusion_matrix(dt,x_test,y_test)\n",
    "print(classification_report(y_test, dt_predict))\n",
    "print(\"Confusion Matrix\")\n",
    "plot_classification_report(classification_report(y_test, dt_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Random Forest Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuracy : 78.125\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.78      0.76      0.77       156\n",
      "           2       0.78      0.80      0.79       164\n",
      "\n",
      "    accuracy                           0.78       320\n",
      "   macro avg       0.78      0.78      0.78       320\n",
      "weighted avg       0.78      0.78      0.78       320\n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 708.661x311.811 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Random forest classifier for training and performance evaluation\n",
    "\n",
    "dt = RandomForestClassifier(random_state=73,n_jobs=-1)\n",
    "dt.fit(x_train,y_train)\n",
    "dt_predict = dt.predict(x_test)\n",
    "#print confusion matrix and accuracy score\n",
    "dt_acc_score = accuracy_score(y_test, dt_predict)\n",
    "print(\"Acuracy :\", dt_acc_score*100)\n",
    "plot_confusion_matrix(dt,x_test,y_test)\n",
    "print(classification_report(y_test, dt_predict))\n",
    "print(\"Confusion Matrix\")\n",
    "plot_classification_report(classification_report(y_test, dt_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Gradient Boosting**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuracy : 74.0625\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.76      0.69      0.72       156\n",
      "           2       0.73      0.79      0.76       164\n",
      "\n",
      "    accuracy                           0.74       320\n",
      "   macro avg       0.74      0.74      0.74       320\n",
      "weighted avg       0.74      0.74      0.74       320\n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 708.661x311.811 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gradient boosting classifier\n",
    "\n",
    "dt = GradientBoostingClassifier(random_state=73)\n",
    "dt.fit(x_train,y_train)\n",
    "dt_predict = dt.predict(x_test)\n",
    "#print confusion matrix and accuracy score\n",
    "dt_acc_score = accuracy_score(y_test, dt_predict)\n",
    "print(\"Acuracy :\", dt_acc_score*100)\n",
    "plot_confusion_matrix(dt,x_test,y_test)\n",
    "print(classification_report(y_test, dt_predict))\n",
    "print(\"Confusion Matrix\")\n",
    "plot_classification_report(classification_report(y_test, dt_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Support Vector Machine**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuracy : 62.18749999999999\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.76      0.69      0.72       156\n",
      "           2       0.73      0.79      0.76       164\n",
      "\n",
      "    accuracy                           0.74       320\n",
      "   macro avg       0.74      0.74      0.74       320\n",
      "weighted avg       0.74      0.74      0.74       320\n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 708.661x311.811 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SVM classifier \n",
    "\n",
    "from sklearn.svm import SVC\n",
    "svc = SVC()\n",
    "svc.fit(x_train, y_train)\n",
    "pred_svc = svc.predict(x_test)\n",
    "dt_acc_score = accuracy_score(y_test, pred_svc)\n",
    "print(\"Acuracy :\", dt_acc_score*100)\n",
    "plot_confusion_matrix(dt,x_test,y_test)\n",
    "print(classification_report(y_test, dt_predict))\n",
    "print(\"Confusion Matrix\")\n",
    "plot_classification_report(classification_report(y_test, dt_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **SVM with GridSearchCV**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param = {\n",
    "    'C': [0.1,0.8,0.9,1,1.1,1.2,1.3,1.4],\n",
    "    'kernel':['linear', 'rbf'],\n",
    "    'gamma' :[0.1,0.8,0.9,1,1.1,1.2,1.3,1.4]\n",
    "}\n",
    "grid_svc = GridSearchCV(svc, param_grid=param, scoring='accuracy', cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score=nan,\n",
       "             estimator=SVC(C=1.0, break_ties=False, cache_size=200,\n",
       "                           class_weight=None, coef0=0.0,\n",
