Prediction of wine quality I.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "noble-cheat",
   "metadata": {},
   "source": [
    "### Done as assignments for the microcredential in data science course. Copyright of the University of Houston HPE Data Science Institute."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "grave-helen",
   "metadata": {},
   "source": [
    "### Load the dataset in the file named winequality_white.csv and produce at least one table and one graph that summarize the dataset statistics. Separate the data into training and testing datasets and set up a classification problem: predicting the quality value (a single variable with seven classes labeled 3, 4, 5, …, 9) based on the values of all the other variables in the file (acidity, alcohol, pH, etc.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "confidential-mississippi",
   "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>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      8.8        6  \n",
       "1      9.5        6  \n",
       "2     10.1        6  \n",
       "3      9.9        6  \n",
       "4      9.9        6  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>4893</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.039</td>\n",
       "      <td>24.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.99114</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.50</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4894</th>\n",
       "      <td>6.6</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.36</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.047</td>\n",
       "      <td>57.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.99490</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4895</th>\n",
       "      <td>6.5</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.041</td>\n",
       "      <td>30.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.99254</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4896</th>\n",
       "      <td>5.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.022</td>\n",
       "      <td>20.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.98869</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0.38</td>\n",
       "      <td>12.8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4897</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.020</td>\n",
       "      <td>22.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.98941</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.32</td>\n",
       "      <td>11.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "4893            6.2              0.21         0.29             1.6      0.039   \n",
       "4894            6.6              0.32         0.36             8.0      0.047   \n",
       "4895            6.5              0.24         0.19             1.2      0.041   \n",
       "4896            5.5              0.29         0.30             1.1      0.022   \n",
       "4897            6.0              0.21         0.38             0.8      0.020   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "4893                 24.0                  92.0  0.99114  3.27       0.50   \n",
       "4894                 57.0                 168.0  0.99490  3.15       0.46   \n",
       "4895                 30.0                 111.0  0.99254  2.99       0.46   \n",
       "4896                 20.0                 110.0  0.98869  3.34       0.38   \n",
       "4897                 22.0                  98.0  0.98941  3.26       0.32   \n",
       "\n",
       "      alcohol  quality  \n",
       "4893     11.2        6  \n",
       "4894      9.6        5  \n",
       "4895      9.4        6  \n",
       "4896     12.8        7  \n",
       "4897     11.8        6  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dataset = pd.read_csv('winequality-white.csv')\n",
    "from IPython.display import display\n",
    "display(dataset.head(5))\n",
    "display(dataset.tail(5))\n",
    "print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "centered-sphere",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of the dataset - Univariate statistics\n"
     ]
    },
    {
     "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>count</th>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.854788</td>\n",
       "      <td>0.278241</td>\n",
       "      <td>0.334192</td>\n",
       "      <td>6.391415</td>\n",
       "      <td>0.045772</td>\n",
       "      <td>35.308085</td>\n",
       "      <td>138.360657</td>\n",
       "      <td>0.994027</td>\n",
       "      <td>3.188267</td>\n",
       "      <td>0.489847</td>\n",
       "      <td>10.514267</td>\n",
       "      <td>5.877909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.843868</td>\n",
       "      <td>0.100795</td>\n",
       "      <td>0.121020</td>\n",
       "      <td>5.072058</td>\n",
       "      <td>0.021848</td>\n",
       "      <td>17.007137</td>\n",
       "      <td>42.498065</td>\n",
       "      <td>0.002991</td>\n",
       "      <td>0.151001</td>\n",
       "      <td>0.114126</td>\n",
       "      <td>1.230621</td>\n",
       "      <td>0.885639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.800000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.009000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.987110</td>\n",
       "      <td>2.720000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.300000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>0.036000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>0.991723</td>\n",
