winequality.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the quality of Wine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data exploration , Preprocessing and cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading dataset\n",
    "wine = pd.read_csv('14winequality-red (1).csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's check how the data is distributed\n",
    "wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
       "       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
       "       'pH', 'sulphates', 'alcohol', 'quality'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 4, 8, 3], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "wine['quality'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1599.000000\n",
       "mean        5.636023\n",
       "std         0.807569\n",
       "min         3.000000\n",
       "25%         5.000000\n",
       "50%         6.000000\n",
       "75%         6.000000\n",
       "max         8.000000\n",
       "Name: quality, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine['quality'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine['quality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fixed acidity           False\n",
       "volatile acidity        False\n",
       "citric acid             False\n",
       "residual sugar          False\n",
       "chlorides               False\n",
       "free sulfur dioxide     False\n",
       "total sulfur dioxide    False\n",
       "density                 False\n",
       "pH                      False\n",
       "sulphates               False\n",
       "alcohol                 False\n",
       "quality                 False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ": boolean\n",
      "    use_inf_as_null had been deprecated and will be removed in a future\n",
      "    version. Use `use_inf_as_na` instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import warnings                              #removes the future warning tabs\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "import pandas as pd\n",
    "pd.reset_option('all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_bins = 10\n",
    "\n",
    "wine.hist(bins=num_bins, figsize=(20,15))\n",
    "plt.savefig(\"wine_histogram_plots\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x254b7043208>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0DgCA0SY9x6uqjktyQpKLp8wBADDCZMWrqv5mkvck+Y3u/s5ePj+jqnZW1c7du3ePDwgAcJBNUryq6l5ZKV3v6O737m2f7j63u3d0947t27ePDQgAsARTXNVYSf4gyRXd/R9HHx8AYCpTjHidmOT0JE+uqssWj2dMkAMAYKjhy0l096eS1OjjAgBMzcr1AACDKF4AAIMoXgAAgyheAACDKF4AAIMoXgAAgyheAACDKF4AAIMoXgAAgyheAACDKF4AAIMoXgAAgyheAACDKF4AAIMoXgAAgyheAACDKF4AAIMoXgAAgyheAACDKF4AAIMoXgAAgyheAACDKF4AAIMoXgAAgyheAACDKF4AAIMoXgAAgyheAACDKF4AAIMoXgAAgyheAACDKF4AAIMoXgAAgyheAACDKF4AAIMoXgAAgyheAACDKF4AAIMoXgAAgyheAACDKF4AAIMoXgAAgyheAACDKF4AAIMoXgAAgyheAACDKF4AAIMoXgAAgyheAACDKF4AAIMoXgAAgyheAACDKF4AAIMoXgAAgyheAACDKF4AAINMUryq6mlV9ZWq+suqevkUGQAARhtevKpqS5L/nOTpSR6e5IVV9fDROQAARptixOuxSf6yu7/a3bckeWeSUybIAQAw1BTF6wFJvrHH9tWL9wAAZq26e+wBq56X5Knd/ZLF9ulJHtvdL73LfmckOWOxeXySrwwNenDcL8kNU4fYZHzn4/nOx/Odj+c7H28jf+cP7O7te/tg6+gkWRnh+ok9to9N8s277tTd5yY5d1SoZaiqnd29Y+ocm4nvfDzf+Xi+8/F85+PN9TufYqrxfyZ5SFU9qKoOTfKCJO+fIAcAwFDDR7y6+7aq+vUkf5JkS5I3dfcXR+cAABhtiqnGdPeHknxoimMPtqGnSjco3/l4vvPxfOfj+c7Hm+V3PvzkegCAzcotgwAABlG8lqCqDquqz1bV56vqi1X1r6bOtFlU1Zaq+lxVfXDqLJtBVV1ZVV+oqsuqaufUeTaDqjqqqi6oqi9X1RVV9TNTZ5qzqjp+8ef7jsd3quo3ps41d1V11uLfz8ur6vyqOmzqTAeLqcYlqKpKsq27v1tV90ryqSRndvefTxxt9qrqnyfZkeTI7n7W1HnmrqquTLKjuzfqWjsbTlW9Ncknu/u8xZXhh3f3jVPn2gwWt7y7Jsnf7e6vT51nrqrqAVn5d/Ph3f29qnpXkg9191umTXZwGPFagl7x3cXmvRYPDXfJqurYJM9Mct7UWWAZqurIJE9I8gdJ0t23KF1DnZTk/yhdQ2xNcu+q2prk8Oxlvc+NSvFaksWU12VJrk9yYXdfPHWmTeA1Sc5OcvvUQTaRTvKRqrpkcbcJluvBSXYnefNiSv28qto2dahN5AVJzp86xNx19zVJXp3kqiTXJvl2d39k2lQHj+K1JN39g+5+VFZW5n9sVT1i6kxzVlXPSnJ9d18ydZZN5sTufnSSpyf5tap6wtSBZm5rkkcneUN3n5Dk5iQvnzbS5rCY1n12kndPnWXuquroJKckeVCSH0+yrapOmzbVwaN4LdliGuCiJE+bOMrcnZjk2Ytzjt6Z5MlV9fZpI81fd39z8Xx9kvcleey0iWbv6iRX7zGCfkFWihjL9/Qkl3b3dVMH2QSekuRr3b27u29N8t4kPztxpoNG8VqCqtpeVUctXt87K3+Ivjxtqnnr7ld097HdfVxWpgP+tLtn8z+k9aiqtlXVEXe8TvJzSS6fNtW8dfeuJN+oquMXb52U5EsTRtpMXhjTjKNcleRxVXX44mK1k5JcMXGmg2aSles3gfsneeviCphDkryruy1vwNz8WJL3rfy9mK1J/mt3f3jaSJvCS5O8YzH19dUkL544z+xV1eFJTk7yT6fOshl098VVdUGSS5PcluRzmdEq9paTAAAYxFQjAMAgihcAwCCKFwDAIIoXAMAgihcAwCCKF7CpVdVxVXX54vWOqnrt4vWTqmo2izYC64N1vAAWuntnkp2LzScl+W6SP5ssEDA7RryADauqXllVX6mq/1FV51fVb1XVRVW1Y/H5/Ra3kbpjZOuTVXXp4vFDo1mLUa4PVtVxSf5ZkrOq6rKqenxVfa2q7rXY78iquvKObYC1MuIFbEhV9Zis3B7qhKz8XXZpktVukn59kpO7+/tV9ZCs3P5lx9527O4rq+qNSb7b3a9eHO+iJM9M8seL475ncR85gDUz4gVsVI9P8r7u/r/d/Z0k79/P/vdK8vtV9YUk707y8AM83nn569vzvDjJmw/w5wGMeAEb2t7ueXZb/vo/lYft8f5ZSa5L8tOLz79/QAfq/vRiuvKJSbZ0txuCAwfMiBewUX0iyS9W1b2r6ogkP794/8okj1m8fu4e+98nybXdfXuS05Ns2c/vvynJEXd57w+zMkVptAu4WxQvYEPq7kuT/FGSy5K8J8knFx+9OsmvVtWfJbnfHj/y+iQvqqo/T/LQJDfv5xAfyEqxu6yqHr947x1Jjs5K+QI4YNW9t5F6gI2lqn43e5wMv6RjPDfJKd19+rKOAcybc7wA1qCqXpfk6UmeMXUWYOMy4gUAMIhzvAAABlG8AAAGUbwAAAZRvAAABlG8AAAGUbwAAAb5fzxyzSf8fpkuAAAAAElFTkSuQmCC\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 = wine)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Here we see that fixed acidity does not give any specification to classify the quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x254b6941408>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAFzCAYAAAB2A95GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAY9klEQVR4nO3de7BlZ1km8OdNhxDIJARNa2sSTNSIRgdF2wyaERgBTRCTGgxO4gDKMEQtAoqXrljMoMDMVE2D5QVRJ4NcFCWGKBqsaLwglwHBdCACSUxVGwLpDsc0IHcQkrzzx9nBQ+d0n92x1/lO7/P7VXXtvdb69t5PdqU6T75v7bWquwMAwPo6anQAAIDNSAkDABhACQMAGEAJAwAYQAkDABhACQMAGODo0QEO1UknndSnnXba6BgAAGu67rrrPtTdW1c7dsSVsNNOOy27du0aHQMAYE1V9f4DHbMcCQAwgBIGADCAEgYAMIASBgAwgBIGADCAEgYAMIASBgAwgBIGADCAEgYAMIASBgAwgBIGADCAEgYAMMARdwPvI9WOHTuytLSUbdu2ZefOnaPjAACDKWHrZGlpKXv37h0dAwDYICxHAgAMoIQBAAyghAEADKCEAQAMoIQBAAyghAEADKCEAQAMoIQBAAyghAEADKCEAQAMoIQBAAyghAEADKCEAQAMoIQBAAyghAEADKCEAQAMoIQBAAyghAEADKCEAQAMoIQBAAyghAEADKCEAQAMoIQBAAyghAEADDBZCauql1fVHVX13gMcr6r61araXVXvrqpvnSoLAMBGM+VM2CuTnHOQ4+cmOWP25+IkvzFhFgCADWWyEtbdb07ykYMMOT/Jb/eytyc5saq+Yqo8AAAbychzwk5OctuK7T2zfQAAC29kCatV9vWqA6surqpdVbVr3759E8cCAJjeyBK2J8mpK7ZPSXL7agO7+7Lu3t7d27du3bou4QAApjSyhF2V5KmzX0k+IsnHuvuDA/MAAKybo6d646p6TZJHJzmpqvYk+fkk90uS7v7NJFcneXyS3Uk+neRpU2UBANhoJith3X3RGsc7yTOn+nwAgI3MFfMBAAZQwgAABlDCAAAGUMIAAAZQwgAABlDCAAAGUMIAAAZQwgAABlDCAAAGUMIAAAZQwgAABlDCAAAGUMIAAAZQwgAABlDCAAAGUMIAAAZQwgAABlDCAAAGUMIAAAZQwgAABlDCAAAGUMIAAAZQwgAABlDCAAAGUMIAAAZQwgAABlDCAAAGOHp0gCl828/+9ugI93L8hz6RLUk+8KFPbKh8173oqaMjAMCmZCYMAGAAJQwAYAAlDABgACUMAGAAJQwAYAAlDABgACUMAGAAJQwAYAAlDABgACUMAGAAJQwAYAAlDABgACUMAGAAJQwAYAAlDABgACUMAGAAJQwAYAAlDABgACUMAGAAJQwAYIBJS1hVnVNVN1fV7qq6dJXjD6mqv66qd1XVu6vq8VPmAQDYKCYrYVW1JclLk5yb5MwkF1XVmfsN+29Jrujuhye5MMmvT5UHAGAjmXIm7Kwku7v7lu7+XJLLk5y/35hOcsLs+YOS3D5hHgCADWPKEnZykttWbO+Z7VvpF5I8uar2JLk6ybNWe6OquriqdlXVrn379k2RFQBgXU1ZwmqVfb3f9kVJXtndpyR5fJLfqap7Zeruy7p7e3dv37p16wRRAQDW15QlbE+SU1dsn5J7Lzc+PckVSdLdf5Pk2CQnTZgJAGBDmLKEXZvkjKo6vaqOyfKJ91ftN+YDSR6TJFX1DVkuYdYbAYCFN1kJ6+47k1yS5JokN2X5V5A3VNULquq82bCfTvKMqvq7JK9J8iPdvf+SJQDAwjl6yjfv7quzfML9yn3PW/H8xiRnT5kBAGAjcsV8AIABlDAAgAGUMACAAZQwAIABlDAAgAGUMACAASa9RAWMtGPHjiwtLWXbtm3ZuXPn6DgA8EWUMBbW0tJS9u7dOzoGAKzKciQAwABKGADAAEoYAMAAShgAwABKGADAAEoYAMAAShgAwABKGADAAEoYAMAAShgAwABKGADAAEoYAMAAShgAwABKGADAAGuWsKraVVXPrKoHr0cgAIDNYJ6ZsAuTfGWSa6vq8qr63qqqiXMBACy0o9ca0N27kzy3qv57kickeXmSu6vq5Ul+pbs/MnFGjgAfeMG/HR3hXu78yJckOTp3fuT9GyrfQ573ntERANgA5jonrKoeluQXk7woyR8kuSDJx5O8Ybpoi+XuY47LXfc/IXcfc9zoKADABrDmTFhVXZfko0l+K8ml3f3Ps0PvqKqzpwy3SD51xveMjgAAbCBrlrAkT+ruW1buqKrTu/t93f3EiXIBACy0eZYjr5xzHwAAczrgTFhVfX2Sb0zyoKpaOeN1QpJjpw4GALDIDrYc+dAs/xryxCTfv2L/J5I8Y8pQAACL7oAlrLv/OMkfV9V3dPffrGMmAICFd7DlyB3dvTPJD1XVRfsf7+5nT5oMAGCBHWw58qbZ4671CAIAsJkcbDny9bPHV61fHACAzeFgy5GvT9IHOt7d502SCABgEzjYcuSLZ49PTLItyatn2xcluXXCTAAAC+9gy5FvSpKqemF3P3LFoddX1ZsnTwYAsMDmuWL+1qr66ns2qur0JFuniwQAsPjmuXfkc5K8saruuX/kaUl+dLJEAACbwJolrLv/rKrOSPL1s11/393/PG0sAIDFdrBfR353d79hv/tGJsnXVFW6+w8nzgYAsLAONhP2qCRvyBffN/IenUQJY0M76di7k9w5ewSAjeVgv478+dnj09YvDhw+P/Owj46OAAAHtOavI6vqf1XViSu2H1xV/2PaWAAAi22eS1Sc291fmFLo7n9K8vjpIgEALL55StiWqrr/PRtV9YAk9z/IeAAA1jBPCXt1kr+qqqdX1X9J8hdJ5rqpd1WdU1U3V9Xuqrr0AGN+sKpurKobqur35o8OAHDkmuc6YTur6j1JHpOkkrywu69Z63VVtSXJS5M8LsmeJNdW1VXdfeOKMWck+bkkZ3f3P1XVl93Hfw4AgCPKPFfMT3f/aZI/PcT3PivJ7u6+JUmq6vIk5ye5ccWYZyR56ew8s3T3HYf4GQAAR6R5fh35iKq6tqo+WVWfq6q7qurjc7z3yUluW7G9Z7Zvpa9L8nVV9daqentVnTN/dACAI9c8M2G/luTCJK9Nsj3JU5N87Ryvq1X29Sqff0aSRyc5JclbquqbVv4aM0mq6uIkFyfJQx7ykDk+GgBgY5vnxPx09+4kW7r7ru5+RZL/MMfL9iQ5dcX2KUluX2XMH3f357v7fUluznIp2//zL+vu7d29fevWrfNEBgDY0OaZCft0VR2T5Pqq2pnkg0mOm+N11yY5o6pOT7I3y7NpP7TfmD9KclGSV1bVSVlenrxl3vDAxrJjx44sLS1l27Zt2blz5+g4ABvaPDNhT5mNuyTJp7I8u/UDa72ou++cveaaJDcluaK7b6iqF1TVebNh1yT5cFXdmOSvk/xsd3/40P8xgI1gaWkpe/fuzdLS0ugoABvePJeoeP/s6WeTPP9Q3ry7r05y9X77nrfieSf5qdkfAIBNY65zwgAAOLzmuk4YsPGc/ZKzR0e4l2M+ekyOylG57aO3bah8b33WW0dHALiXuWfCqmqek/EBAJjDPBdr/c7ZifM3zba/uap+ffJkAAALbJ6ZsF9K8r1JPpwk3f13SR45ZSgAgEU3770jb6v6ogvg3zVNHOBI1g/s3J270w/c/+YYAOxvnhJ2W1V9Z5KeXbT12ZktTQKs9PmzPz86AsARY57lyB9L8sws33x7T5JvmW0DAHAfzXOx1g8l+c/rkAUAYNM