       "                           decision_function_shape='ovr', degree=3,\n",
       "                           gamma='scale', kernel='rbf', max_iter=-1,\n",
       "                           probability=False, random_state=None, shrinking=True,\n",
       "                           tol=0.001, verbose=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'C': [0.1, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4],\n",
       "                         'gamma': [0.1, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4],\n",
       "                         'kernel': ['linear', 'rbf']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_svc.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1.3, 'gamma': 0.1, 'kernel': 'linear'}"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_svc.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuracy : 67.5\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.69      0.65      0.67       156\n",
      "           2       0.68      0.72      0.70       164\n",
      "\n",
      "    accuracy                           0.68       320\n",
      "   macro avg       0.68      0.68      0.68       320\n",
      "weighted avg       0.68      0.68      0.68       320\n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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N68BvC+2120jmL1rCwiXV1NTU8o8pd3HUoeM3SHPsxEN48PEnAVi5eg3zFixi6HaDGNS/H9OfmEVdXR21tbVMnzGLnXYY2haPURKymdaf06hUuB4VVyaTaesitBnXpfJWrGBNUiVwBXA4ScPTyZJG5KaJiG/Uxx7Ab4Gbck5fCpzWnLK3ZKfxdOCb6X8fBs4GZkfSjjwDGCtpBwBJXSQNb3D9k8DBknpJqiInMNqEt4EtcvanAl9Ju2CRNFxS17RMk9Mxbf1IAsnGvAjUl1E55RVwNPBSuj8a+H8kgVruAIapwMT0GXqRtPRNzcljG5Kg1hpRVVXFZT/6Hkee+mV2HX80nznqMHbZcQcu+uXl3HbPAwBMHDeW3j17MmrCMXzyxDO45Pvn0btXT44/ciJDtxvE6EOPY8/DjmfUzjty1KHj2vaBrE24HlmxuC6VLymZZ63QLY+9gfkRsSAiaoDrgWM3kf5k4O/1OxFxH0m8Unj5W2qeF0mHkAzO75mOS3sZ+ENE/Do9PwH4ObBZeskPImKKpAdJxp3NSsdufZOkNepFYHVEfL/hVBuS3omIbmlQdjfQB/gzcBnwY5LASiRdmp8C3iKJdCeQTMEBcG0jU4EcCHw5Ik6VVEESdHZP83oW+EpEvJVOw7ErsCy9dElEHJPmcQbwvfT4TyLimvT4GOC7EbHJIHTkTsNj9r03b/JnbZbP47PnsN/uI/InNMtjzryFjBg2pK2LYWXulRVvstOeY5+KiFYZtNel37AY/oXLCk7/7M+OXAyszDl0ZURcCZC+1DgpIr6Y7p8G7BMR5zTMR9J2pC8epr1y9cfHkTPGPp8Wm2ctjRw75ewPb3D+fmCvRq4bl7N7XURcmbas3Qzck6b5fINruqX/rQUOaZDl9/gwWMq10Q+1kbI8LOlnknpGxFqSFw4aS/fJTeRxNY2/vHAa8Lt8ZTAzM7OPr5nThazcRCCZbzx6rsnAjbmB2kdR6u/zX5S+HPBvYCFwSxuU4Txg2xbI999pQGtmZmYtSCq8C7SAbtB849FzTSanC/SjKrkVDHJFxDdLoAxPtFC+fywoYRm/amyloxTnDbLy1JHn7LPyVsTvwZnAMElDgNdIArKNJuSXtCPQi2RGjI+lpIM1MyuOcl5mxUqL65KVq2IFaxFRJ+kckhcGK4GrI+IFSRcDsyJiSpr0ZOD6aPBygKSHSeaI7SapGjgzIqZu6p4O1szMzKx9E0WdFDci7gTubHDsggb7FzVx7YHNvZ+DNTMzM2vXhKgowTU/C+VgzawDyGY77qS4ZlZ6Wv07KZ1nrVw5WDMzs4JFQYu/mG1aS83xuinlPN7SwZqZmZm1awJUvr2gDtbMzMysnXM3qJmZmVlpK+f5Jh2smXUAanR1FLPmc12yYmj9yZXlMWtmVtrK+S9KM2t/WjtwkrtBzczMzEpbOf/R6mDNrANoi9fkrX1yXbJyJEGlgzVrMf5itCKIrOuRFYcnWLZy5WDNzMzMrERJorOXmzIzMzMrTcIta2ZmZmYlS4IqB2tmZmZmpckta9ayyngSPysdFZXlO1bDSktlZWVbF8Gs+SQHa2ZmZmalKmlZK98/Wh2smZmZWbvnljUzK22eZs3MOjBPimstat26Gh575gWy2SyR/osbEUQ2kLTBWmdSRd711jLZzPrP2WxABBUVGy5wW1Gx6TEpEUE2PpwYM5tJPlduMC5KeRfq9TO13jPNmTuf2tqaj/QckPw8Pu5z1P88Pvb/m/Tn8XH+3/iZPvozLVi8hFVvvl32z9EUP1PrPNNb79VuMk2xCY9Zsxa0WedO7D96l7YuhpW5qsoq9tt9RFsXw9qBnt26MGLYkLYuhpW5V1a82er3rCzjF/YcrJmZmVm75m5QMzMzsxImwWZebspaTBk321oJcTWyIsk3rsmsFJX7mDX/1pl1AP4H1ool3+Bxs1JVWaGCt3wkTZI0V9J8Sec3cv43kman28uS1uacO13SvHQ7vZCyu2XNzMzM2rVijlmTVAlcARwKVAMzJU2JiDn1aSLiGznp/xMYnX7eErgQGEMyqdJT6bVrNnVP/7ltZmZm7Vr92qBFalnbG5gfEQsioga4Hjh2E+lPBv6efj4MmBYRq9MAbRowKd8N3bJm1gFks9n8iczMWkmrfycV923QAcCrOfvVwD6N3lbaDhgC3L+Jawfku6GDNTMzK1h4OQwrgojWrUcf4QWDPpJm5exfGRFXrs9uY0090GTgxoion924Odeu52DNzMzM2r1mBmsrI2JME+eqgUE5+wOBpU2knQx8tcG14xpc+2C+wnjMmpmZmbVr9S8YFGnM2kxgmKQhkjqTBGRTNr6ndgR6AY/nHJ4KTJTUS1IvYGJ6bJPcsmZmZmbtWv0LBsUQEXWSziEJsiqBqyPiBUkXA7Mioj5wOxm4PnL6fCNitaQfkQR8ABdHxOp893SwZtYByLPiWpG4LlkxtPp8fUVebioi7gTubHDsggb7FzVx7dXA1c25n4M1sw5AZTxzt5m1P609UbeQF3I3MzMzK1UCOpXxH60O1sw6gNZ+Td7MrKQIKhysWYvxP7JWBJF1PbLiyGQy+ROZlRiBu0HNzMzMSlmFgzUzMzOz0pS0rLV1KT46B2tmZmbWvkkes2ZmZmZWqoS7Qa0llXHlstLhedasWCorK9u6CGYfibtBzayktfps4WZmJcQta2ZmZmalrMjLTbW21l3vwayZpj7wCLscfBQ7HXA4v7jiqkbT3HDb3YyacAy7HXIsp53z7fXHz//Jr9jtkGPZdfzRfP2Cn3bsiWE78KOD65EVj+tSeapvWSt0KzVuWbOSlclk+NoPfsxd1/2Rgf22Yd+jTuKoQ8czYvj269PMW7iYn19xFQ/d9Dd69ezBipWrAHhs1jM8NusZnr7nJgAO/vTnmD5jJgfvt3ebPEtby0a2rYvQZlyPiis6cOTvulQ8rT25slBZLzfllrU8JH1d0ufSzz+S9Jyk2ZLukdQ/Pf7Z9Phzkh6