       "      <td>3.090000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>0.043000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>0.993740</td>\n",
       "      <td>3.180000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>0.996100</td>\n",
       "      <td>3.280000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>11.400000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.200000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.660000</td>\n",
       "      <td>65.800000</td>\n",
       "      <td>0.346000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>1.038980</td>\n",
       "      <td>3.820000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    4898.000000       4898.000000  4898.000000     4898.000000   \n",
       "mean        6.854788          0.278241     0.334192        6.391415   \n",
       "std         0.843868          0.100795     0.121020        5.072058   \n",
       "min         3.800000          0.080000     0.000000        0.600000   \n",
       "25%         6.300000          0.210000     0.270000        1.700000   \n",
       "50%         6.800000          0.260000     0.320000        5.200000   \n",
       "75%         7.300000          0.320000     0.390000        9.900000   \n",
       "max        14.200000          1.100000     1.660000       65.800000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  4898.000000          4898.000000           4898.000000  4898.000000   \n",
       "mean      0.045772            35.308085            138.360657     0.994027   \n",
       "std       0.021848            17.007137             42.498065     0.002991   \n",
       "min       0.009000             2.000000              9.000000     0.987110   \n",
       "25%       0.036000            23.000000            108.000000     0.991723   \n",
       "50%       0.043000            34.000000            134.000000     0.993740   \n",
       "75%       0.050000            46.000000            167.000000     0.996100   \n",
       "max       0.346000           289.000000            440.000000     1.038980   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  4898.000000  4898.000000  4898.000000  4898.000000  \n",
       "mean      3.188267     0.489847    10.514267     5.877909  \n",
       "std       0.151001     0.114126     1.230621     0.885639  \n",
       "min       2.720000     0.220000     8.000000     3.000000  \n",
       "25%       3.090000     0.410000     9.500000     5.000000  \n",
       "50%       3.180000     0.470000    10.400000     6.000000  \n",
       "75%       3.280000     0.550000    11.400000     6.000000  \n",
       "max       3.820000     1.080000    14.200000     9.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Summary of the dataset - Univariate statistics')   \n",
    "display(dataset.describe(include='all'))\n",
    "print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "authorized-jonathan",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistical summary of X:\n"
     ]
    },
    {
     "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>pH</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>density</th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>sulphates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.188267</td>\n",
       "      <td>6.391415</td>\n",
       "      <td>10.514267</td>\n",
       "      <td>0.045772</td>\n",
       "      <td>0.334192</td>\n",
       "      <td>0.994027</td>\n",
       "      <td>6.854788</td>\n",
       "      <td>0.278241</td>\n",
       "      <td>35.308085</td>\n",
       "      <td>138.360657</td>\n",
       "      <td>0.489847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.151001</td>\n",
       "      <td>5.072058</td>\n",
       "      <td>1.230621</td>\n",
       "      <td>0.021848</td>\n",
       "      <td>0.121020</td>\n",
       "      <td>0.002991</td>\n",
       "      <td>0.843868</td>\n",
       "      <td>0.100795</td>\n",
       "      <td>17.007137</td>\n",
       "      <td>42.498065</td>\n",
       "      <td>0.114126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.720000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.009000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.987110</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.090000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>0.036000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>0.991723</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>0.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.180000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>0.043000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.993740</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>0.470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.280000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>11.400000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.996100</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.820000</td>\n",
       "      <td>65.800000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>0.346000</td>\n",
       "      <td>1.660000</td>\n",
       "      <td>1.038980</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>1.080000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                pH  residual sugar      alcohol    chlorides  citric acid  \\\n",
       "count  4898.000000     4898.000000  4898.000000  4898.000000  4898.000000   \n",
       "mean      3.188267        6.391415    10.514267     0.045772     0.334192   \n",