4YAmrqpckOeCJHd397EkSAQBsAgebCdu1bikAADaZA5aw7n7VegYBANhMDrYc+cvd/ZNV9fqssizZ3edNmgwAYIEdbDnyd2aPL16PIAAAm8nBliOvmz39lu7+lZXHquonkrxpymAAAItsnuuE/fAq+37kMOcAANhUDnZO2EVJfijJ6VV11YpDx2d2H0kAAO6bg50T9rYkH0xyUpJfXLH/E0nePWUoAIBFd7Bzwt6f5P1JvmP94gAAbA5r3raoqh6R5CVJviHJMUm2JPlUd58wcTYA1rBjx44sLS1l27Zt2blz5+g4wCFYs4Ql+bUkFyZ5bZLtSZ6a5GunDAXAfJaWlrJ3797RMYD7YJ4Slu7eXVVbuvuuJK+oqrdNnAsAYKHNU8I+XVXHJLm+qnZm+WT946aNBQCw2OYpYU/J8nlglyR5TpJTk/zAlKEAAO6xqOc+rlnCZr+STJLPJHn+tHEANq43PfJRoyPcy2eO3pJU5TN79myofI96s5uqcPgs6rmPB7tY63uyyo2779HdD5skEQDAJnCwmbAnrFsKAIBNZq2LtSZJqurLk3z7bPNvu/uOqYMBACyyNW/gXVU/mORvkzwpyQ8meUdVXTB1MADWdmJ3vqQ7J/YBzx4BNqh5fh353CTffs/sV1VtTfKXSa6cMhgAa3vyXXePjgDcR2vOhCU5ar/lxw/P+ToAAA5gnpmwP6uqa5K8Zrb9n5JcPV0kAIDFN891wn62qp6Y5N8nqSSXdffrJk8GALDA1ixhVfWcJK/t7j9chzwAAJvCPOd2nZDkmqp6S1U9c3a5CgAA/hXWLGHd/fzu/sYkz0zylUneVFV/OXkyAIAFdii/crwjyVKWfx35ZdPEAQDYHOa5WOuPV9Ubk/xVkpOSPMN9IwEA/nXmuUTFVyX5ye6+fuowAACbxTyXqLh0PYIAAGwmrnwPADCAEgYAMIASBgAwwDwn5gMAMzt27MjS0lK2bduWnTt3jo7DEUwJA4BDsLS0lL17946OMZn/+eQLRke4l4/c8bHlx6UPbqh8z331lf+q11uOBAAYQAkDABhACQMAGGDSElZV51TVzVW1u6oOeNHXqrqgqrqqtk+ZBwBgo5ishFXVliQvTXJukjOTXFRVZ64y7vgkz07yjqmyAABsNFP+OvKsJLu7+5YkqarLk5yf5Mb9xr0wyc4kPzNhFgCOQL/2068fHeFePvqhT33hcSPlu+QXv390BA7RlMuRJye5bcX2ntm+L6iqhyc5tbv/5GBvVFUXV9Wuqtq1b9++w58UAGCdTVnCapV9/YWDVUcl+aUkP73WG3X3Zd29vbu3b9269TBGBAAYY8oStifJqSu2T0ly+4rt45N8U5I3VtWtSR6R5Con5wMAm8GUJezaJGdU1elVdUySC5Ncdc/B7v5Yd5/U3ad192lJ3p7kvO7eNWEmAIANYbIS1t13JrkkyTVJbkpyRXffUFUvqKrzpvpcAIAjwaT3juzuq5Ncvd++5x1g7KOnzAIAh8Nxx5zwRY9wX7mBNwAcgrO/5omjI7Ag3LYIAGAAJQwAYAAlDABgACUMAGAAJ+YDABvasVuO+qLHRaGEAQAb2sO/9PjRESaxWJUSAOAIoYQBAAyghAEADKCEAQAMoIQBAAyghAEADKCEAQAMoIQBAAyghAEADKCEAQAMoIQBAAyghAEADKCEAQAMoIQBAAyghAEADKCEAQAMoIQBAAyghAEADKCEAQAMoIQBAAyghAEADKCEAQAMoIQBAAyghAEADKCEAQAMoIQBAAyghAEADKCEAQAMoIQBAAyghAEADKCEAQAMoIQBAAyghAEADKCEAQAMoIQBAAyghAEADKCEAQAMoIQBAAyghAEADKCEAQAMMGkJq6pzqurmqtpdVZeucvynqurGqnp3Vf1VVX3VlHkAADaKyUpYVW1J8tIk5yY5M8lFVXXmfsPelWR7dz8syZVJdk6VBwBgI5lyJuysJLu7+5bu/lySy5Ocv3JAd/91d396tvn2JKdMmAcAYMOYsoSdnOS2Fdt7ZvsO5OlJ/nS1A1V1cVXtqqpd+/btO4wRAQDGmLKE1Sr7etWBVU9Osj3Ji1Y73t2Xdff27t6+devWwxgRAGCMoyd87z1JTl2xfUqS2/cfVFWPTfLcJI/q7n+eMA8AwIYx5UzYtUnOqKrTq+qYJBcmuWrlgKp6eJL/k+S87r5jwiwAABvKZCWsu+9MckmSa5LclOSK7r6hql5QVefNhr0oyb9J8tqqur6qrjrA2wEALJQplyPT3VcnuXq/fc9b8fyxU34+AMBG5Yr5AAADKGEAAAMoYQAAAyhhAAADKGEAAAMoYQAAAyhhAAADKGEAAAMoYQAAAyhhAAADKGEAAAMoYQAAAyhhAAADKGEAAAMoYQAAAyhhAAADKGEAAAMoYQAAAyhhAAADKGEAAAMoYQAAAyhhAAADKGEAAAMoYQAAAyhhAAADKGEAAAMoYQAAAyhhAAADKGEAAAMoYQAAAyhhAAADKGEAAAMoYQAAAyhhAAADKGEAAAMoYQAAAyhhAAADKGEAAAMoYQAAAyhhAAADKGEAAAMoYQAAAyhhAAADKGEAAAMoYQAAAyhhAAADKGEAAANMWsKq6pyqurmqdlfVpascv39V/f7s+Duq6rQp8wAAbBSTlbCq2pLkpUnOTXJmkouq6sz9hj09yT9199cm+aUk/3uqPAAAG8mUM2FnJdnd3bd09+eSXJ7k/P3GnJ/kVbPnVyZ5TFXVhJkAADaEKUvYyUluW7G9Z7Zv1THdfWeSjyX50gkzAQBsCNXd07xx1ZOSfG93/9fZ9lOSnNXdz1ox5obZmD2z7X+Yjfnwfu91cZKLZ5sPTXLzJKGnd1KSD40Oscn4ztef73z9+c7Xn+98/R2p3/lXdffW1Q4cPeGH7kly6ortU5LcfoAxe6rq6CQPSvKR/d+ouy9LctlEOddNVe3q7u2jc2wmvvP15ztff77z9ec7X3+L+J1PuRx5bZIzqur0qjomyYVJrtpvzFVJfnj2/IIkb+ippuYAADaQyWbCuvvOqrokyTVJtiR5eXffUFUvSLKru69K8ltJfqeqdmd5BuzCqfIAAGwkUy5HpruvTnL1fvuet+L5Z5M8acoMG8wRv6R6BPKdrz/f+frzna8/3/n6W7jvfLIT8wEAODC3LQIAGEAJm1hVHVtVf1tVf1dVN1TV80dn2iyqaktVvauq/mR0ls2gqm6tqvdU1fVVtWt0ns2gqk6sqiur6u+r6qaq+o7RmRZZVT109u/3PX8+XlU/OTrXoquq58z++/neqnpNVR07OtPhYjlyYrM7ABzX3Z+sqvsl+X9JfqK73z442sKrqp9Ksj3JCd39hNF5Fl1V3Zpke3cfidfxOSJV1auSvKW7Xzb7FfoDu/ujo3NtBrNb8+1N8u+6+/2j8yyqqjo5y//dPLO7P1NVVyS5urtfOTbZ4WEmbGK97JOzzfvN/mi+E6uqU5J8X5KXjc4CU6iqE5I8Msu/Mk93f04BW1ePSfIPCti6ODrJA2bXE31g7n3N0SOWErYOZsti1ye5I8lfdPc7RmfaBH45yY4kd48Osol0kj+vqutmd7lgWl+dZF+SV8yW3V9WVceNDrWJXJjkNaNDLLru3pvkxUk+kOSDST7W3X8+NtXho4Stg+6+q7u/Jct3DTirqr5pdKZFVlVPSHJHd183Ossmc3Z3f2uSc5M8s6oeOTrQgjs6ybcm+Y3ufniSTyW5dGykzWG29HtekteOzrLoqurBSc5PcnqSr0xyXFU9eWyqw0cJW0ezpYI3JjlncJRFd3aS82bnKF2e5Lur6tVjIy2+7r599nhHktclOWtsooW3J8meFTPrV2a5lDG9c5O8s7v/cXSQTeCxSd7X3fu6+/NJ/jDJdw7OdNgoYROrqq1VdeLs+QOy/C/U349Ntdi6++e6+5TuPi3LSwZv6O6F+T+njaiqjquq4+95nuR7krx3bKrF1t1LSW6rqofOdj0myY0DI20mF8VS5Hr5QJJHVNUDZz90e0ySmwZnOmwmvWI+SZKvSPKq2S9pjkpyRXe7ZAKL5suTvG7578gcneT3uvvPxkbaFJ6V5Hdny2O3JHna4DwLr6oemORxSX50dJbNoLvfUVVXJnlnkjuTvCsLdOV8l6gAABjAciQAwABKGADAAEoYAMAAShgAwABKGADAAEoYwExVnVZV7509315Vvzp7/uiqWpgLRAIbg+uEAayiu3cl2TXbfHSSTyZ527BAwMIxEwYshKp6blXdXFV/WVWvqaqfqao3VtX22fGTZreyumfG6y1V9c7Zn3vNcs1mv/6kqk5L8mNJnlNV11fVd1XV+6rqfrNxJ1TVrfdsA8zLTBhwxKuqb8vyLaoenuW/196Z5GA3cL8jyeO6+7NVdUaWb0GzfbWB3X1rVf1mkk9294tnn/fGJN+X5I9mn/sHs/vaAczNTBiwCL4ryeu6+9Pd/fEkV60x/n5J/m9VvSfJa5OceYif97L8yy2CnpbkFYf4egAzYcDCWO0ebHfmX/5n89gV+5+T5B+TfPPs+GcP6YO63zpb0nxUki3d7WblwCEzEwYsgjcn+Y9V9YCqOj7J98/235rk22bPL1gx/kFJPtjddyd5SpIta7z/J5Icv9++387yMqZZMOA+UcKAI153vzPJ7ye5PskfJHnL7NCLk/x4Vb0tyUkrXvLrSX64qt6e5OuSfGqNj3h9lkve9VX1XbN9v5vkwVkuYgCHrLpXm8EHOHJV1S9kxYn0E33GBUnO7+6nTPUZwGJzThjAIaqqlyQ5N8njR2cBjlxmwgAABnBOGADAAEoYAMAAShgAwABKGADAAEoYAMAAShgAwAD/H19qjscvdrf4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'volatile acidity', data = wine)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Here we see that its quite a downing trend in the volatile acidity as we go higher the quality "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x254b719f848>"
      ]
     },
     "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": [
    "\n",
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'citric acid', data = wine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Composition of citric acid go higher as we go higher in the quality of the wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x254b68ff548>"
      ]
     },
     "execution_count": 15,
     "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 = 'residual sugar', data = wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x254b0ed2208>"
      ]
     },
     "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": [
    "\n",
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'chlorides', data = wine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Composition of chloride also go down as we go higher in the quality of the wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x254b69360c8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAFzCAYAAAB7Ha4BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAcSklEQVR4nO3de7SddX3n8feHBEQyQdAcuXmJOsoMdSriGbRSEUQR8MLYwQ7MqAy1Ul3aqrWTajtLW7tm6lC1rZeRIuJdvFDjoIMCVhGlg20SsWDVioiQhJAA5aqtRr7zx3lSD+Gck004ez+/7P1+rbXXfm77PB/2YuHH57ef55eqQpIkSW3Yre8AkiRJ+jnLmSRJUkMsZ5IkSQ2xnEmSJDXEciZJktQQy5kkSVJDlvYdYDGtWLGiVq5c2XcMSZKkHVq7du1NVTW1/faxKmcrV65kzZo1fceQJEnaoSQ/nGu7w5qSJEkNsZxJkiQ1xHImSZLUEMuZJElSQyxnkiRJDbGcSZIkNcRyJkmS1BDLmSRJUkMsZ5IkSQ2xnEmSJDXEciZJktQQy5kkSVJDxmric0mSNDlWrVrFpk2b2H///TnjjDP6jrNoLGeSJGmXtGnTJjZs2NB3jEXnsKYkSVJDLGeSJEkNsZxJkiQ1xHImSZLUEMuZJElSQyxnkiRJDbGcSZIkNcRyJkmS1BDLmSRJUkMsZ5IkSQ2xnEmSJDXEuTUlDd24Tk4sScNgOZM0dOM6ObEkDYPDmpIkSQ0Z2pWzJOcAzwU2V9Xju22fAA7uDtkHuLWqDp3js9cCdwA/A7ZW1fSwckqSJLVkmMOaHwDeBXxo24aq+k/blpO8Dbhtgc8fXVU3DS1dI/wtjiRJmm1o5ayqLk2ycq59SQL8KvCMYZ1/V+FvcSRJ0mx9/ebsacCNVfW9efYXcFGStUlOH2EuSZKkXvV1t+YpwLkL7D+iqjYmeShwcZLvVNWlcx3YlbfTAR7xiEcsflJJkqQRGvmVsyRLgV8BPjHfMVW1sXvfDKwGDl/g2LOqarqqpqemphY7riRJ0kj1Maz5TOA7VbV+rp1JliVZvm0ZOBa4aoT5JEmSejO0cpbkXOD/AQcnWZ/kpd2uk9luSDPJgUku6Fb3A76W5JvA3wD/t6q+MKyckiRJLRnm3ZqnzLP9v86xbSNwQrd8DfCEYeWSJElqmTMESJIkNcRyJkmS1BDLmSRJUkP6es6ZpCE64p1H9B3hHva4dQ92Yzeuv/X6prJd9puX9R1Bku7FK2eSJEkNsZxJkiQ1xHImSZLUEMuZJElSQyxnkiRJDbGcSZIkNcRyJkmS1BCfcyZJY2jVqlVs2rSJ/fffnzPOOKPvOJLuA8uZJI2hTZs2sWHDhr5jSNoJDmtKkiQ1xHImSZLUEMuZJElSQyxnkiRJDbGcSZIkNcS7NSUNXe1V3M3d1F7VdxRJap7lTNLQ/fSIn/YdQZJ2GQ5rSpIkNcRyJkmS1BDLmSRJUkMsZ5IkSQ2xnEmSJDXEuzUlaRF85cin9x3hHn68dAkk/Hj9+qayPf3Sr/QdYWhWrVrFpk2b2H///TnjjDP6jqNdmOVMkqRFsGnTJjZs2NB3DI0BhzUlSZIaYjmTJElqiOVMkiSpIZYzSZKkhljOJEmSGmI5kyRJasjQylmSc5JsTnLVrG1/kGRDkiu61wnzfPa4JN9NcnWS1w8royRJUmuGeeXsA8Bxc2z/06o6tHtdsP3OJEuAdwPHA4cApyQ5ZIg5JUmSmjG0clZVlwK37MRHDweurqprquonwMeBExc1nCRJUqP6+M3Zq5L8XTfsue8c+w8Crp+1vr7bNqckpydZk2TNli1bFjurJEnSSI26nL0HeAxwKHAD8LY5jskc22q+P1hVZ1XVdFVNT01NLU5KjbVVq1bxkpe8hFWrVvUdRZKkexnp3JpVdeO25STvBT43x2HrgYfPWn8YsHHI0TRBnP9Ok2Cfqnu8S9p1jLScJTmgqm7oVl8AXDXHYX8LPDbJo4ANwMnAfx5RREkaCy/62d19R5C0k4ZWzpKcCxwFrEiyHngTcFSSQ5kZprwW+I3u2AOBs6vqhKramuRVwIXAEuCcqvrWsHJKkiS1ZGjlrKpOmWPz++Y5diNwwqz1C4B7PWZDkiRp3DlDgCRJUkMsZ5IkSQ2xnEmSJDXEciZJktQQy5kkSVJDLGeSJEkNsZxJkiQ1xHImSZLUEMuZJElSQyxnkiRJDRnpxOeSJC2Wd73us31HuIdbb7rrX95byvaqtz2v7wi6j7xyJkmS1BDLmSRJUkMsZ5IkSQ2xnEmSJDXEciZJktQQy5kkSVJDLGeSJEkNsZxJkiQ1xHImSZLUEMuZJElSQyxnkiRJDbGcSZIkNcSJzyVJ0kD+x4tO6jvCPdyy+baZ9003NJXt9z9y3v36vFfOJEmSGmI5kyRJaojlTJIkqSGWM0mSpIZYziRJkhpiOZMkSWqI5UySJKkhljNJkqSGWM4kSZIaMrRyluScJJuTXDVr258k+U6Sv0uyOsk+83z22iRXJrkiyZphZZQkSWrNMK+cfQA4brttFwOPr6pfBP4BeMMCnz+6qg6tqukh5ZMkadEs22Nvlj1gH5btsXffUbSLG9rcmlV1aZKV2227aNbq5UA7E2FJknQ/HPGYX+k7gsZEn785+zXg8/PsK+CiJGuTnL7QH0lyepI1SdZs2bJl0UNKkiSNUi/lLMnvA1uBj85zyBFVdRhwPPDKJEfO97eq6qyqmq6q6ampqSGklSRJGp0dlrMk+yV5X5LPd+uHJHnpzp4wyanAc4H/UlU11zFVtbF73wysBg7f2fNJkiTtSga5cvYB4ELgwG79H4DX7MzJkhwH/C7w/Kr60TzHLEuyfNsycCxw1VzHSpIkjZtBbghYUVWfTPIGgKramuRnO/pQknOBo4AVSdYDb2Lm7swHABcnAbi8ql6e5EDg7Ko6AdgPWN3tXwp8rKq+cN//0dSK69787/qOcA9bb3kwsJStt/ywqWyPeOOVfUeQJDVgkHJ2V5KHMPMjfZI8BbhtRx+qqlPm2Py+eY7dCJzQLV8DPGGAXJIkSWNnkHL228D5wGOSXAZM4SMwJEmShmKH5ayq1iV5OnAwEOC7VfXToSeTJEmaQPOWsyTzPU3vcUmoqk8PKZMkSdLEWujK2fO694cCTwW+1K0fDVwCWM4kSZIW2bzlrKpOA0jyOeCQqrqhWz8AePdo4kmSJE2WQZ5ztnJbMevcCDxuSHkkSZIm2iB3a16S5ELgXGYep3Ey8OWhppIkSZpQg9yt+aru5oCndZvOqqrVw40lSZI0mQa5crbtzkxvAJAkSRqyhR6l8bWq+uUkd9DNDrBtF1BVtffQ00mSJE2Yhe7W/OXuffno4kiSJE22Hd6tmeSZc2w7dThxJEmSJtsgj9J4Y5L3JFmWZL8kn+XnD6iVJEnSIhqknD0d+D5wBfA14GNV5cTnkiRJQzBIOdsXeDIzBe2fgUcmyVBTSZIkTahBytnlwOer6jjg3wMHApcNNZUkSdKEGuQ5Z8+squsAqurHwG8lOXK4sSRJkibTIDMEXJfk+cC2QvaVqvrscGNJkiRNpkEepfEW4NXA33ev30ryx8MOJkmSNIkGGdY8ATi0qu4GSPJB4BvAG4YZbFie9N8+1HeEe1h+0x0sAa676Y6msq39k5f0HUGSpIk0yA0BAPvMWn7QMIJIkiRpsCtnfwx8I8mXmZlX80h20atmkiRJrRvkhoBzk1zCzGM0AvxuVW0adjBJkqRJNO+wZpJ/070fBhwArAeuBw7stkmSJGmRLXTl7HXAy4C3zbGvgGcMJZEkSdIEm7ecVdXLuvejRxdHkiRpss1bzpL8ykIfrKpPL34cSZKkybbQsObzuveHAk8FvtStHw1cAljOJEmSFtlCw5qnAST5HHBIVd3QrR8AvHs08SRJkibLIA+hXbmtmHVuBB43pDySJEkTbZCH0F6S5ELgXGbu0jwZ+PJQU0mSJE2oQR5C+6okL2BmZgCAs6pq9XBjSZIkTaaB5tasqtVV9druNXAxS3JOks1Jrpq17cFJLk7yve5933k+e2p3zPeSnDroOSVJknZlg058vrM+ABy33bbXA39VVY8F/qpbv4ckDwbeBDwZOBx403wlTpIkaZwMtZxV1aXALdttPhH4YLf8QeA/zPHRZwMXV9UtVfWPwMXcu+RJkiSNnQXLWZIlST6yyOfcb9vdn937Q+c45iBm5vHcZn23TZIkaawtWM6q6mfAVJI9RpRnm8wVZ84Dk9OTrEmyZsuWLUOOJUmSNFyDPErjWuCyJOcDd23bWFVv38lz3pjkgKq6oXug7eY5jlkPHDVr/WHMzEpwL1V1FnAWwPT09JwFTpptxZ53A1u7d0mS2jJIOdvYvXYDli/COc8HTgXe0r3/nzmOuRD4n7NuAjgWeMMinFvid37x1r4jSJI0r0Gec/aHO/vHk5zLzBWwFUnWM3MH5luATyZ5KXAd8MLu2Gng5VX161V1S5I/Av62+1NvrqrtbyyQJEkaOzssZ0m+zBy/96qqZ+zos1V1yjy7jpnj2DXAr89aPwc4Z0fnkCRJGieDDGv+zqzlPYH/CGwdThxJkqTJNsiw5trtNl2W5CtDyiNJkjTRBhnWfPCs1d2AJwH7Dy2RJEnSBBtkWHMtM785CzPDmT8AXjrMUJIkSZNq3nKW5IVV9SngmKq6ZoSZJEmSJtZCMwRse67YeaMIIkmSpIWHNW/uHqPxqG52gHuoqucPL5YkSdJkWqicPQc4DPgw8LbRxJEkSZps85azqvoJcHmSp1aVM4pLkiSNwEI3BHyWbmaAJPfa77CmJEnS4ltoWPOtI0shSZIkYOFhTWcBkCRJGrFBZgj4AXNPfP7ooSSSJEmaYIPMEDA9a3lP4IXAg+c5VpIkSffDQg+hBaCqbp712lBVfwY8YwTZJEmSJs4gw5qHzVrdjZkracuHlkiSJGmCDTKsOfsBtFuBa4FfHUoaSZKkCbfDclZVR48iiCRJkgb4zVmSVyfZOzPOTrIuybGjCCdJkjSfPZfsxgOX7MaeS3ZYZ3Ypgwxr/lpV/XmSZwMPBU4D3g9cNNRkkiRJC3jiQ8bzJ/CDVM1tczedALy/qr45a5skSZIW0SDlbG2Si5gpZxcmWQ7cPdxYkiRJk2mQYc2XAocC11TVj5I8hJmhTUmSJC2yQe7WvBtYN2v9ZuDmYYaSJEmaVON1e4MkSdIuznImSZLUkIHKWZJfTnJatzyV5FHDjSVJkjSZBnkI7ZuA3wXe0G3aHfjIMENJkiRNqkGunL0AeD5wF0BVbcSJzyVJkoZikHL2k6oqoACSLBtuJEmSpMk1SDn7ZJK/APZJ8jLgi8B7hxtLkiRpMg3ynLO3JnkWcDtwMPDGqrp46MkkSZIm0CAzBAD8A1BV9cUkeyVZXlV3DDOYJEnSJBrkbs2XAecBf9FtOgj4zM6eMMnBSa6Y9bo9yWu2O+aoJLfNOuaNO3s+SZKkXckgV85eCRwOfB2gqr6X5KE7e8Kq+i4zc3WSZAmwAVg9x6Ffrarn7ux5JEmSdkWD3BDwz1X1k20rSZbS3bm5CI4Bvl9VP1ykvydJkrRLG6ScfSXJ7wEP7G4M+BTw2UU6/8nAufPs+6Uk30zy+SS/sEjnkyRJatog5ez1wBbgSuA3gAuA/35/T5xkD2YebvupOXavAx5ZVU8A3skCv3FLcnqSNUnWbNmy5f7GkiRJ6tWC5az7TdiHquq9VfXCqjqpW16MYc3jgXVVdeP2O6rq9qq6s1u+ANg9yYq5/khVnVVV01U1PTU1tQixJEmS+rNgOauqnwFT3VWuxXYK8wxpJtk/Sbrlw5nJefMQMkiSJDVlkLs1rwUuS3I+3fyaAFX19p09aZK9gGcxM0y6bdvLu797JnAS8IokW4EfAycv0tU6SZKkpg1SzjZ2r91YpAnPq+pHwEO223bmrOV3Ae9ajHNJkiTtSuYtZ0k+XFUvBm6tqj8fYSZJkqSJtdBvzp6U5JHAryXZN8mDZ79GFVCSJGmSLDSseSbwBeDRwFogs/ZVt12SJEmLaN4rZ1X1jqr6t8A5VfXoqnrUrJfFTJIkaQh2+BDaqnrFKIJIkiRpsBkCJEmSNCKWM0mSpIZYziRJkhpiOZMkSWqI5UySJKkhljNJkqSGWM4kSZIaYjmTJElqiOVMkiSpIQvNrakRuHuPZfd4lyRJk81y1rO7Hnts3xEkSVJDHNaUJElqiOVMkiSpIZYzSZKkhljOJEmSGmI5kyRJaojlTJIkqSGWM0mSpIZYziRJkhpiOZMkSWqI5UySJKkhljNJkqSGWM4kSZIaYjmTJElqiOVMkiSpIZYzSZKkhljOJEmSGmI5kyRJakhv5SzJtUmuTHJFkjVz7E+SdyS5OsnfJTmsj5ySJEmjtLTn8x9dVTfNs+944LHd68nAe7p3SZKksdXysOaJwIdqxuXAPkkO6DuUJEnSMPVZzgq4KMnaJKfPsf8g4PpZ6+u7bfeQ5PQka5Ks2bJly5CiSpIkjUaf5eyIqjqMmeHLVyY5crv9meMzda8NVWdV1XRVTU9NTQ0jpyRJ0sj0Vs6qamP3vhlYDRy+3SHrgYfPWn8YsHE06SRJkvrRSzlLsizJ8m3LwLHAVdsddj7wku6uzacAt1XVDSOOKkmSNFJ93a25H7A6ybYMH6uqLyR5OUBVnQlcAJwAXA38CDitp6ySJEkj00s5q6prgCfMsf3MWcsFvHKUuSRJkvrW8qM0JEmSJo7lTJIkqSGWM0mSpIZYziRJkhpiOZMkSWqI5UySJKkhljNJkqSGWM4kSZIaYjmTJElqiOVMkiSpIZYzSZKkhljOJEmSGmI5kyRJaojlTJIkqSGWM0mSpIZYziRJkhpiOZMkSWqI5UySJKkhljNJkqSGWM4kSZIaYjmTJElqiOVMkiSpIZYzSZKkhljOJEmSGmI5kyRJaojlTJIkqSGWM0mSpIZYziRJkhpiOZMkSWqI5UySJKkhljNJkqSGWM4kSZIaYjmTJElqyMjLWZKHJ/lykm8n+VaSV89xzFFJbktyRfd646hzSpIk9WFpD+fcCryuqtYlWQ6sTXJxVf39dsd9taqe20M+SZKk3oz8yllV3VBV67rlO4BvAweNOockSVKLev3NWZKVwBOBr8+x+5eSfDPJ55P8wgJ/4/Qka5Ks2bJly5CSSpIkjUZv5SzJvwL+EnhNVd2+3e51wCOr6gnAO4HPzPd3quqsqpququmpqanhBZYkSRqBXspZkt2ZKWYfrapPb7+/qm6vqju75QuA3ZOsGHFMSZKkkevjbs0A7wO+XVVvn+eY/bvjSHI4MzlvHl1KSZKkfvRxt+YRwIuBK5Nc0W37PeARAFV1JnAS8IokW4EfAydXVfWQVZIkaaRGXs6q6mtAdnDMu4B3jSaRJElSO5whQJIkqSGWM0mSpIZYziRJkhpiOZMkSWqI5UySJKkhljNJkqSGWM4kSZIaYjmTJElqiOVMkiSpIZYzSZKkhljOJEmSGmI5kyRJaojlTJIkqSGWM0mSpIZYziRJkhpiOZMkSWqI5UySJKkhljNJkqSGWM4kSZIaYjmTJElqiOVMkiSpIZYzSZKkhljOJEmSGmI5kyRJaojlTJIkqSGWM0mSpIZYziRJkhpiOZMkSWqI5UySJKkhljNJkqSGWM4kSZIaYjmTJElqSC/lLMlxSb6b5Ookr59j/wOSfKLb//UkK0efUpIkafRGXs6SLAHeDRwPHAKckuSQ7Q57KfCPVfWvgT8F/tdoU0qSJPWjjytnhwNXV9U1VfUT4OPAidsdcyLwwW75POCYJBlhRkmSpF70Uc4OAq6ftb6+2zbnMVW1FbgNeMhI0kmSJPUoVTXaEyYvBJ5dVb/erb8YOLyqfnPWMd/qjlnfrX+/O+bmOf7e6cDp3erBwHeH/I8wDCuAm/oOMWH8zkfP73z0/M5Hz+989Hbl7/yRVTW1/calPQRZDzx81vrDgI3zHLM+yVLgQcAtc/2xqjoLOGsIOUcmyZqqmu47xyTxOx89v/PR8zsfPb/z0RvH77yPYc2/BR6b5FFJ9gBOBs7f7pjzgVO75ZOAL9WoL/FJkiT1YORXzqpqa5JXARcCS4BzqupbSd4MrKmq84H3AR9OcjUzV8xOHnVOSZKkPvQxrElVXQBcsN22N85a/ifghaPO1aNdelh2F+V3Pnp+56Pndz56fuejN3bf+chvCJAkSdL8nL5JkiSpIZazHiXZM8nfJPlmkm8l+cO+M02CJEuSfCPJ5/rOMimSXJvkyiRXJFnTd55JkGSfJOcl+U6Sbyf5pb4zjbMkB3f/fm973Z7kNX3nGndJXtv97+dVSc5NsmffmRaDw5o96mY9WFZVdybZHfga8OqqurznaGMtyW8D08DeVfXcvvNMgiTXAtNVtas+i2iXk+SDwFer6uzuzvi9qurWvnNNgm6awg3Ak6vqh33nGVdJDmLmfzcPqaofJ/kkcEFVfaDfZPefV856VDPu7FZ371625SFK8jDgOcDZfWeRhiXJ3sCRzNz5TlX9xGI2UscA37eYjcRS4IHdM1H34t7PTd0lWc561g2xXQFsBi6uqq/3nWnM/RmwCri77yATpoCLkqztZvXQcD0a2AK8vxvCPzvJsr5DTZCTgXP7DjHuqmoD8FbgOuAG4LaquqjfVIvDctazqvpZVR3KzEwJhyd5fN+ZxlWS5wKbq2pt31km0BFVdRhwPPDKJEf2HWjMLQUOA95TVU8E7gJe32+kydANIT8f+FTfWcZdkn2BE4FHAQcCy5K8qN9Ui8Ny1ohuyOES4Lieo4yzI4Dnd79/+jjwjCQf6TfSZKiqjd37ZmA1cHi/icbeemD9rCvx5zFT1jR8xwPrqurGvoNMgGcCP6iqLVX1U+DTwFN7zrQoLGc9SjKVZJ9u+YHM/Iv2nX5Tja+qekNVPayqVjIz7PClqhqL/5fVsiTLkizftgwcC1zVb6rxVlWbgOuTHNxtOgb4+x4jTZJTcEhzVK4DnpJkr+4Gu2OAb/ecaVH0MkOA/sUBwAe7O3t2Az5ZVT7eQeNmP2D1zH87WQp8rKq+0G+kifCbwEe7YbZrgNN6zjP2kuwFPAv4jb6zTIKq+nqS84B1wFbgG4zJbAE+SkOSJKkhDmtKkiQ1xHImSZLUEMuZJElSQyxnkiRJDbGcSZIkNcRyJkkDSLIyyVXd8nSSd3TLRyUZiwdfSmqDzzmTpPuoqtYAa7rVo4A7gb/uLZCkseKVM0ljL8nvJ/luki8mOTfJ7yS5JMl0t39FN63XtitkX02yrnvd66pYd7Xsc0lWAi8HXpvkiiRPS/KDJLt3x+2d5Npt65I0CK+cSRprSZ7EzHRdT2Tmv3nrgLULfGQz8Kyq+qckj2VmKp7puQ6sqmuTnAncWVVv7c53CfAc4DPdef+ym/dPkgbilTNJ4+5pwOqq+lFV3Q6cv4Pjdwfem+RK4FPAIffxfGfz86mSTgPefx8/L2nCeeVM0iSYa566rfz8/6DuOWv7a4EbgSd0+//pPp2o6rJuaPTpwJKqcpJ3SfeJV84kjbtLgRckeWCS5cDzuu3XAk/qlk+adfyDgBuq6m7gxcCSHfz9O4Dl2237EDPDoV41k3SfWc4kjbWqWgd8ArgC+Evgq92utwKvSPLXwIpZH/nfwKlJLgceB9y1g1N8lpnyd0WSp3XbPgrsy0xBk6T7JFVzXe2XpPGU5A+Y9QP+IZ3jJODEqnrxsM4haXz5mzNJWkRJ3gkcD5zQdxZJuyavnEmSJDXE35xJkiQ1xHImSZLUEMuZJElSQyxnkiRJDbGcSZIkNcRyJkmS1JD/Dwd+B6nPeS/mAAAAAElFTkSuQmCC\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 = wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x254b70c3248>"
      ]
     },
     "execution_count": 18,
     "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 = wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x254b68ec648>"
      ]
     },
     "execution_count": 19,
     "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 = 'sulphates', data = wine)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Sulphates level goes higher with the quality of wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x254b7164208>"
      ]
     },
     "execution_count": 20,
     "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 = wine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Alcohol level also goes higher as the quality of wine increases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x254b673b808>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "x=wine[\"volatile acidity\"]\n",
    "y=wine[\"quality\"]\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x254b66d2408>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "x=wine[\"citric acid\"]\n",
    "y=wine[\"quality\"]\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x254b662bcc8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "x=wine[\"chlorides\"]\n",
    "y=wine[\"quality\"]\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x254b667fe88>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "x=wine[\"free sulfur dioxide\"]\n",
    "y=wine[\"quality\"]\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x254b6a3bf08>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "x=wine[\"total sulfur dioxide\"]\n",
    "y=wine[\"quality\"]\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x254b6aa5688>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "x=wine[\"sulphates\"]\n",
    "y=wine[\"quality\"]\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x254b6b0bb48>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "x=wine[\"alcohol\"]\n",
    "y=wine[\"quality\"]\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x254b6a69788>"
      ]
     },
     "execution_count": 28,
     "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.boxplot(x = 'quality', y = 'fixed acidity', data = wine)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x254b6a7fa88>"
      ]
     },
     "execution_count": 29,
     "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.boxplot(x = 'quality', y = 'volatile acidity', data = wine)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x254b6e40cc8>"
      ]
     },
     "execution_count": 30,
     "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": [
    "\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "sns.boxplot(x = 'quality', y = 'citric acid', data = wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x254b6cca288>"
      ]
     },
     "execution_count": 31,
     "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.boxplot(x = 'quality', y = 'residual sugar', data = wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x254b6e91d48>"
      ]
     },
     "execution_count": 32,
     "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": [
    "\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "sns.boxplot(x = 'quality', y = 'chlorides', data = wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x254b6d963c8>"
      ]
     },
     "execution_count": 33,
     "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.boxplot(x = 'quality', y = 'free sulfur dioxide', data = wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x254b832cbc8>"
      ]
     },
     "execution_count": 34,
     "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.boxplot(x = 'quality', y = 'total sulfur dioxide', data = wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x254b8490508>"
      ]
     },
     "execution_count": 35,
     "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.boxplot(x = 'quality', y = 'sulphates', data = wine)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x254b8390d88>"
      ]
     },
     "execution_count": 36,
     "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.boxplot(x = 'quality', y = 'alcohol', data = wine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## conclusion graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_pivot =wine.pivot_table(index='quality')\n",
    "class_pivot.plot.bar(figsize=(20,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation = wine.corr()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.subplots(figsize=(10,10))\n",
    "sns.heatmap(correlation,vmax=1,square=True,annot=True,cmap='Blues')\n",
    "plt.savefig('Correlation_heatmap')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations\n",
    " From above bar plots,we observed that the following Attributes are more appropriate in 'PREDICTING THE QUALITY OF WINE':\n",
    "* volatile acidity\n",
    "* citric acid\n",
    "* chlorides\n",
    "* free sulphur dioxide\n",
    "* total sulphur dioxide\n",
    "* sulphates\n",
    "* alcohol level\n",
    "* density"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=['volatile acidity','citric acid','chlorides','free sulfur dioxide','total sulfur dioxide','sulphates','alcohol','density']\n",
    "X=wine[cols]\n",
    "y=wine['quality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>density</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.527821</td>\n",
       "      <td>0.270976</td>\n",
       "      <td>0.087467</td>\n",
       "      <td>15.874922</td>\n",
       "      <td>46.467792</td>\n",
       "      <td>0.658149</td>\n",
       "      <td>10.422983</td>\n",
       "      <td>0.996747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.179060</td>\n",
       "      <td>0.194801</td>\n",
       "      <td>0.047065</td>\n",
       "      <td>10.460157</td>\n",
       "      <td>32.895324</td>\n",
       "      <td>0.169507</td>\n",
       "      <td>1.065668</td>\n",
       "      <td>0.001887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>0.990070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>0.995600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>0.996750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>0.997835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>1.003690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       volatile acidity  citric acid    chlorides  free sulfur dioxide  \\\n",
       "count       1599.000000  1599.000000  1599.000000          1599.000000   \n",
       "mean           0.527821     0.270976     0.087467            15.874922   \n",
       "std            0.179060     0.194801     0.047065            10.460157   \n",
       "min            0.120000     0.000000     0.012000             1.000000   \n",
       "25%            0.390000     0.090000     0.070000             7.000000   \n",
       "50%            0.520000     0.260000     0.079000            14.000000   \n",
       "75%            0.640000     0.420000     0.090000            21.000000   \n",
       "max            1.580000     1.000000     0.611000            72.000000   \n",
       "\n",
       "       total sulfur dioxide    sulphates      alcohol      density  \n",
       "count           1599.000000  1599.000000  1599.000000  1599.000000  \n",
       "mean              46.467792     0.658149    10.422983     0.996747  \n",
       "std               32.895324     0.169507     1.065668     0.001887  \n",
       "min                6.000000     0.330000     8.400000     0.990070  \n",
       "25%               22.000000     0.550000     9.500000     0.995600  \n",
       "50%               38.000000     0.620000    10.200000     0.996750  \n",
       "75%               62.000000     0.730000    11.100000     0.997835  \n",
       "max              289.000000     2.000000    14.900000     1.003690  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.9978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0.9968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0.9970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0.9980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.9978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   volatile acidity  citric acid  chlorides  free sulfur dioxide  \\\n",
       "0              0.70         0.00      0.076                 11.0   \n",
       "1              0.88         0.00      0.098                 25.0   \n",
       "2              0.76         0.04      0.092                 15.0   \n",
       "3              0.28         0.56      0.075                 17.0   \n",
       "4              0.70         0.00      0.076                 11.0   \n",
       "\n",
       "   total sulfur dioxide  sulphates  alcohol  density  \n",
       "0                  34.0       0.56      9.4   0.9978  \n",
       "1                  67.0       0.68      9.8   0.9968  \n",
       "2                  54.0       0.65      9.8   0.9970  \n",
       "3                  60.0       0.58      9.8   0.9980  \n",
       "4                  34.0       0.56      9.4   0.9978  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_X=wine[cols]\n",
    "all_y=wine['quality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.96187667, -1.39147228, -0.24370669, ..., -0.57920652,\n",
       "        -0.96024611,  0.55827446],\n",
       "       [ 1.96744245, -1.39147228,  0.2238752 , ...,  0.1289504 ,\n",
       "        -0.58477711,  0.02826077],\n",
       "       [ 1.29706527, -1.18607043,  0.09635286, ..., -0.04808883,\n",
       "        -0.58477711,  0.13426351],\n",
       "       ...,\n",
       "       [-0.09955388, -0.72391627, -0.24370669, ...,  0.54204194,\n",
       "         0.54162988, -0.53355375],\n",
       "       [ 0.65462046, -0.77526673, -0.26496041, ...,  0.30598963,\n",
       "        -0.20930812, -0.67665745],\n",
       "       [-1.21684919,  1.02199944, -0.43499018, ...,  0.01092425,\n",
       "         0.54162988, -0.66605717]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_all_X = StandardScaler()\n",
    "all_X = sc_all_X.fit_transform(X)\n",
    "all_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_X,test_X,train_y,test_y=train_test_split(all_X,all_y,test_size=0.25,random_state=0,stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1199, 8)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 8)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiclass Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K Neighbors classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knnmodel = KNeighborsClassifier(n_neighbors=20)\n",
    "\n",
    "knnmodel.fit(train_X,train_y)\n",
    "predictions=knnmodel.predict(test_X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5675\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy_knnm=accuracy_score(test_y,predictions)\n",
    "print(accuracy_knnm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.475      0.475      0.5125     0.525      0.54375    0.54716981\n",
      " 0.575      0.58125    0.59375    0.65625   ]\n",
      "0.5484669811320755\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores=cross_val_score(knnmodel,all_X,all_y,cv=10)\n",
    "scores.sort()\n",
    "accuracy_knn=scores.mean()\n",
    "    \n",
    "print(scores)\n",
    "print(accuracy_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0,   2,   0,   0,   0],\n",
       "       [  0,   0,   5,   8,   0,   0],\n",
       "       [  0,   0, 120,  47,   3,   0],\n",