TtFvO9ZMkzZU0X9L5OcevlzSs9Z+ofDw5+3m2H7wtQ7cbROfOnTjpmMO57Z77N0jzp+tu5CunT6ZXzx4AbN2nN5CM0fpgXQ01NbWsq6mhtrZ2/TnrWFyPrFhcl8pY2g1a6FZqOmSwJqmg15kkVQFnANelhy6NiFERsTtwO3BBenwhcHBEjAJ+BFyZc58rgMOBEcDJkkak1/we+LB93DaydPkKBvbfZv3+gH59eW35ig3SzFuwmHkLFnPQcacy9phTmPrAIwDst+fujNtvLwaNGc+gPccz8eCx7Dxse6zjcT2yYnFdKl9JN2jhW6kpuW5QSYOBu4FHgH2BZ4FrgB8CWwOfjYgnJe0N/A/wCeB94AsRMTcNkH4OHEYyUuePEfFbSYuAq4GJwOWSXgL+AHQBXgHOiIg1DYozAXg6IuoAIuKtnHNd0/yJiMdyjs8ABqaf9wbmR8SC9NmuB44F5gAPA3+WVFWfv22osfEcDd9qrMvUMX/hYu775zVUL3ud8cefzux7b2bl6rW8NH8Bi568D4BJp5zFwzNmceC+Y1ql7FY6XI+sWFyXyls5rw1aqi1rOwCXAaOAnYBTgAOAbwLfS9O8BBwUEaNJWrh+mh7/EjAEGJ22dP1fTr4fRMQBEXE98FfgO2ma54ELGynHWOCp3AOSfiLpVeCzfNiylutM4K708wDg1Zxz1ekxIiILzAd2owFJX5I0S9KsNWvXNnKLjmFAv75UL12+fv+1Za/Tv+9WG6U5euIEOnXqxJBtBzJ8+8HMW7iYW6feyz6jd6Nb1y5069qFSeMP4IlnnmvtR7AS4HpkxeK6VL7K/QWDUg3WFkbE82lA8wJwXyR/0jwPDE7T9ABukPRv4DfALunxTwJ/yGkNW52T7z8AJPUAekbEQ+nxvwAHNVKOfsAbuQci4vsRMYgkCDwn95yk8STB2nfqDzWSZ+6fZiuA/hsliLgyIsZExJhePXs2kkXHsNduI5m/aAkLl1RTU1PLP6bcxVGHjt8gzbETD+HBx58EYOXqNcxbsIih2w1iUP9+TH9iFnV1ddTW1jJ9xix22mFoWzxGSahQqf6qtzzXo+JSo19rHYPrUvFUVLTyd5KgsqLwrdSUXDdoal3O52zOfpYPy/wj4IGIOC7tOn0wPS6anqjg3WaW431g8ybOXQfcQdoiJ2kUcBVweESsStNUA4NyrhkILM3Z3zy9hzWiqqqKy370PY489ctkMhk+f9Jx7LLjDlz0y8vZc9QuHD1xPBPHjWXa9McYNeEYKioqueT759G7V0+OP3IiDzz2JKMPPQ5JTDz4AI46dFxbP1Lb6bj/vroeWdG4LhVPa0/UXe6T4qrU5nlJA6/bI2Jkuv/ndP/G3HOSbgaujYh/SboI+HxEDJZ0Nknr2uSIqJO0ZUSsTsesjYmIlWm+zwLnRMTD6fU9IuIbDcpyNjAwIn6Q7g+LiHnp5/8keangBEnbAvcDn8sdv5a+oPAycAjwGjATOCUiXkjPPw9MjIhlTf08Ru68Y8yedtNH+2GapZ54/iX22XWnti6GtQNzFyxhx6HbtnUxrMy9suJNdtpz7FMR0SqD9nYeNTr+evv9+ROm9t5uy1YrWyFKtWWtEL8A/iLpXJJAqd5VwHDgOUm1wB+Byxu5/nTgD5K6AAuALzSS5i7gbzn7l0jakaSFbzFwdnr8AqA38Lv0r4W6tBuzTtI5wFSgErg6J1DrC7y/qUDNrFiy2Y47z5oVV6n9gW9WiHJvWSu5YC0iFgEjc/Y/39i5iHicJCir99/p8Trg3HTLzXdwg/3ZJG+bbqosiyWtqm9Ri4jjm0j3ReCLTZy7E7izkVOnAP9vU/dPM8ibxCwvVyMrEgf+VpZUmmPRClVywVoJOp/kRYN5Rc53LRu22pmZmVkLcMtaOxcRc4G5LZDvNcXO08zMzBpXxrGagzUzMzNr34QnxTUzsw6itadcMCuWYi431dS63w3SnChpjqQXJF2Xc/znkv6dbicVUna3rJU6fzFaEVSU88haKymtPpmpWTGoeP+c5qz7fSjJfKozJU2JiDk5aYYB3wXGRsQaSVunx48E9gB2BzYDHpJ0V4PlLDfi3zozMzNr14SoaMaWx/p1vyOiBqhf9zvXWcAV9WuOR8SK9PgI4KGIqIuId0nWP5+U74YO1szMzKzdkwrfgD71a3Sn25dysmpy3e8cw4Hhkh6VNENSfUD2LHC4pC6S+gDj2XClo0Y1uxtUUi9gUER4BVqzMhFZT7RmZqWjLSZXLmQsWo6Vm1jBIN+635DEV8OAcSRLTT4saWRE3CNpL+AxkrXHHwfq8hWmoJY1SQ9K6i5pS5Ko8BpJvy7kWjNre+FZca1IXJesGNpicmU1Y8sj37rf9WlujYjaiFhIMgXYMICI+ElE7B4Rh6a3yzuPa6HdoD3SwW+fBq6JiD1J1t80MzMzK2n1k+IWuuUxExgmaYikzsBkYEqDNLeQdHGSdncOBxZIqpTUOz0+ChgF3JPvhoV2g1ZJ6gecCHy/wGvMzMzMSkKx3gZtat1vSRcDsyJiSnpuoqQ5QAb4VkSskrQ5SZcowFvAqekymZtUaLB2cXrjRyNipqShFH/5JTMzM7MWUcw3Khtb9zsiLsj5HDS+TvkHJG+ENktBwVpE3ADckLO/AGh0UXMzMzOzUpK85Vm+85YW+oLBcEn3Sfp3uj9K0g9atmhmViyeyNSKRYUMvzbLo7KysvXvWVH4VmoKLdIfSWbirQVIp+2Y3FKFMjMzMysWkQQ8hW6lptAxa10i4skGTYh5B8SZmZmZlYJy7gYtNFhbKWl70knfJJ0ALGuxUtmH2mDiQGt/spnWn9PI2qe2mB/L7GMrcIH2UlVosPZV4EpgJ0mvAQuBU1usVGZmVpLaYuZ5s2Io41it4LdBFwCflNQVqIiIt1u2WGZmZmbFkUyK29al+OgKfRv0vyR1B94DfiPpaUkTW7ZoZmZmZsUhqeCt1BT60sMZ6XJTE4GtgS8Al7RYqczMzMyKpL5lrdCt1BQ6Zq2+6EeQrA36rEox9GyP/GO2YnA1siLxnH1Wrsr5a7DQYO0pSfcAQ4DvStoC8CtBrcRvX9nHFq5HVhyB65J9fK1fhwpaoL1kFRqsnQnsDhomtRMAACAASURBVCyIiPckbUnSFWotbN26dTw6azbZbGwwjUcmm0WCCn34V64qKvI2xOVO4RAE2WxQkdtHL1GRpw04AiLnFy0bQURQmfsXdwH5+Jla75mumzaTM39xS30J8v6JmTxCzlt/kQUajuUoJJ/cPAKINI+cCwv4Am08n9wWHj9TcqDln2nChD3oPufN+sRENnIuTX5vclvflKzzk+f+2Q0eI5vNIoRy6niz84kgG7HRGCTlbRn0M7XGM21Z2cpTteYvVkkrNFjbD5gdEe9KOhXYA7is5Ypl9TbbbDMO3HuPti6Glbn7X1jOopnvtXUxrB3oNXQXegwc2tbFsDK3Td3qVr2fIlAZTztT6OCD3wPvSdoN+DawGPhri5XKPlTGlctKh6uRFUu4C9TKlLJ1BW+lptBgrS6SdvJjgcsi4jJgi5YrlpmZmVmxRDJMoNCtxBTaDfq2pO+SrFpwkKRKoFPLFcvMzMysiMq4i6HQlrWTgHXAmRGxHBgAXNpipTIzMzMrlugALWtpgPbrnP0leMyamZmZlQmVYBBWqEKXm9pX0kxJ70iqkZSR9Gb+K+1jK+d3ja1keCJTK5aKysq2LoLZR9PeW9aAy4HJwA3AGOBzwLCWKpSZmZlZ8URJBmGFKjRYIyLmS6qMiAxwjaTHWrBcZmZmZsURdIhg7T1JnYHZkn4BLAO6tlyxzKyoyvclKDNrj1r9zcyAMp4jsNCBLKcBlcA5wLvAIOD4liqUmRVXtoxfWbdS47pkH19brC+ryBa8lZpC3wZdnH58H/hhyxXHzMzMrAWUYBBWqE0Ga5KeZxN/RkXEqKKXyMzMzKyYIiCbaetSfGT5WtY+DfQFXm1wfDtgaYuUyMzMzKzIitm9KWkScBnJELGrIuKSRtKcCFxE0uj1bESckh7/BXAkyVC0acB/pUt6NinfmLXfAG9FxOLcDXgvPWdmZmZW4oq3gkG65OYVwOHACOBkSSMapBkGfBcYGxG7AF9Pj+8PjAVGASOBvYCD85U+X7A2OCKe2+iRI2YBg/NlbmaloaLCkytbsbgu2cfXJpMrF29S3L2B+RGxICJqgOuBYxukOQu4IiLWAETEivpSAJsDnYHNSNZZfz3fDfMFa5tv4twn8mVuZmZm1uaavzZoH0mzcrYv5eQ2gA2Hh1Wnx3INB4ZLelTSjLTblIh4HHiAZAq0ZcDUiHgxX/HzjVmbKemsiPhj7kFJZwJP5cvczMzMrK2JZo9ZWxkRYzaRXUMNx5xVkaz0NA4YCDwsaSTQB9g5PQYwTdJBETF9U4XJF6x9HbhZ0mf5MDgbQ9J8d1yea60YPD+WFUFbzGlk7VO4Llm5Kl7drSaZb7beQDZ+6bIamBERtcBCSXP5MHibERHvAEi6C9gX2GSwtslu0Ih4PSL2J5lbbVG6/TAi9ouI5YU9k5mZtRd5XlozK1GRdoUWuG3aTGCYpCHp6k6TgSkN0twCjAeQ1IekW3QBsAQ4WFKVpE4kLxd87G7Q5BEjHiDpYzUzMzMrL0VcGzQi6iSdA0wlmbrj6oh4QdLFwKyImJKemyhpDpABvhURqyTdCEwA6uexvTsibst3z4IXcjczMzMrV8WcZy0i7gTubHDsgpzPAZybbrlpMsCXm3s/B2tmZmbWzkX7XW7KSoA8p5F9fK5GVixyZbJyFAGZ2rYuxUfmYM1K2tQHHuHciy4hk8lwxsnH8+2vfnGjNDfcdjc/+s3vkMSonXfkb5f/AoDzf/Ir7rp/OtlslkMO3I/f/PC7HfYfGinflIrt28T9R/Lrb51CRYW45paHufSaDXovuPS8yYzbaycAumzema227M7WB53DbsMH8dvvn0b3rp8gk8lyyZ9u54Z7ZrbFI5QMVXTsujT3yencfvlPyGYz7HXEZxh3yoY9Wrdf8VMWzJ4BQM26D3h3zSouvC2ZTOGpqTfxwLW/B2D8qV9hz8M+3bqF7+DK+U1mB2tWsjKZDF/7wY+567o/MrDfNux71Ekcdeh4Rgzffn2aeQsX8/MrruKhm/5Gr549WLFyFQCPzXqGx2Y9w9P33ATAwZ/+HNNnzOTg/fZuk2extlNRIS47/1SO+MqvqH59NY//3wXc/tBsXlzw4Zv23/rV9es//8fkQ9h9x20BeO+DGs7476uYv2QF/bbqyYz/u4B7Hvs3b77zfqs/h7W9bCbDlMt+yJmXXkP3rbbhiq8cz877H0LfwTusT3PUV7+3/vNjN/2VpfOTF/3ee2st9/31cs75/U1I4rdnH8eI/Q/hE1v0aPXn6JjKeyH3DvknkqRBkh6Q9KKkFyT91ybSfl3S59LPn0nTZyWNaZBulKTH0/PPS9q8wfkpkv6ds/9LSROK/WztyZOzn2f7wdsydLtBdO7ciZOOOZzb7rl/gzR/uu5GvnL6ZHr1TL7wtu7TG0i6aj5YV0NNTS3ramqora1df846lr1GDuWVV1ew8LU3qK3L8M+pT3D0uN2bTH/SpH34x91PADBvyevMX5KsErPsjbW8seZtttpyi1Ypt5WeV196jt4DtmPL/ttS1akzu004khcfu7fJ9M/efwe7TTgKgJdnPsKwPcfSpXtPPrFFD4btOZa5Tz7cWkW3IAnWCt1KTIcM1oA64LyI2JlkMrqvNlyEFUBSFXAGcF166N/Ap2kweV2a7lrg7HTB1nFAbc75TwPvNMj+t8D5xXiY9mrp8hUM7L/N+v0B/fry2vIVG6SZt2Ax8xYs5qDjTmXsMacw9YFHANhvz90Zt99eDBoznkF7jmfiwWPZedj2dFQdeW6sAVv3pPr11ev3X3t9Df236tVo2m379WZw/z48MHPjaY/G7DKEzlWVvPLqGy1WVittb618nR5bf/id1L3PNrz5RuPLOq5Z/hprllez/eh9P7x2q34fXrvVNry1Mu+SkO1Wa3dJBkFkMgVvpaZDdoNGRP2aXETE25JeJFnXa06DpBOApyOiLk37IjQ6wHYi8FxEPJumW1V/QlI3kld3vwT8M6cMiyX1lrSNJxhuXGMBRsOffV2mjvkLF3PfP6+hetnrjD/+dGbfezMrV6/lpfkLWPTkfQBMOuUsHp4xiwP3bWr1kPatA8dqqJGVYWKjlWESJx62NzfdN4tsdsPz2/TpwZ9/fBZnXHBVhw58Ex34+Qv4Tqr33AN3MPKgwz5csLzRa4taurLS6r9HQTFXMGh1HbVlbT1Jg4HRwBONnB5LYWugDgdC0lRJT0v6ds65HwG/At5r5Lqn03s0LNOX6hePXbN2bQG3b58G9OtL9dIP49jXlr1O/75bbZTm6IkT6NSpE0O2Hcjw7Qczb+Fibp16L/uM3o1uXbvQrWsXJo0/gCeeea61H8FKQPWKNQzsu+X6/QF9e7HsjcZ/r048bO/1XaD1tui6Obf+79e58IqbePL5BS1aVitt3bfahjdXfPid9NbK5XTvs3WjaZ994MMu0PXXvrHsw2vfWM4Wvfu2XGGtgXA3aLlKW73+BXw9It5qJEk/oJA+jyrgAOCz6X+Pk3SIpN2BHSLi5iauWwH0b3gwIq6MiDERMaZXz56FPEq7tNduI5m/aAkLl1RTU1PLP6bcxVGHjt8gzbETD+HBx58EYOXqNcxbsIih2w1iUP9+TH9iFnV1ddTW1jJ9xix22mFoWzyGtbFZLyxkh237Mrh/HzpVVXLiYftw+4OzN0o3fLtt6Nm9KzOefWX9sU5Vldzwq3O49vbH+Ne9s1qz2FaCBu60KytfW8TqZa9SV1vDs/ffwc77HbJRujeWLOD9t99i211Grz82fK8DmDfrUd5/+03ef/tN5s16lOF7HdCaxe/YIohspuCt1HTIblCAdE2ufwH/FxE3NZHsfWDzJs7lqgYeioiVad53AnuQjFPbU9Iikp/11pIejIhx6XWbp/ewRlRVVXHZj77Hkad+mUwmw+dPOo5ddtyBi355OXuO2oWjJ45n4rixTJv+GKMmHENFRSWXfP88evfqyfFHTuSBx55k9KHHIYmJBx/AUYeOa+tHsjaQyWT5+s+v5Y7fnUtFRQV/ufUR5ixYyoVf+RRPzVnE7Q8lgdtJk/bhhqlPbnDtZybuxYF7DKd3z2587pikEfyLF/yJZ19+tdWfw9peZWUVx/znBVz9nTOJTIYxh59A3yHDmHbNZQwYPpIRY5PA7dn7b2e38Uds0EXapXtPJpz2H1z+leMBmHDaV+nSveP+Md4myrgbVB1x/IWS36C/AKsj4uubSHc2MDAiftDg+IPANyNiVrrfC7iPpFWtBrgb+E1E3JFzzWDg9ogYmXPsNuAnETGjqTKM3HnHmD2tqVjSrDAXXnUHl976UlsXw9qBb543me4DhrR1MazM9a1ZyRmT9n8qIlplIPGeO28fj1/9i4LTb7b/Ca1WtkJ01G7QscBpwARJs9PtiEbS3QUcVL8j6ThJ1cB+wB2SpgJExBrg18BMYDbJSwl3NJLfemnL3g6A+1asxXXUyYDNrDS1+uTKZT51R4fsBo2IR6CRV8Q2TrdY0ipJwyJiXjr2rNHxZxFxLcn0HU3ltQgYmXPoKODG+jdNzczMrKVEWXeDdshgrZnOJ3nRYF6R860ieUvUrMV1xOEOZmbrRRC1NW1dio/MwVoeETEXmNsC+d5QYMJi39o6IAdrVizZEpww1CyvKO/lphysmZmZWbvnhdzNzMzMSpZb1szMzMxKV/3boGXKwZqZmZm1a0G4G9RakOfHsiLwPGtWLK0+P5ZZMbhlzcxKnYM1KxbXJStPHrNmZmZmVroCooynnXGwZmZmZu2cVzAwsxKXzXpSXCsW1yX7+NpkcuUy7gb1SFEzMzNr3yLI1tUWvOUjaZKkuZLmSzq/iTQnSpoj6QVJ16XHxkuanbN9IOlT+e7nljUzMzNr3yKITHG6QSVVAlcAhwLVwExJUyJiTk6aYcB3gbERsUbS1kkx4gFg9zTNlsB84J5893SwZmZmZu1aBEUL1oC9gfkRsQBA0vXAscCcnDRnAVdExJrk/rGikXxOAO6KiPfy3dDdoGZmZtbOJZPiFrrlMQB4NWe/Oj2WazgwXNKjkmZImtRIPpOBvxdSeresmXUAFZ4by4rGdck+vorWnly5+S1rfSTNytm/MiKuTD839kvQ8M2bKmAYMA4YCDwsaWRErAWQ1A/YFZhaSGEcrJl1BP731cxKSRv8AdnMYG1lRIxp4lw1MChnfyCwtJE0MyKiFlgoaS5J8DYzPX8icHN6Pi93g5qZmVm7FhFkM5mCtzxmAsMkDZHUmaQ7c0qDNLcA4wEk9SHpFl2Qc/5kCuwCBbesmXUIEeU7GaSVlgjPs2blqVgLuUdEnaRzSLowK4GrI+IFSRcDsyJiSnpuoqQ5QAb4VkSsApA0mKRl7qFC7+lgrdT5i9GKwNXIiqVY/+CZtaoiTt2RZBd3Anc2OHZBzucAzk23htcuYuMXEjbJwZqZmZm1e8UM1lqbgzUzMzNr1yKirFuFHayVuNq6OpYsXZ5UtAZdWRKomW/UNLZGZEVF8/JorCzFysfPlCj2M6184w0+oZqcQhWUQ4OPDS76SHmUWj6lVJZi5dOyZXlr5XKqunT7MPlG/wAG2mBahgIKENFgLFwkvzMb/N7kz6fRshQrHz9TUZ+pc92a/Pcrsqxb1qylRNVmrO65Q1sXw8rcoOzdHPfMg21dDGsHTji6O0OWPt/WxbAyt7T7oPyJiikbZGvqWveeReRgrcRVVFTQs9eWbV0MK3PdttiCzf3rbkXQrctmbNmtS1sXw8rcu926tur9gvJ+Ocbf3mZmZta+Fflt0NbmYM3MzMzaPQdr1mI8AaUVQ7aMm/+ttLguWVmK8q67DtbMzMysXQvcDWpmZmZWugIi/5qfJcvBmpmZmbVznhTXzMzMrHSFXzCwFtTcme/NGuN6ZMUiVeRPZFZyPGbNzEqcgzUrFlclK0cRXm7KzMzMrHRFkK31clNmZmZmpSkgMuU7b6mDNbMOIBvl2/xvJcYTdVsRZFq5SzIId4OamZmZlayAyJbvHxoO1szMzKzdy7ob1MzMzKw0hedZMzMzMythEX7BwMxKm+dZs6JxXbIiqKho/cmV3Q1qZiVN+B9YMysdrR7zuxvUzMzMrHQFkPXboGZWysJzY1mRuCpZWSrzMWtekbfE+R9ZKwbXIyuW8ATLVqaymWzBWz6SJkmaK2m+pPObSHOipDmSXpB0Xc7xbSXdI+nF9PzgfPdzy5qZmZm1a5GFbE1x/tCQVAlcARwKVAMzJU2JiDk5aYYB3wXGRsQaSVvnZPFX4CcRMU1SNyBvwRysmZmZWTtX1OWm9gbmR8QCAEnXA8cCc3LSnAVcERFrACJiRZp2BFAVEdPS4+8UckMHa1bSpt8/jR9//ztkMhlOPPV0vvy1czdKc+etN/G/l/4MSey0y0h+84erAfjFxRfw4L1TAfjqud/myE8d36plt9Ix4rCDOfGyC6iorOTRq/7B1J//fqM0e37mSI666OtEBNXPvsjVn/0vAH5X9wqvPT8XgNVLXuP3x57VqmW30lI1YBib73sEVFRQO/cp1j03faM0nYaMZLPRE4Ags3o57z94A5X9hvCJfY5Yn6aiRx/ee/Cf1C1+sRVL34E1f7mpPpJm5exfGRFXpp8HAK/mnKsG9mlw/XAASY8ClcBFEXF3enytpJuAIcC9wPkRkdlUYRysWcnKZDJc9J3z+PMNt7JN/wEcP3EcEw47gmE77rQ+zaIF8/nDZb/mH7ffQ4+evVj1xhsAPDDtbl547lmm3P8oNevW8dlPHcFBhxzKFlt0b6vHsTaiigpOvuJiLjv0VNZUL+e7M6fw3JRpLHtx/vo0W+8wmMO++x9cOvZ43lv7Flts1Xv9uZr3P+Ano49oLGvraCQ23/9o3r37GuLdt+h2zNnULnmR7No31iep6N6bzXY7iHduvxJqPkCbdwUgs2wh79xyRZJN50/Q7cRvUFc9v9HbWPEFzZ5nbWVEjGniXGMTjzTMvAoYBowDBgIPSxqZHj8QGA0sAf4BfB7406YK02FfMJB0taQVkv6dJ93XJX0u/fyZdKBgVtKYnDSDJb0vaXa6/SHnXGdJV0p6WdJLko5Pj58j6QsFlPOjP2SZe+7pWWw3ZCjbDh5C586dOfK447nv7js2SPOPv/2FU884ix49ewHQe6utAJg/dy577z+WqqoqunTtyk67jOTh++9t9WcoFW0xAWWpGLz37qyYv5iVC18lU1vLzOtvY9SxEzdIc8BZk3noir/y3tq3AHj7jVVtUdSy0JHrUuVWA8m+tYp4ew1kM9QueJ5O2+68QZrOO45h3ZwnoOYDAOKDdzfKp2rILtS9Og8yta1SbiN9GzRb8JZHNTAoZ38gsLSRNLdGRG1ELATmkgRv1cAzEbEgIuqAW4A98t2w4/7WwZ+BSZtKIKkKOAOof4vj38CngY3bveGViNg93c7OOf59YEVEDAdGAA+lx68GvvbRi9/+LV++jH4DBq7f36Zff15ftuHvw6JX5rPwlfmcdOShnHD4BKbfPw2AnXYZyfT7pvH+e++xetUqZjzyMMtee61Vy2+lodeAvqx59cN6s7Z6Gb0G9N0gzdbDh9J3+BC+9ciNfPvxmxlx2MHrz3XafDO+O3MK3378ZnZrEORZx6Iu3Yl331y/n33vLdR1w9b6ih69qezRh65HnUXXo79M1YBhG+XTeegoahc81+LltQ1FJgre8pgJDJM0RFJnYDIwpUGaW4DxAJL6kHR/Lkiv7SVpqzTdBDYc69aoDtsNGhHTC3hddgLwdBr9EhEvQrNbu84AdkqvzwIr08/vSVokae+IeDL3AklfAr4E0Hebfs25V/vSyHQTDX/2dZk6Fi94hWtvuZPlS1/j5GMmcef0GRw4/hCen/00Jx55KFv27sPoMXtRWVXZWiW3UtLI72vDqUwqqirZetgQfjVuMr0GbsM3H76Bi0cexvtvvsX3tt2fN5etoM+QQXzj/r/z2vMvsXLBktYqvZW6ht9TqqCie2/eveNPqGsPuh31Rd6+6bfrW9r0iW5U9OpLXfW8NihsxxVRvOWmIqJO0jnAVJLxaFdHxAuSLgZmRcSU9NxESXOADPCtiFgFIOmbwH1K/kF7Cvhjvnt25Ja1Qowl+UEWYoikZyQ9JOlAAEk903M/kvS0pBsk5f5JP4uk73oDEXFlRIyJiDE9e/X6WA9Qzrbp159lr1Wv31++bClbNwhet+k3gEMOP5JOnToxaLvBDN1hGIsWvALAf3zjW9z2wKP85cZbCYLBQ7dv1fKXlo47z9qa6uX0GtR//X7Pgf1Yu3TFBmnWVi/n2Vunka2rY9Wial6fu4Cthw0G4M1lSdqVC1/l5QdnsO3oXVqt7FZa4r23UNce6/crunQn3nt7gzTZd9+idsmLEFninTVk31xJZfcPx0B2GrordYvnJHNJdGBtMfVjZLMFb3nzirgzIoZHxPYR8ZP02AVpoEYkzo2IERGxa0Rcn3PttIgYlR7/fETU5Lufg7VN6we8kTcVLAO2jYjRwLnAdZK6k7RcDgQejYg9gMeBX+ZctwLo3zAzS+w6ek8WLVjAq4sXUVNTwx03/4tDDttwoPehRxzJE48kvdKrV61i4SvzGbTdYDKZDGtWJ+OOXnrh38yd8wIHjDuk1Z+hVGQ78KS4i2c+y9bDBtN78EAqO3Vir8lH89yUaRukmX3LPQwfvx8AXXv3YuvhQ1i5YAldenanqnPn9ce3H7sny+Z08BaRDlyXMm+8RmX33qhbL6iopNPQXald8tIGaeoWv0hVv6EAaLMuVHTvQ/bt1evPdxo6ihp3gZItICAqqgiymcK3UtNhu0EL9D6web5EEbEOWJd+fkrSKyT9008B7wE3p0lvAM7MuXTz9B7WiKqqKi685FLOOOk4MpkMJ5xyGsN22pn/ueTH7Lr7Hhwy6QgOHP9JHnngfiYdsBeVlZV858If0WvL3qz74ANOPiYZkthtiy345RV/pKrK1b0jymYy/OOcC/ja1L9SUVnJY1f/k2Vz5nH0D7/B4lnP89xt9zJn6kOMmHggF74wjWwmw03f+hnvrl7L0P324LP/76dENlCFuPuS32/wFql1MJHl/cdvp+uk00EV1L78FNm1K9hsj0PIrHyNuiUvUffaPKoG7kC3T38NIssHM+8m1iVf8+rWk4quPcgsW9Smj9EhBWW93JQ68jI06Zi12yNiZBPnzwYGRsQPGhx/EPhmRMxK97cCVkdERtJQ4GFg14hYnU6Wd2VE3C/p88CREfGZ9LrfkrS6XU8TdhqxS9z+0IyP+aTW0d36818y71d/aetiWDtwxiWfYsc+3dq6GFbmFnUdyO4nf+WpTUyPUVTDNusS/9tv45c9mnLE4udarWyF6LDdoJL+TtItuaOkaklnNpLsLuCgnGuOk1QN7AfcIWlqeuog4DlJzwI3AmdHRH2793eAiyQ9B5wGnJeT/1iSCfHMzMyspUSQqckWvJWaDtsvFBEnF5BmsaRVkoZFxLyIuJkPuzRz0/0L+FdTeZAT8NWTNBp4ISJWNr/0ZmZmVqiI8h6722GDtWY4n+RFg2KPKu4D/HeR8zRrVIU6bCO6FVsHnqjbiqeysvW/kzIO1tqviJhLMvNwsfOdlj+VmZmZfVwBlPH7BQ7WzMzMrP1zy5qZlbSO/Na3FZfrkpUjt6xZi/IXoxWD65EVi+uSlaMIt6yZmZmZlTS3rJmZmZmVqCDcsmZmZmZWqjxmzVqUPKeRFYHrkRWL65KVKwdrZlbS/A+sFYvrkpUjv2BgZmZmVsICqMk6WDMzMzMrSR6zZmYlLyjjbykza3eybdDK5W5QMytpnsjUisZ1yYqgtb+TkjFrrXrLonKwZmZmZu2eW9bMzMzMSlQA2bYuxMfgYM3MzMzaOa9gYGZmHYXnWbMiaO35+vw2qJmVvApVtHURzMzWq6hoi2CtfKM1B2tmZmbWvpX526D+c9vMzMzatfqWtUK3fCRNkjRX0nxJ5zeR5kRJcyS9IOm6nOMZSbPTbUoh5XfLWonz/FhWDNlsOb8HZaXEdcnKUTGXm5JUCVwBHApUAzMlTYmIOTlphgHfBcZGxBpJW+dk8X5E7N6cezpYK3GZujqWvbqIyGY3CtwkoYrmNY5mM5mNjlVUVDRv0HBEo1/YFZWVzSqLn6lpxX6md2o/oOsuQ5pzKWy06oGaPba8sT82hKA5+UTjKzA0d4Cyn2lT+RT+TEveyfCJHp2S64hGZ6KvbG59j2j096aimQ+Saex3uELJsxTIz9S0Yj5TTbfW7dgr8gsGewPzI2IBgKTrgWOBOTlpzgKuiIg1ABGx4uPcUG65KW2S3gAWt3U5SlwfYGVbF8LaBdclKwbXo8JsFxFbtcaNJN1N8v+lUJsDH+TsXxkRV6Z5nQBMiogvpvunAftExDk597sFeBkYC1QCF0XE3em5OmA2UAdcEhG35CuMW9ZKXGtV5HImaVZEjGnrclj5c12yYnA9Kj0RMamI2TXWlNiw5asKGAaMAwYCD0saGRFrgW0jYqmkocD9kp6PiFc2dUO/YGBmZmZWuGpgUM7+QGBpI2lujYjaiFgIzCUJ3oiIpel/FwAPAqPz3dDBmpmZmVnhZgLDJA2R1BmYDDR8q/MWYDyA/n979x97VV3Hcfz5yh+hokW6mmJIRVgISgXNspUVfddP0Whh5QrNLRfK+qH9mNpM+6HVXAK5cokkM2OYbdRSVMASBIXkZ/7IDXFptaE0TcYw6dUf53PX4fr9fgn4cu/9Xl+P7W7nfD73nvs53+9n577P53POeUtHAaOBTZKGSXp5rfwUdr3WrVeZBo1ucF27GxBdI30pBkL6URez/YKk84FFVNejzbH9Z0mXA6ttLyx1PZIeBHYCF9l+WtI7gZ9J+g/VgNmV9btI+5IbDCIiIiI6WKZBIyIiIjpYgrWIiIiIDpZgLVqqlmZjo6QFkg4dgG1OkDSzn/pjJN2ydPTm2AAABoRJREFUr98Tg0dTP/utpFcO8PanSZpdli+TdOFAbj9aqyn9z1pJIyUdKWmppOca/+uIdkmwFq223fZ422OB54Hz6pWq7FG/tL3a9ox+6v9m+xN719wYpOr9bCswvd0Nio7W6C+N12aqB6JeCrQ0EJeUG//iRRKsRTvdA4wqZ7EPSboWeAB4raQeSSskPVBG4IYCSJoo6V5J6yTdL+lwSadK+l2pf0/t7HhNqR8paWOpHyLpBkkbSn3j1uppkm6VdLukRyX9oE1/kxh4K4DhjRVJF0laJWm9pG/Xyj9bytZJmlfKPibpvtJX7pL0mja0P9rA9jbby9j1KfYvIumEcixaW/rPG0t5b/3pOEmLS/liSSNK+VxJV0taClwl6TBJc0o/XSNp8v7e3+hsieCjLcrZ44eA20vR8cDZtr9Ynj1zCTDJ9jZJXwe+IulKYD4w1fYqSUcA25s2fSEw3fbyEuA1H2inA9geJ+lNwB2SRpe68VQPJ9wBPCJplu2/DuiOR0upSrj8fuD6st5D9WDKt1M9hXyhpHcDTwMXUyVdfkrSq8omlgEn27akc4GvAV9t8W7E/neIpLVl+THbZ+zBZ88DrrF9U3nm1gGSTqD3/jQbuNH2LySdA8wETi91o6mOeTslfQ9YYvucMoV/v6S7bG/b1x2NwSnBWrRa/aB4D9WP6DHA47ZXlvKTgTHAclUJgg+mGh05Hvi77VUAtp+FFyW/Xg5cLekm4FbbTzTVvwuYVT7/sKTHqQ6SAIttP1O2+SBwHJBgbXBq9LORwJ+AO0t5T3mtKetDqYK3k4BbbD8FYHtrqT8WmC/paKp++FhLWh+ttt32+L387ArgYknHUh1zHpX0PnrvT+8APl6W5wH1EfwFtneW5R7gtNq1kEOAEcBDe9nGGOQyDRqtVr825ALbz5fy+hmjgDtr7xtj+/OlvN8HA9q+EjgXOARYWUbP6nrL6dawo7a8k5zMDGaNH9/jqIKsxjVrAr5f61ujbF9P331rFjDb9jjgC1Q/mvESJumM2qUWE2z/EjiNapR/UQnUdnusKurvaT4GTqn10xG2E6i9hCVYi060EjhF0igASYeWqcqHgWMkTSzlhzdfjCvpDbY32L4KWA00B2t/BD5T3jua6mz1kf26N9E2ZaR0BnChpIOonip+Tu0ayOGSXg0sBj4p6chS3pi2egXwZFn+XEsbHx3J9m9qQdRqVcm4N9meSZVy6ET67k/3UqUmguo4tKyPr1kEXKAyLSBpt7kjo7tl5CA6ju0tkqYBN6vkUAMusf0XSVOBWZIOoTqTndT08S+VmwZ2UuVbuw04ulZ/LfBTSRuAF4Bptnc0TZVGF7G9RtI64Ezb8yS9GVhR/ufPAWeVVDHfBf4gaSfVNOk04DJggaQnqU4iXteOfYj2kLQZOAI4WNLpQE8vqYGmAmdJ+jfwD+By21v76E8zgDmSLgK2AGf38dVXAD8G1peAbTPw0YHctxhckm4qIiIiooNlGjQiIiKigyVYi4iIiOhgCdYiIiIiOliCtYiIiIgOlmAtIiIiooMlWIuIriDJjRyMZf1ASVtU8sb287nxkj7cT/0ESTMHsq0REXsiwVpEdIttwNjyDD6AD/C/B9r2ZzzQa7Am6UDbq23PGKA2RkTssQRrEdFNbgM+UpY/BdzcqJB0mKQ5klZJWiNpckm8fTkwtaQPmirpMknXSboDuFHSqY3ROUlDJd0gaYOk9ZKmSDpA0lxJG0v5l1u90xHR3RKsRUQ3+RVwpqQhVGl/7qvVXQwssT0ReC/wQ+Ag4FvA/JI+aH5579uAybY/3bT9S4FnbI+zfSKwhGpkbrjtsSWH6A37a+ci4qUpwVpEdA3b64GRVKNqv2+q7gG+IWktcDdVUvYRfWxqoe3tvZRPAn5S+75/ApuA10uaJemDwLP7sg8REc0SrEVEt1kI/IjaFGghYEotCfcI2w/1sY1tfZQL2CVHXwnYTqIKAKcDP9/bhkdE9CbBWkR0mzlUybQ3NJUvAi4oibGR9JZS/i/g8P9z23cA5zdWJA2TdBTwMtu/ppomfeu+ND4iolmCtYjoKrafsH1NL1VXUF2jtl7SxrIOsBQY07jBYDeb/w4wrNxMsI7q2rfhwN1lenUu8M2B2I+IiAbZ3v27IiIiIqItMrIWERER0cESrEVERER0sARrERERER0swVpEREREB0uwFhEREdHBEqxFREREdLAEaxEREREd7L/2koowmNSEGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 708.661x311.811 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "svc2 = SVC(C = 1.4, gamma =  0.1, kernel= 'linear')\n",
    "svc2.fit(x_train, y_train)\n",
    "pred_svc2 = svc2.predict(x_test)\n",
    "dt_acc_score = accuracy_score(y_test, pred_svc2)\n",
    "print(\"Acuracy :\", dt_acc_score*100)\n",
    "plot_confusion_matrix(dt,x_test,y_test)\n",
    "print(classification_report(y_test, dt_predict))\n",
    "print(\"Confusion Matrix\")\n",
    "plot_classification_report(classification_report(y_test, dt_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Comaprison of performances of all the models used**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#List of all the models\n",
    "classifier =[ DecisionTreeClassifier(random_state=73),\n",
    "            RandomForestClassifier(random_state=73,n_jobs=-1),\n",