       "std       0.151001        5.072058     1.230621     0.021848     0.121020   \n",
       "min       2.720000        0.600000     8.000000     0.009000     0.000000   \n",
       "25%       3.090000        1.700000     9.500000     0.036000     0.270000   \n",
       "50%       3.180000        5.200000    10.400000     0.043000     0.320000   \n",
       "75%       3.280000        9.900000    11.400000     0.050000     0.390000   \n",
       "max       3.820000       65.800000    14.200000     0.346000     1.660000   \n",
       "\n",
       "           density  fixed acidity  volatile acidity  free sulfur dioxide  \\\n",
       "count  4898.000000    4898.000000       4898.000000          4898.000000   \n",
       "mean      0.994027       6.854788          0.278241            35.308085   \n",
       "std       0.002991       0.843868          0.100795            17.007137   \n",
       "min       0.987110       3.800000          0.080000             2.000000   \n",
       "25%       0.991723       6.300000          0.210000            23.000000   \n",
       "50%       0.993740       6.800000          0.260000            34.000000   \n",
       "75%       0.996100       7.300000          0.320000            46.000000   \n",
       "max       1.038980      14.200000          1.100000           289.000000   \n",
       "\n",
       "       total sulfur dioxide    sulphates  \n",
       "count           4898.000000  4898.000000  \n",
       "mean             138.360657     0.489847  \n",
       "std               42.498065     0.114126  \n",
       "min                9.000000     0.220000  \n",
       "25%              108.000000     0.410000  \n",
       "50%              134.000000     0.470000  \n",
       "75%              167.000000     0.550000  \n",
       "max              440.000000     1.080000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_name = [ 'pH', 'residual sugar', 'alcohol','chlorides','citric acid','density','fixed acidity','volatile acidity','free sulfur dioxide','total sulfur dioxide','sulphates'] \n",
    "X = dataset[X_name]  \n",
    "print('Statistical summary of X:')\n",
    "display(X.describe())\n",
    "print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "invisible-review",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of X - Univariate graphs\n",
      "\n",
      "Histograms:\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as pl  \n",
    "\n",
    "print('Summary of X - Univariate graphs')\n",
    "print()\n",
    "\n",
    "print('Histograms:')\n",
    "X.hist(figsize=(20,10), bins=50) \n",
    "pl.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "chief-generator",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    2198\n",
       "5    1457\n",
       "7     880\n",
       "8     175\n",
       "4     163\n",
       "3      20\n",
       "9       5\n",
       "Name: quality, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_name = 'quality'\n",
    "y = dataset[y_name]\n",
    "display(y.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "widespread-deviation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done with setting aside data for testing\n"
     ]
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "\n",
    "test_pct = 0.20\n",
    "seed = 7          \n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=test_pct, random_state=seed)\n",
    "print('done with setting aside data for testing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "genuine-ukraine",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on model fitting...\n",
      "done with training the model\n"
     ]
    }
   ],
   "source": [
    "print('Working on model fitting...')\n",
    "\n",
    "import sklearn  \n",
    "from sklearn.neighbors import KNeighborsClassifier  \n",
    "\n",
    "neigh = KNeighborsClassifier()  \n",
    "neigh.fit(X_train, y_train) \n",
    "print('done with training the model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "neither-munich",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing accuracy...\n",
      "Accuracy: 0.46938775510204084\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print('computing accuracy...')\n",
    "y_predicted = neigh.predict(X_test)   \n",
    "print('Accuracy:', accuracy_score(y_test, y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "wrong-integer",
   "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>pH</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>density</th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>sulphates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3004</th>\n",
       "      <td>3.16</td>\n",
       "      <td>1.20</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.99074</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.180</td>\n",
       "      <td>26.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3222</th>\n",
       "      <td>3.01</td>\n",
       "      <td>1.10</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.98990</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.160</td>\n",
       "      <td>41.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4046</th>\n",
       "      <td>2.94</td>\n",
       "      <td>17.55</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.99971</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.170</td>\n",
       "      <td>33.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1170</th>\n",
       "      <td>3.38</td>\n",