       "       [  0,   0,  55,  88,  17,   0],\n",
       "       [  0,   0,   6,  25,  19,   0],\n",
       "       [  0,   0,   0,   3,   2,   0]], dtype=int64)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(test_y, predictions) \n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'knn_model')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heat map\n",
    "\n",
    "sns.heatmap(cm, annot=True, fmt='.2f',xticklabels = [\"3\",\"4\",\"5\",\"6\",\"7\",\"8\"] , yticklabels = [\"3\",\"4\",\"5\",\"6\",\"7\",\"8\"] )\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.title('knn_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier \n",
    "\n",
    "dtree_model = DecisionTreeClassifier(max_depth = 7,max_features='auto',random_state=0).fit(train_X, train_y) \n",
    "dtree_predictions = dtree_model.predict(test_X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5875\n"
     ]
    }
   ],
   "source": [
    "accuracy_dtm=accuracy_score(test_y,dtree_predictions)\n",
    "print(accuracy_dtm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.44375    0.45       0.4625     0.48125    0.50314465 0.53125\n",
      " 0.53125    0.5625     0.56875    0.675     ]\n",
      "0.520939465408805\n"
     ]
    }
   ],
   "source": [
    "scores=cross_val_score(dtree_model,all_X,all_y,cv=10)\n",
    "scores.sort()\n",
    "accuracy_dt=scores.mean()\n",
    "    \n",
    "print(scores)\n",
    "print(accuracy_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0,   2,   0,   0,   0],\n",
       "       [  1,   0,   3,   8,   1,   0],\n",
       "       [  0,   3, 110,  52,   4,   1],\n",
       "       [  1,   0,  44, 101,  14,   0],\n",
       "       [  0,   0,   4,  22,  24,   0],\n",
       "       [  0,   0,   1,   0,   4,   0]], dtype=int64)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a confusion matrix \n",
    "cm = confusion_matrix(test_y, dtree_predictions) \n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'dt_model')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot=True, fmt='.2f',xticklabels = [\"3\",\"4\",\"5\",\"6\",\"7\",\"8\"] , yticklabels = [\"3\",\"4\",\"5\",\"6\",\"7\",\"8\"] )\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.title('dt_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support vector machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6425\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svcmodel = SVC(kernel='rbf')\n",
    "svcmodel.fit(train_X,train_y)\n",
    "prediction = svcmodel.predict(test_X)\n",
    "accuracy_svcm= accuracy_score(test_y,prediction) # can use for test or train\n",
    "print(accuracy_svcm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.50625    0.55       0.55625    0.5625     0.56875    0.59748428\n",
      " 0.6125     0.6125     0.61875    0.66875   ]\n",
      "0.5853734276729561\n"
     ]
    }
   ],
   "source": [
    "scores=cross_val_score(svcmodel,all_X,all_y,cv=10)\n",
    "scores.sort()\n",
    "accuracy_svc=scores.mean()\n",
    "    \n",
    "print(scores)\n",
    "print(accuracy_svc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0,   2,   0,   0,   0],\n",
       "       [  0,   0,   9,   4,   0,   0],\n",
       "       [  0,   0, 135,  35,   0,   0],\n",
       "       [  0,   0,  48, 106,   6,   0],\n",
       "       [  0,   0,   2,  32,  16,   0],\n",
       "       [  0,   0,   0,   3,   2,   0]], dtype=int64)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a confusion matrix\n",
    "cm = confusion_matrix(test_y, prediction) \n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'svc_model')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot=True, fmt='.2f',xticklabels = [\"3\",\"4\",\"5\",\"6\",\"7\",\"8\"] , yticklabels = [\"3\",\"4\",\"5\",\"6\",\"7\",\"8\"] )\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.title('svc_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6575\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "randommodel = RandomForestClassifier(n_estimators=150,max_depth=7,random_state=0)\n",
    "randommodel.fit(train_X,train_y)\n",
    "\n",
    "prediction_randommodel = randommodel.predict(test_X)\n",
    "accuracy_randommodel= accuracy_score(test_y,prediction_randommodel) # can use for test or train\n",
    "print(accuracy_randommodel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.525     0.53125   0.55      0.5625    0.5875    0.59375   0.60625\n",
      " 0.60625   0.6163522 0.64375  ]\n",
      "0.5822602201257862\n"
     ]
    }
   ],
   "source": [
    "scores=cross_val_score(randommodel,all_X,all_y,cv=10)\n",
    "scores.sort()\n",
    "accuracy_rf=scores.mean()\n",
    "    \n",
    "print(scores)\n",
    "print(accuracy_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0,   2,   0,   0,   0],\n",
       "       [  0,   0,   6,   7,   0,   0],\n",
       "       [  0,   0, 135,  35,   0,   0],\n",
       "       [  0,   0,  48, 106,   6,   0],\n",
       "       [  0,   0,   2,  26,  22,   0],\n",
       "       [  0,   0,   0,   2,   3,   0]], dtype=int64)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(test_y, prediction_randommodel) \n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'rf_model')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot=True, fmt='.2f',xticklabels = [\"3\",\"4\",\"5\",\"6\",\"7\",\"8\"] , yticklabels = [\"3\",\"4\",\"5\",\"6\",\"7\",\"8\"] )\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.title('rf_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gaussian Naive bayes classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5725\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gaussianmodel=  GaussianNB()\n",
    "gaussianmodel.fit(train_X,train_y)\n",
    "prediction_gaussianmodel = gaussianmodel.predict(test_X)\n",
    "accuracy_gaussianmodel= accuracy_score(test_y,prediction_gaussianmodel) # can use for test or train\n",
    "print(accuracy_gaussianmodel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.4875     0.5125     0.525      0.53125    0.54375    0.54375\n",
      " 0.58125    0.5875     0.59748428 0.625     ]\n",
      "0.553498427672956\n"
     ]
    }
   ],
   "source": [
    "scores=cross_val_score(gaussianmodel,all_X,all_y,cv=10)\n",
    "scores.sort()\n",
    "accuracy_gnb=scores.mean()\n",
    "    \n",
    "print(scores)\n",
    "print(accuracy_gnb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0,   2,   0,   0,   0],\n",
       "       [  0,   0,   6,   7,   0,   0],\n",
       "       [  0,   0, 135,  35,   0,   0],\n",
       "       [  0,   0,  48, 106,   6,   0],\n",
       "       [  0,   0,   2,  26,  22,   0],\n",
       "       [  0,   0,   0,   2,   3,   0]], dtype=int64)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(test_y, prediction_randommodel) \n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'gnb_model')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot=True, fmt='.2f',xticklabels = [\"3\",\"4\",\"5\",\"6\",\"7\",\"8\"] , yticklabels = [\"3\",\"4\",\"5\",\"6\",\"7\",\"8\"] )\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.title('gnb_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Binary classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Making binary classificaion for the response variable.\n",
    "#Dividing wine as good and bad by giving the limit for the quality\n",
    "bins = (2, 6.5, 8)\n",
    "group_names = ['bad', 'good']\n",
    "wine['quality'] = pd.cut(wine['quality'], bins = bins, labels = group_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now lets assign a labels to our quality variable\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_quality = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bad becomes 0 and good becomes 1 \n",
    "wine['quality'] = label_quality.fit_transform(wine['quality'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=wine[cols]\n",
    "y=wine['quality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.9978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0.9968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0.9970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0.9980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.9978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   volatile acidity  citric acid  chlorides  free sulfur dioxide  \\\n",
       "0              0.70         0.00      0.076                 11.0   \n",
       "1              0.88         0.00      0.098                 25.0   \n",
       "2              0.76         0.04      0.092                 15.0   \n",
       "3              0.28         0.56      0.075                 17.0   \n",
       "4              0.70         0.00      0.076                 11.0   \n",
       "\n",
       "   total sulfur dioxide  sulphates  alcohol  density  \n",
       "0                  34.0       0.56      9.4   0.9978  \n",
       "1                  67.0       0.68      9.8   0.9968  \n",
       "2                  54.0       0.65      9.8   0.9970  \n",
       "3                  60.0       0.58      9.8   0.9980  \n",