    "            GradientBoostingClassifier(random_state=73),\n",
    "            SVC(C = 1.4, gamma =  0.1, kernel= 'linear')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_columns = []\n",
    "classifier_compare = pd.DataFrame(columns = classifier_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>Classifier Name</th>\n",
       "      <th>Classifier Train Accuracy</th>\n",
       "      <th>Classifier Test Accuracy</th>\n",
       "      <th>Classifier Precission</th>\n",
       "      <th>Classifier Recall</th>\n",
       "      <th>Classifier F1 score</th>\n",
       "      <th>Classifier AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.7812</td>\n",
       "      <td>0.781289</td>\n",
       "      <td>0.781250</td>\n",
       "      <td>0.781147</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.8632</td>\n",
       "      <td>0.7406</td>\n",
       "      <td>0.742154</td>\n",
       "      <td>0.740625</td>\n",
       "      <td>0.739747</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.6844</td>\n",
       "      <td>0.684515</td>\n",
       "      <td>0.684375</td>\n",
       "      <td>0.683902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.7138</td>\n",
       "      <td>0.6750</td>\n",
       "      <td>0.679848</td>\n",
       "      <td>0.675000</td>\n",
       "      <td>0.671330</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Classifier Name  Classifier Train Accuracy  \\\n",
       "1      RandomForestClassifier                     1.0000   \n",
       "2  GradientBoostingClassifier                     0.8632   \n",
       "0      DecisionTreeClassifier                     1.0000   \n",
       "3                         SVC                     0.7138   \n",
       "\n",
       "   Classifier Test Accuracy  Classifier Precission  Classifier Recall  \\\n",
       "1                    0.7812               0.781289           0.781250   \n",
       "2                    0.7406               0.742154           0.740625   \n",
       "0                    0.6844               0.684515           0.684375   \n",
       "3                    0.6750               0.679848           0.675000   \n",
       "\n",
       "   Classifier F1 score  Classifier AUC  \n",
       "1             0.781147             NaN  \n",
       "2             0.739747             NaN  \n",
       "0             0.683902             NaN  \n",
       "3             0.671330             NaN  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluation of model based on various parameters\n",
    "# Generate the values for those parameters for respective models\n",
    "row_index = 0\n",
    "for i in classifier:\n",
    "    y_pred = i.fit(x_train, y_train).predict(x_test)\n",
    "    new_y=[int(j) for j in y_pred]\n",
    "    #fp, tp, th = roc_curve(y_test, new_y,pos_label=1,sample_weight=None)\n",
    "    #print(tp)\n",
    "    classifier_name = i.__class__.__name__\n",
    "    classifier_compare.loc[row_index,'Classifier Name'] = classifier_name\n",
    "    classifier_compare.loc[row_index, 'Classifier Train Accuracy'] = round(i.score(x_train, y_train), 4)\n",
    "    classifier_compare.loc[row_index, 'Classifier Test Accuracy'] = round(i.score(x_test, y_test), 4)\n",
    "    classifier_compare.loc[row_index, 'Classifier Precission'] = precision_score(y_test, y_pred,average='weighted')\n",
    "    classifier_compare.loc[row_index, 'Classifier Recall'] = recall_score(y_test, y_pred,average='weighted')\n",
    "    classifier_compare.loc[row_index, 'Classifier F1 score'] = f1_score(y_test, y_pred,average='weighted')\n",
    "    #classifier_compare.loc[row_index, 'Classifier AUC'] = auc(fp, tp)\n",
    "    row_index+=1\n",
    "    \n",
    "#Print the values in the order of accuracy score\n",
    "classifier_compare.sort_values(by = ['Classifier Test Accuracy'], ascending = False, inplace = True)    \n",
    "classifier_compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Comparison based on Train accuracy\n",
    "plt.subplots(figsize=(8,6))\n",
    "sns.barplot(x=\"Classifier Name\", y=\"Classifier Train Accuracy\",data=classifier_compare,palette='hot',edgecolor=sns.color_palette('dark',7))\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Classifier Train Accuracy Comparison')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Order based on Train Accuracy-score:**\n",