       "4                  34.0       0.56      9.4   0.9978  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: quality, dtype: int32"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.96187667, -1.39147228, -0.24370669, ..., -0.57920652,\n",
       "        -0.96024611,  0.55827446],\n",
       "       [ 1.96744245, -1.39147228,  0.2238752 , ...,  0.1289504 ,\n",
       "        -0.58477711,  0.02826077],\n",
       "       [ 1.29706527, -1.18607043,  0.09635286, ..., -0.04808883,\n",
       "        -0.58477711,  0.13426351],\n",
       "       ...,\n",
       "       [-0.09955388, -0.72391627, -0.24370669, ...,  0.54204194,\n",
       "         0.54162988, -0.53355375],\n",
       "       [ 0.65462046, -0.77526673, -0.26496041, ...,  0.30598963,\n",
       "        -0.20930812, -0.67665745],\n",
       "       [-1.21684919,  1.02199944, -0.43499018, ...,  0.01092425,\n",
       "         0.54162988, -0.66605717]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Feature Scaleing\n",
    "sc_X = StandardScaler()\n",
    "X = sc_X.fit_transform(X)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0,stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=50, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=500,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=0, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression(C=50,max_iter=500,random_state=0)\n",
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression accuracy: 0.888\n",
      "Logistic regression f1 score: 0.477\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,f1_score\n",
    "\n",
    "accuracy_log = accuracy_score(y_train, logreg.predict(X_train))\n",
    "\n",
    "f1_score_log = f1_score(y_train, logreg.predict(X_train))\n",
    "\n",
    "print('Logistic regression accuracy: {:.3f}'.format(accuracy_log))\n",
    "print('Logistic regression f1 score: {:.3f}'.format(f1_score_log))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression accuracy: 0.845\n",
      "Logistic regression f1 score: 0.367\n"
     ]
    }
   ],
   "source": [
    "accuracy_log = accuracy_score(y_test, logreg.predict(X_test))\n",
    "acc = {'LogisticRegression' : accuracy_log}\n",
    "\n",
    "f1_score_log = f1_score(y_test, logreg.predict(X_test))\n",
    "f1 = {'LogisticRegression' : f1_score_log}\n",
    "\n",
    "print('Logistic regression accuracy: {:.3f}'.format(accuracy_log))\n",
    "print('Logistic regression f1 score: {:.3f}'.format(f1_score_log))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.83125    0.85       0.85       0.8625     0.86875    0.86875\n",
      " 0.86875    0.8875     0.89937107 0.90625   ]\n",
      "0.8693121069182389\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores=cross_val_score(logreg,X,y,cv=10)\n",
    "scores.sort()\n",
    "accuracy_log_cv=scores.mean()\n",
    "acc_cv = {'LogisticRegression' : accuracy_log_cv}\n",
    "    \n",
    "print(scores)\n",
    "print(accuracy_log_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_log= logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.92      0.91       346\n",
      "           1       0.41      0.33      0.37        54\n",
      "\n",
      "    accuracy                           0.84       400\n",
      "   macro avg       0.65      0.63      0.64       400\n",
      "weighted avg       0.83      0.84      0.84       400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,prediction_log))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[320,  26],\n",
       "       [ 36,  18]], dtype=int64)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm_log = confusion_matrix(y_test, prediction_log) \n",
    "cm_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'log_model')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MDJzjzuJRJWaWDh5VUsKjSswsHdzjLnER8GiS6wZYjkeVmFltVAsCcq7yHbiPBYYAjZL11cDBkuolI07MzGqFKHSqpFhnMr3uxmRmCOwDdAUeknRlnus2M8udUyUltgcOjIjVAJJuAIaSGR44Dbgrz/WbmeUkTcMB8x24dwKyJ//9Gtg5Ir6QtC7PdZuZ5c6Bu8STwBRJzyXrPwSekrQtmeermZnVDulJcef9lvdbJI0EupCZq+SiiJiabO6Zz7rNzCoj1qcncue7x01ETCOTzzYzq73SE7fzH7jNzNLAFyfNzNLGPW4zs3Rxj9vMLG3c4zYzS5dYX9MtyJ0Dt5kZECnqced7rhIzs3QoqsRSDkk7ShovaZak9yVdlpQ3lzRW0kfJa7OkXJLukzRb0ruSDqyoqZUK3JKaSNqnMseYmaVBFOW+VGA9cHlE7A0cClySxM2rgXER0QEYl6wDnAx0SJY+wMCKKqgwcEsaJ6lx8tPhPeBJSX+osOlmZilSVYE7IhZExPTk/SpgFtAO6E5mmmuS1zOS992BRyNjCtBUUpvy6silx908eTr7j4AhEdEJODGH48zMUiMKlfMiqY+kqVlLn9LOKWkX4ADgDaB1RCyATHAHWiW7tQM+yzpsblJWplwuTjaQ1BI4E/h9DvubmaVOZS5ORsQgYFB5+0hqBPwN+FVErJRU5q6lVVHeuXPpcd8KTAD+ExFvStoN+CSH48zMUiOKlPNSEUkNyQTtJyLi70nxouIUSPK6OCmfC+yYdXh7YH55568wcEfE0xGxT0T0SdY/jojuFbbczCxFqirHrUzXejAwKyL6Z20azjfP3O0NPJdVfm4yuuRQYEVxSqUsuVycvD25ONlA0mhJiyT9V0XHmZmlSYRyXipwBNALOEbSjGQ5BbgDOF7SR8DxyTrASOBjYDbwEHBxRRXkkuM+OSKukXQGma79vmSGsjyZw7FmZqlQVTfgRMQkSs9bQ+YB6hvuH8Allakjp4uTyespwFMRsVRSemZjMTPLQVFhxbnr2iKXwP2ipJlAIZmB5C0APy/SzOqUXC461hYVBu6IuCK54WZZRKyX9CWZMd1mZnVGnQrcieZAF0lbZZU5x21mdUakKAFcYeCWdD1wArAXMJrMXZOTcOA2szokTT3uXG7A+SnQDVgQEb2Ajng6WDOrY6pwOGDe5RKAv4iIQknrJW0HLAR2y3O7zMyqVWEdG1XytqSmwMPAVGAlMD2vrTIzq2a1oSedq1xGlVyYvH1A0migcfGUhWZmdUWactxlBm5J+5exab2k/SPi3Ty1ycys2tWVUSUPlLMtgKOquC1mZjWmTvS4I+LI6myImVlNKixKzyN4c5kd8KLk4mTxerOynvZgZpZWEbkvNS2XHzEXRURB8UpELAf65q9JZmbVryiU81LTchkOWD97RVI9oGF+mmNmVjPq1HBAYKykp4AHyVyU7Au8lNdWmZlVs9qQAslVLoH7CjLB+tdkJgcfA/w5n40C2HGPU/NdhaXQuvVf13QTrI6qDSmQXOVyA04hcH+ymJnVSWkaVeLJoszMyOSB08KB28yMOpYqKSZpy4jwI8vMrE5K06iSXG7AOUTSe8BHyXpHSX/Ke8vMzKpRUSWWmpZLNv4+4DTgc4CIeIfMgxXMzOqMQDkvNS2XVEm9iPhU+lZjC/PUHjOzGrE+RamSXAL3Z5IOAUJSfeB/gH/lt1lmZtWrNvSkc5VL4O5LJl2yE7CIzF2TnqvEzOqU2pC7zlUuN+AsBs6uhraYmdWYOtXjlvQQpYxNjwhP7WpmdUZV9rglPUxmUMfiiNgvKbsR+G9gSbLbtRExMtl2DXA+meuHv4yI0eWdP5dUSfaEUlsBPYDPKvEZzMxqvcKq7XH/lcw0IY9uUH5vRNydXSBpHzJZjX2BtsBLkr6XTDdSqlxSJc9sUMljwNicmm5mlhJV+eSyiHhV0i457t4deDq5wfETSbOBQ4DJZR2wKbOq7ArsvAnHmZnVWkUo50VSH0lTs5ZcU8eXSnpX0sOSmiVl7fh2FmNuUlamXHLcy/kmx10PWAZcnWMjzcxSoTKTTEXEIGBQJasYCNySVHULcA9wHpSaoym3OeUGbmXuuukIzEuKiiLSNN24mVlu8j0cMCIWFb9PBn08n6zOBXbM2rU9ML+8c5WbKkmC9LCIKEwWB20zq5OKpJyXTSGpTdZqD2Bm8n44cLakLSXtCnQA3izvXLmMKnlT0oERMX2TWmtmlgJVOY9H8rjHrkALSXOBG4CukjqRSYPMAS4EiIj3JT0L/BNYD1xS3ogSKCdwS2oQEeuBLsB/S/o3sIZMPiYi4sDN/GxmZrVGFY8qOaeU4sHl7H8rcGuu5y+vx/0mcCBwRq4nMzNLq6I6cuekACLi39XUFjOzGpOmC3jlBe6Wkn5T1saI6J+H9piZ1YiqTJXkW3mBuz7QiNLHGJqZ1Sl1ZXbABRFxc7W1xMysBhWmqItaYY7bzOy7oK70uI+ttlaYmdWwOhG4I2JZdTbEzKwmpeiRkzndOWlmVufViR63mdl3SVXe8p5vDtxmZtSdcdxmZt8ZTpWYmaWMA7eZWcrUlblKzMy+M5zjNjNLGY8qMTNLmaIUJUscuM3M8MVJM7PUSU9/24HbzAxwj9vMLHXWKz19bgduMzOcKjEzSx2nSszMUsbDAc3MUiY9YduB28wMcKrEzCx1ClPU565X0w0wM6sNiiqxVETSw5IWS5qZVdZc0lhJHyWvzZJySbpP0mxJ70o6sKLzO3CbmQFRiT85+Ctw0gZlVwPjIqIDMC5ZBzgZ6JAsfYCBFZ3cgdvMjKrtcUfEq8CyDYq7A0OS90OAM7LKH42MKUBTSW3KO78DdzXbcssteHHcM4ybNIwJk0dwxTWXlmy7+vrLeG3qi7z6xvOcf+HPSj3+rHO68/q0Ubw+bRRnndO9pHz/jvsw/rXnmDx9FP3uvDbvn8Oq1kOD7mH+3HeY8fa4krKOHffltYkjmPrWGKZMHsnBnTuVemyvXmcy6/1JzHp/Er16nVlSfuAB3+ft6S/xwT8ncW//m/P+GdKuiMh5kdRH0tSspU8OVbSOiAUAyWurpLwd8FnWfnOTsjL54mQ1W7fuK358+i9Yu2YtDRo0YPioxxk3diLf23M32rVvQ5eDTyEiaNGi+UbHNm3ahMuvuoQTu55JRDBmwlBGjxzPihUrubP/Dfz2Vzcw7a0ZPPl/f+aY447k5Zcm1sAntE3x6KPPMmDAIzzyyB9Lyu647Tpu6defUaPHc/JJx3DH7ddx7PFnfuu4Zs2a8rvrfs0PDst8b96c8iIjRoyhoGAFD9x/O337XsWUN6bx/PDHOOnEbowaPb66P1pqVObSZEQMAgZVUdWlPcKh3Oa4x10D1q5ZC0DDhg1o0LAhEUHv887mnjsHEJH591q6dMPfsqDrsUcwYfzrFBSsYMWKlUwY/zrdjutCq9YtabRdI6a9NQOAZ59+jpNOPbb6PpBttomT3mDZ8oJvlUUE2zXeDoDGTbZj/oJFGx13wglH89K4iSxfXkBBwQpeGjeRE0/syg47tGK7xtsx5Y1pADz2xFBOP33DlKtlW0/kvGyiRcUpkOR1cVI+F9gxa7/2wPzyTuQedw2oV68eYyYMZdddd+KRvzzF29PeZeddd6L7j07mlNOO4/Oly7juqtv45ONPv3VcmzatmT9vYcn6gvmLaNOmNW3atGLB/EUblVu6/ea3NzDy+Se5647fUa+eOPLo7hvt067tDsyd+83/8XnzFtCu7Q60a7sD8+Yu+KZ8bqbcypbjRcfNMRzoDdyRvD6XVX6ppKeBHwArilMqZclLj1vSe8mwllKXco4ryRut/aqgrN1Sr6ioiOOO/BEH7NuNAw76Pnvt3YEtt2jIunXrOLHbmTz+6FDuvb/fRsdJG/9GFZl8W6nllm4X9jmXy6+4kV13P5jLr7iJh/58z0b7lPpvH2V/V6xsVTwc8ClgMrCnpLmSzicTsI+X9BFwfLIOMBL4GJgNPARcXNH585UqOQ34ITAqWXomy0hgaFkHRcSgiOgcEZ232aJpnppWe6xcsYrXJ71Jt2O7MH/+Il4YPgaAkSPGss++e260//z5C2nb7pteU5u2rVm4YDHz5y+iTdvWG5Vbup3b60yGDRsJwNChIzj44I0vTs6dt4D27duWrLdr14b5CxYyd94C2rX/ZmBCu/ZtmD9/41SLfaMqhwNGxDkR0SYiGkZE+4gYHBGfR8SxEdEheV2W7BsRcUlE7B4R34+IqRWdPy+BOyI+jYhPgSMi4sqIeC9ZrgZOzEedabH99s1o3CSTt9xqqy058ujDmP3RJ4x6YRxdjjoUgMO7HMzH/56z0bGvjHuNrsccQZMmjWnSpDFdjzmCV8a9xuJFS1izeg0Hdu4IwFlnd2f0yJer7TNZfsxfsIijjzoMgGO6deGj2Z9stM+YMRM4/rijaNq0CU2bNuH4445izJgJLFy4mFWrVvODQzL3cvTq+RNGjBhdre1Pm6rscedbvnPc20rqEhGTACQdDmyb5zprtVY7tOS+gbdTv3596qkew/8xirGjX+GNKdMYMOgP9OnbmzVr1vKbX/4OgI6d9uXc887m8l/+joKCFdz7h4GMGv8sAP3vGkBBwQoArvrNTfxxwO1stfWWvDx2IuPGvlpjn9Eq7/HHHuDoow6jRYvmzPl4KjfdfDcXXXQF/fvfTIMGDVj35Zf07XslAAcduD99+vTiwouuYPnyAm697X+Z8voLAPS79V6WJxc5L730GgYPvpett9qKUaPH8+Io/zAvT2GkJ5WkyGNjJR0EPAw0SYoKgPMiYnpFx+7QdO/0/C1atVm6dmVNN8FqofVfzSttSF2l/NfOPXKOOU9+Omyz69scee1xR8Q0oKOkxmR+SKzIZ31mZpsqTRdv8z4cUNKpwL7AVsVXuiPCt3GZWa1SG3LXucpr4Jb0ILAN0A34C/AT4M181mlmtinS9AScfN85eXhEnAssj4ibgMP49h1CZma1QhXPDphX+U6VfJG8rpXUFvgc2DXPdZqZVVqaRpXkO3A/L6kpcBcwLSn7S57rNDOrtDSlSvIduO8G+gJHkrn9cyI5TBJuZlbdfHHyG0OAVcB9yfo5wKPAWXmu18ysUmpD7jpX+Q7ce0ZEx6z18ZLeyXOdZmaVlqZUSb5Hlbwt6dDiFUk/AF7Lc51mZpUWETkvNS0vPW5J75F5gkND4FxJ/0nWdwb+mY86zcw2R2GKetz5SpWclqfzmpnlRZpSJXkJ3MmUrmZmqVEbUiC58qPLzMxwj9vMLHU8HNDMLGV8y7uZWco4VWJmljIO3GZmKeNRJWZmKeMet5lZynhUiZlZyhRGeiZ2deA2M8M5bjOz1HGO28wsZaoyxy1pDpmHyBQC6yOis6TmwDPALsAc4KyIWL4p58/3fNxmZqlQFJHzkqNuEdEpIjon61cD4yKiAzAuWd8kDtxmZmR63Ln+2UTdyTzOkeT1jE09kQO3mRmZUSW5LpL6SJqatfTZ4HQBjJE0LWtb64hYAJC8ttrUtjrHbWYGlUmBEBGDgEHl7HJERMyX1AoYK+mDzW1fNve4zcyo2lRJRMxPXhcDw4BDgEWS2gAkr4s3ta0O3GZmVN3FSUnbStqu+D1wAjATGA70TnbrDTy3qW11qsTMjCodDtgaGCYJMjH2yYgYJekt4FlJ5wP/Ac7c1AocuM3MgMIorJLzRMTHQMdSyj8Hjq2KOhy4zczwLe9mZqnjW97NzFLGPW4zs5SpzDjumubAbWaGH6RgZpY6fpCCmVnKOMdtZpYyznGbmaWMe9xmZinjcdxmZinjHreZWcp4VImZWcr44qSZWco4VWJmljK+c9LMLGXc4zYzS5k05biVpp8y31WS+iRPlTYr4e/Fd5cfFpwOfWq6AVYr+XvxHeXAbWaWMg7cZmYp48CdDs5jWmn8vfiO8sVJM7OUcY/bzCxlHLjNzFLGgbuOkLSLpJk13Q6rOpvzb+rvQ93mwG1mljK+5b2GSPod0BP4DFgKTANeAh4EtgH+DZwXEcsldSqj/CDgYWAtMKn6P4VVgwaShgAHAP8CzgV+C/wQ2Bp4HbgwIsLfh+8O97hrgKTOwI/J/Gf8EdA52fQocEvK/4MAAAR9SURBVFVE7A+8B9xQQfkjwC8j4rDqartVuz2BQcm//UrgYuD+iDg4IvYjE7xPS/b19+E7woG7ZnQBnouILyJiFTAC2BZoGhETkn2GAEdJapJj+WPV2H6rPp9FxGvJ+8fJfHe6SXpD0nvAMcC+/j58tzhVUjNURefwIPy6b8N/4wAGAJ0j4jNJNwJb4e/Dd4p73DVjEvBDSVtJagScCqwBlks6MtmnFzAhIlaUUV4ArJDUJSnvWY3tt+qzk6Ti1Mc5fJO7Xpp8d34C4O/Dd4t73DUgIt6SNBx4B/gUmAqsAHoDD0raBvgY+EVySFnlvwAelrQWGF2NH8Gqzyygt6Q/Ax8BA4FmZK51zAHeytrX34fvCN/yXkMkNYqI1UkwfhXoExHTa7pdZlb7ucddcwZJ2odMfnKIg7aZ5co9bjOzlPHFSTOzlHHgNjNLGQduM7OUceC2MkkqlDRD0kxJ/5eMgNnUc3WV9Hzy/nRJV5ezb1NJF29CHTdK+m0l9l9d2TrMagMHbivPFxHRKZkT4yvgouyNyqj0dygihkfEHeXs0pTMnBxmVgoHbsvVRGCPZJ7nWZIGANOBHSWdIGmypOlJz7wRgKSTJH0gaRKZybRIyn8u6f7kfWtJwyS9kyyHA3cAuye9/T8k+10h6S1J70q6Ketc10n6UNJLZCZk2kgZdWRvbyRpXNL+9yR1T8q3lfRCcsxMST9Nyu+Q9M+kLXdX2d+wWY48jtsqJKkBcDIwKinaE/hFRFwsqQVwPXBcRKyRdBXwG0l3AQ+RmQRpNvBMGae/j8wt/D0k1QcaAVcD+0VEp6T+E4AOwCFk5uQYLukoMtMEnE1mlsUGZH6QTMuxjmxfAj0iYmXyeaYkd7aeBMyPiFOTdjSR1BzoAeyVTKXaNLe/RbOq48Bt5dla0ozk/URgMNAW+DQipiTlhwL7AK9JAtgCmAzsBXwSER8BSHoc6FNKHceQmWOaiCgkM99Gsw32OSFZ3k7WG5EJ5NsBwyJibVLH8DI+x0Z1bLBdwG3JD4MioB3Qmsxt5XdLuhN4PiImJj/EvgT+IukF4Pky6jTLGwduK88Xxb3eYklwXpNdBIyNiHM22K8TVTdbnYDbI+LPG9TxqyqqoyfQEjgoIr6WNAfYKiL+lTyc4BTgdkljIuJmSYcAx5Lp7V9K5geDWbVxjts21xTgCEl7AEjaRtL3gA+AXSXtnux3ThnHjwP6JsfWl9QYWEWmN11sNHBeVu68naRWZOZ46SFpa0nbkXkqTK51ZGsCLE6Cdjdg52TftsDaiHgcuBs4MGlDk4gYCfwK6IRZNXOP2zZLRCyR9HPgKUlbJsXXJ73VPsALkpaSmY50v1JOcRmZeVvOBwqBvhExWdJryjzs9sWIuELS3sDkpMe/GvhZREyX9Awwg8wsixPLaOZGdZBJ5xR7AhghaWpyrg+S8u8Df5BUBHydHLcd8Jyk4jmwf12Jvy6zKuG5SszMUsapEjOzlHHgNjNLGQduM7OUceA2M0sZB24zs5Rx4DYzSxkHbjOzlPl/OY7ZJSRSHC8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm_log, annot=True, fmt='.2f',xticklabels = [\"good\", \"bad\"] , yticklabels = [\"good\", \"bad\"] )\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.title('log_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K Neighbors classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=10, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier as KNN\n",
    "knnmodel = KNN(n_neighbors=10)\n",
    "knnmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighbors accuracy: 0.903\n",
      "KNeighbors f1 score: 0.564\n"
     ]
    }
   ],
   "source": [
    "accuracy_knnmodel=accuracy_score(y_train, knnmodel.predict(X_train))\n",
    "\n",
    "f1_score_knnmodel=f1_score(y_train, knnmodel.predict(X_train))\n",
    "\n",
    "print('KNeighbors accuracy: {:.3f}'.format(accuracy_knnmodel))\n",
    "print('KNeighbors f1 score: {:.3f}'.format(f1_score_knnmodel))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighbors accuracy: 0.870\n",
      "KNeighbors f1 score: 0.409\n"
     ]
    }
   ],
   "source": [
    "accuracy_knnmodel=accuracy_score(y_test, knnmodel.predict(X_test))\n",
    "acc = {'KNeighbors': accuracy_knnmodel}\n",
    "\n",
    "f1_score_knnmodel=f1_score(y_test, knnmodel.predict(X_test))\n",
    "f1 = {'KNeighbors': f1_score_knnmodel}\n",
    "\n",
    "print('KNeighbors accuracy: {:.3f}'.format(accuracy_knnmodel))\n",
    "print('KNeighbors f1 score: {:.3f}'.format(f1_score_knnmodel))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.8375     0.8375     0.84375    0.8625     0.86875    0.86875\n",
      " 0.875      0.88125    0.89308176 0.89375   ]\n",
      "0.866183176100629\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores=cross_val_score(knnmodel,X,y,cv=10)\n",
    "scores.sort()\n",
    "accuracy_knnmodel_cv=scores.mean()\n",
    "acc_cv = {'KNeighbors': accuracy_knnmodel_cv}\n",
    "\n",
    "print(scores)\n",
    "print(accuracy_knnmodel_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_knnmodel = knnmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.95      0.93       346\n",
      "           1       0.53      0.33      0.41        54\n",
      "\n",
      "    accuracy                           0.87       400\n",
      "   macro avg       0.72      0.64      0.67       400\n",
      "weighted avg       0.85      0.87      0.86       400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,prediction_knnmodel))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[330,  16],\n",
       "       [ 36,  18]], dtype=int64)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_knnmodel = confusion_matrix(y_test, prediction_knnmodel) \n",
    "cm_knnmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'knn_model')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm_knnmodel, annot=True, fmt='.2f',xticklabels = [\"good\", \"bad\"] , yticklabels = [\"good\", \"bad\"] )\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.title('knn_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=6, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=0, splitter='best')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier \n",
    "\n",
    "dtree_model = DecisionTreeClassifier(max_depth = 6,max_features='auto',random_state=0)\n",
    "dtree_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree accuracy: 0.912\n",
      "Decision Tree f1 score: 0.649\n"
     ]
    }
   ],
   "source": [
    "accuracy_dtree_model=accuracy_score(y_train, dtree_model.predict(X_train))\n",
    "\n",
    "f1_score_dtree_model=f1_score(y_train, dtree_model.predict(X_train))\n",
    "\n",
    "print('Decision Tree accuracy: {:.3f}'.format(accuracy_dtree_model))\n",
    "print('Decision Tree f1 score: {:.3f}'.format(f1_score_dtree_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree accuracy: 0.853\n",
      "Decision Tree f1 score: 0.416\n"
     ]
    }
   ],
   "source": [
    "accuracy_dtree_model=accuracy_score(y_test, dtree_model.predict(X_test))\n",
    "acc.update({'DecisionTree': accuracy_dtree_model})\n",
    "\n",
    "f1_score_dtree_model=f1_score(y_test, dtree_model.predict(X_test))\n",
    "f1.update({'DecisionTree': f1_score_dtree_model})\n",
    "\n",
    "print('Decision Tree accuracy: {:.3f}'.format(accuracy_dtree_model))\n",
    "print('Decision Tree f1 score: {:.3f}'.format(f1_score_dtree_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7625     0.775      0.84375    0.85534591 0.85625    0.85625\n",
      " 0.8625     0.875      0.89375    0.9       ]\n",
      "0.8480345911949685\n"
     ]
    }
   ],
   "source": [
    "scores=cross_val_score(dtree_model,X,y,cv=10)\n",
    "scores.sort()\n",
    "accuracy_dtree_model_cv=scores.mean()\n",
    "acc_cv.update({'DecisionTree': accuracy_dtree_model_cv})\n",
    "\n",
    "print(scores)\n",
    "print(accuracy_dtree_model_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_dtree_model = dtree_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.92      0.92       346\n",
      "           1       0.45      0.39      0.42        54\n",
      "\n",
      "    accuracy                           0.85       400\n",
      "   macro avg       0.68      0.66      0.67       400\n",
      "weighted avg       0.84      0.85      0.85       400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,prediction_dtree_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[320,  26],\n",
       "       [ 33,  21]], dtype=int64)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_dtree_model = confusion_matrix(y_test, prediction_dtree_model) \n",
    "cm_dtree_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'dtree_model')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm_dtree_model, annot=True, fmt='.2f',xticklabels = [\"good\", \"bad\"] , yticklabels = [\"good\", \"bad\"] )\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.title('dtree_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=6, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=200,\n",
       "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfm = RandomForestClassifier(n_estimators=200,max_depth=6,random_state=0)\n",
    "rfm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest accuracy: 0.949\n",
      "Random Forest f1 score: 0.773\n"
     ]
    }
   ],
   "source": [
    "accuracy_rfm=accuracy_score(y_train, rfm.predict(X_train))\n",
    "\n",
    "f1_score_rfm=f1_score(y_train, rfm.predict(X_train))\n",
    "\n",
    "print('Random Forest accuracy: {:.3f}'.format(accuracy_rfm))\n",
    "print('Random Forest f1 score: {:.3f}'.format(f1_score_rfm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest accuracy: 0.880\n",
      "Random Forest f1 score: 0.429\n"
     ]
    }
   ],
   "source": [
    "accuracy_rfm=accuracy_score(y_test, rfm.predict(X_test))\n",
    "acc.update({'RandomForest': accuracy_rfm})\n",
    "\n",