    "\n",
    "**Random Forest >=  Decision Tree > RGB > SVC**\n",
    "\n",
    "*The decision tree and random forest can have high variance(overfitting) and hence it contributes in very high train accuracy*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Comparison based on Test accuracy\n",
    "plt.subplots(figsize=(8,6))\n",
    "sns.barplot(x=\"Classifier Name\", y=\"Classifier Test Accuracy\",data=classifier_compare,palette='hot',edgecolor=sns.color_palette('dark',7))\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Classifier Test Accuracy Comparison')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Order based on Test Accuracy-score:**\n",
    "**Random Forest > RGB > Decision Tree > SVC**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Comparison based on Precision \n",
    "plt.subplots(figsize=(8,6))\n",
    "sns.barplot(x=\"Classifier Name\", y=\"Classifier Precission\",data=classifier_compare,palette='hot',edgecolor=sns.color_palette('dark',7))\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Classifier Precision Comparison')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Order based on Precision:**\n",
    "**Random Forest > RGB > Decision Tree > SVC**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Comparison based on Recall\n",
    "plt.subplots(figsize=(8,6))\n",
    "sns.barplot(x=\"Classifier Name\", y=\"Classifier Recall\",data=classifier_compare,palette='hot',edgecolor=sns.color_palette('dark',7))\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Classifier Recall Comparison')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Order based on Recall-score:**\n",
    "**Random Forest > RGB > Decision Tree > SVC**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Comparison based on F1-score\n",
    "plt.subplots(figsize=(8,6))\n",
    "sns.barplot(x=\"Classifier Name\", y=\"Classifier F1 score\",data=classifier_compare,palette='hot',edgecolor=sns.color_palette('dark',7))\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Classifier F1 Score Comparison')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Order based on F1-score:**\n",
    "**Random Forest > RGB > Decision Tree > SVC**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Results & Discussion**\n",
    "\n",
    "**Behaviour of Random Forest**\n",
    "Random Forest classifier has been the best model for wine quality prediction. Random forests use bootstrap aggregating(bagging) technique to train the model over the training set.\n",
    "\n",
    "A single decision tree may have higher variance(overfitting) but in a random forest, multiple trees are generated. Polling is done to find the most probable answer. Though dataset has low dimensionality, random forests are helpful for high dimensional datasets.\n",
    "\n",
    "\n",
    "**Behaviour of SVM**\n",
    "SVM performs better when the margin of separation is higher. Here, the margin is low and not easy to distinguish in two classes.\n",
    "SVM is more effective in high dimensional space. In give dataset, there are lesser attributes. Also, there is no probabilistic explanation for its behaviour.\n",
    "\n",
    "**Behaviour of Decision Tree**\n",
    "Decision tree here overfits the training set and hence gives 100% training accuracy. Since it overfits, it always has performed worse than RGB or random forests though it performs better than SVC.\n",
    "\n",
    "**Behaviour of RGB**\n",
    "RGB performs better than decision tree and SVC but not as that of a random forest. This can be attributed to its parameters and overfitting of the model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Summary**\n",
    "\n",
    "**Four of the popular supervised learning models were used to predict the quality of the wine. <br>\n",
    "The initial dataset was highly biased because it had a majority of samples belonging to three classes only. <br>\n",
    "We converted it into a two-class dataset and applied classification techniques to build a precise model. We achieved a test accuracy of nearly 78% with Random forests, which was highest among all the used models.**"
   ]
  },
  {
   "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
   },
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