    "f1_score_rfm=f1_score(y_test, rfm.predict(X_test))\n",
    "f1.update({'RandomForest': f1_score_rfm})\n",
    "\n",
    "print('Random Forest accuracy: {:.3f}'.format(accuracy_rfm))\n",
    "print('Random Forest f1 score: {:.3f}'.format(f1_score_rfm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.85       0.85625    0.88125    0.88125    0.8875     0.8875\n",
      " 0.8875     0.8875     0.89308176 0.89375   ]\n",
      "0.8805581761006289\n"
     ]
    }
   ],
   "source": [
    "scores=cross_val_score(rfm,X,y,cv=10)\n",
    "scores.sort()\n",
    "accuracy_rfm_cv=scores.mean()\n",
    "acc_cv.update({'RandomForest': accuracy_rfm_cv})\n",
    "    \n",
    "print(scores)\n",
    "print(accuracy_rfm_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_rfm= rfm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.97      0.93       346\n",
      "           1       0.60      0.33      0.43        54\n",
      "\n",
      "    accuracy                           0.88       400\n",
      "   macro avg       0.75      0.65      0.68       400\n",
      "weighted avg       0.86      0.88      0.86       400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,prediction_rfm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[334,  12],\n",
       "       [ 36,  18]], dtype=int64)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_rfm = confusion_matrix(y_test, prediction_rfm) \n",
    "cm_rfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'rfm_model')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm_rfm, annot=True, fmt='.2f',xticklabels = [\"good\", \"bad\"] , yticklabels = [\"good\", \"bad\"] )\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.title('rfm_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=0, shrinking=True, tol=0.001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svm = SVC(kernel='rbf',random_state=0)\n",
    "svm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM accuracy: 0.902\n",
      "SVM f1 score: 0.494\n"
     ]
    }
   ],
   "source": [
    "accuracy_svm=accuracy_score(y_train, svm.predict(X_train))\n",
    "\n",
    "f1_score_svm=f1_score(y_train, svm.predict(X_train))\n",
    "\n",
    "print('SVM accuracy: {:.3f}'.format(accuracy_svm))\n",
    "print('SVM f1 score: {:.3f}'.format(f1_score_svm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM accuracy: 0.873\n",
      "SVM f1 score: 0.320\n"
     ]
    }
   ],
   "source": [
    "accuracy_svm=accuracy_score(y_test, svm.predict(X_test))\n",
    "acc.update({'SupportVectorMachine': accuracy_svm})\n",
    "\n",
    "f1_score_svm=f1_score(y_test, svm.predict(X_test))\n",
    "f1.update({'SupportVectorMachine': f1_score_svm})\n",
    "\n",
    "print('SVM accuracy: {:.3f}'.format(accuracy_svm))\n",
    "print('SVM f1 score: {:.3f}'.format(f1_score_svm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.85       0.8625     0.86875    0.875      0.88125    0.88125\n",
      " 0.88679245 0.8875     0.8875     0.89375   ]\n",
      "0.8774292452830188\n"
     ]
    }
   ],
   "source": [
    "scores=cross_val_score(svm,X,y,cv=10)\n",
    "scores.sort()\n",
    "accuracy_svm_cv=scores.mean()\n",
    "acc_cv.update({'SupportVectorMachine': accuracy_svm_cv})\n",
    "    \n",
    "print(scores)\n",
    "print(accuracy_svm_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_svm = svm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.97      0.93       346\n",
      "           1       0.57      0.22      0.32        54\n",
      "\n",
      "    accuracy                           0.87       400\n",
      "   macro avg       0.73      0.60      0.62       400\n",
      "weighted avg       0.85      0.87      0.85       400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,prediction_svm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[337,   9],\n",
       "       [ 42,  12]], dtype=int64)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_svm = confusion_matrix(y_test, prediction_svm) \n",
    "cm_svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'svm_model')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm_svm, annot=True, fmt='.2f',xticklabels = [\"good\", \"bad\"] , yticklabels = [\"good\", \"bad\"] )\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.title('svm_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gaussian Naive bayes classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gaussian_model=  GaussianNB()\n",
    "gaussian_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive bayes accuracy: 0.860\n",
      "Gaussian Naive bayes f1 score: 0.560\n"
     ]
    }
   ],
   "source": [
    "accuracy_gaussian_model=accuracy_score(y_train, gaussian_model.predict(X_train))\n",
    "\n",
    "f1_score_gaussian_model=f1_score(y_train, gaussian_model.predict(X_train))\n",
    "\n",
    "print('Gaussian Naive bayes accuracy: {:.3f}'.format(accuracy_gaussian_model))\n",
    "print('Gaussian Naive bayes f1 score: {:.3f}'.format(f1_score_gaussian_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive bayes accuracy: 0.838\n",
      "Gaussian Naive bayes f1 score: 0.496\n"
     ]
    }
   ],
   "source": [
    "accuracy_gaussian_model=accuracy_score(y_test, gaussian_model.predict(X_test))\n",
    "acc.update({'Gaussianmodel': accuracy_gaussian_model})\n",
    "\n",
    "f1_score_gaussian_model=f1_score(y_test, gaussian_model.predict(X_test))\n",
    "f1.update({'Gaussianmodel': f1_score_gaussian_model})\n",
    "\n",
    "print('Gaussian Naive bayes accuracy: {:.3f}'.format(accuracy_gaussian_model))\n",
    "print('Gaussian Naive bayes f1 score: {:.3f}'.format(f1_score_gaussian_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.66875    0.7625     0.83647799 0.8375     0.8375     0.86875\n",
      " 0.875      0.89375    0.90625    0.91875   ]\n",
      "0.8405227987421384\n"
     ]
    }
   ],
   "source": [
    "scores=cross_val_score(gaussian_model,X,y,cv=10)\n",
    "scores.sort()\n",
    "accuracy_gaussian_model_cv=scores.mean()\n",
    "acc_cv.update({'Gaussianmodel': accuracy_gaussian_model_cv})\n",
    "\n",
    "print(scores)\n",
    "print(accuracy_gaussian_model_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_gaussian_model = gaussian_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.88      0.90       346\n",
      "           1       0.43      0.59      0.50        54\n",
      "\n",
      "    accuracy                           0.84       400\n",
      "   macro avg       0.68      0.73      0.70       400\n",
      "weighted avg       0.86      0.84      0.85       400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,prediction_gaussian_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[303,  43],\n",
       "       [ 22,  32]], dtype=int64)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_gaussian_model = confusion_matrix(y_test, prediction_gaussian_model) \n",
    "cm_gaussian_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'gaussian model')"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm_gaussian_model, annot=True, fmt='.2f',xticklabels = [\"good\", \"bad\"] , yticklabels = [\"good\", \"bad\"] )\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.title('gaussian model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance metrics of different models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiclass classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5675 0.5875 0.6425 0.6575 0.5725\n"
     ]
    }
   ],
   "source": [
    "#accuracy\n",
    "print(accuracy_knnm,accuracy_dtm,accuracy_svcm,accuracy_randommodel,accuracy_gaussianmodel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5484669811320755 0.520939465408805 0.5853734276729561 0.5822602201257862 0.553498427672956 0.845\n"
     ]
    }
   ],
   "source": [
    "#accuracy after cross validation\n",
    "print(accuracy_knn,accuracy_dt,accuracy_svc,accuracy_rf,accuracy_gnb,accuracy_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binary classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'KNeighbors': 0.87, 'DecisionTree': 0.8525, 'RandomForest': 0.88, 'SupportVectorMachine': 0.8725, 'Gaussianmodel': 0.8375}\n"
     ]
    }
   ],
   "source": [
    "#accuracy before cross validation\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'KNeighbors': 0.866183176100629, 'DecisionTree': 0.8480345911949685, 'RandomForest': 0.8805581761006289, 'SupportVectorMachine': 0.8774292452830188, 'Gaussianmodel': 0.8405227987421384}\n"
     ]
    }
   ],
   "source": [
    "#accuracy after cross validation\n",
    "print(acc_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'KNeighbors': 0.409090909090909, 'DecisionTree': 0.4158415841584158, 'RandomForest': 0.42857142857142855, 'SupportVectorMachine': 0.32, 'Gaussianmodel': 0.49612403100775193}\n"
     ]
    }
   ],
   "source": [
    "# f1 scores\n",
    "print(f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the model to disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'model_rfm.pkl'\n",
    "joblib.dump(rfm,open(filename,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
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}