       "      <td>4.50</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.99660</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.250</td>\n",
       "      <td>20.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4672</th>\n",
       "      <td>3.47</td>\n",
       "      <td>1.30</td>\n",
       "      <td>10.466667</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.99212</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.335</td>\n",
       "      <td>69.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pH  residual sugar    alcohol  chlorides  citric acid  density  \\\n",
       "3004  3.16            1.20  11.000000      0.034         0.36  0.99074   \n",
       "3222  3.01            1.10  12.000000      0.037         0.34  0.98990   \n",
       "4046  2.94           17.55   9.000000      0.050         0.28  0.99971   \n",
       "1170  3.38            4.50   9.900000      0.062         0.43  0.99660   \n",
       "4672  3.47            1.30  10.466667      0.036         0.14  0.99212   \n",
       "\n",
       "      fixed acidity  volatile acidity  free sulfur dioxide  \\\n",
       "3004            6.3             0.180                 26.0   \n",
       "3222            6.6             0.160                 41.0   \n",
       "4046            7.2             0.170                 33.0   \n",
       "1170            7.7             0.250                 20.0   \n",
       "4672            4.9             0.335                 69.0   \n",
       "\n",
       "      total sulfur dioxide  sulphates  \n",
       "3004                 111.0       0.51  \n",
       "3222                 115.0       0.68  \n",
       "4046                 154.0       0.43  \n",
       "1170                 115.0       0.50  \n",
       "4672                 168.0       0.46  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "rental-sharing",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'pH': [1.00], 'residual sugar': [17.55], 'alcohol': [10.0],'chlorides':[0.05],'citric acid':[0.4],'density':[0.98],'fixed acidity':[7.0],'volatile acidity':[0.2],'free sulfur dioxide':[20.0],'total sulfur dioxide':[100.0],'sulphates':[0.5]}\n",
    "df=pd.DataFrame(data=d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "regulated-heather",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh.predict(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "standard-mongolia",
   "metadata": {},
   "source": [
    "### Train and tune (via cross-validation) at least two different models based on Decision Trees (e.g., DecisionTreeClassifier, RandomForestClassifier); Consider at least two different hyperparameter options (e.g., tree depth)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "orange-pressing",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as pl  \n",
    "from sklearn import model_selection \n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "future-charger",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data from file ...\n",
      "Removing rows with missing data ...\n",
      "Reading list of problem variables X and y...\n",
      "Partitioning data into parts: formative (for development) and summative (for testing) ...\n",
      "done \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Loading data from file ...')  \n",
    "dataset = pandas.read_csv('winequality-white.csv')\n",
    "print('Removing rows with missing data ...')  \n",
    "dataset = dataset.dropna()\n",
    "print('Reading list of problem variables X and y...')\n",
    "X_name =[ 'pH', 'residual sugar', 'alcohol','chlorides','citric acid','density','fixed acidity','volatile acidity','free sulfur dioxide','total sulfur dioxide','sulphates'] \n",
    "y_name = 'quality'\n",
    "X = dataset[X_name]   \n",
    "y = dataset[y_name]   \n",
    "\n",
    "\n",
    "seed = 42 \n",
    "\n",
    "print('Partitioning data into parts: formative (for development) and summative (for testing) ...')\n",
    "test_size = 0.20  \n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=test_size, random_state=seed)\n",
    "\n",
    "print('done \\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "early-majority",
   "metadata": {},
   "outputs": [],
   "source": [
    "scoring = 'f1_macro'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "durable-benjamin",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning model...\n",
      "Best hyperparameters found on development set for Decision Tree:\n",
      "{'criterion': 'gini', 'max_depth': 7}\n",
      "Decision tree has maximum depth 7.\n",
      "f1_score is\n",
      "0.2963277540166415\n"
     ]
    }
   ],
   "source": [
    "# Decision Tree\n",
    "print('Tuning model...')\n",
    "selected_model = DecisionTreeClassifier()\n",
    "hyperparameters = {'max_depth':[4, 5, 6, 7], 'criterion':['gini', 'entropy'] }\n",
    "clf = GridSearchCV(selected_model, hyperparameters, cv=5, scoring=scoring)\n",
    "clf.fit(X_train, y_train)\n",
    "print(\"Best hyperparameters found on development set for Decision Tree:\")\n",
    "print(clf.best_params_)\n",
    "tuned_model_DT = clf.best_estimator_\n",
    "\n",
    "print(f'Decision tree has maximum depth {tuned_model_DT.tree_.max_depth}.')\n",
    "y_pred = tuned_model_DT.predict(X_test)\n",
    "print( 'f1_score is')\n",
    "print( f1_score(y_test, y_pred, average='macro') )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "differential-completion",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning model...\n",
      "Best hyperparameters found on development set for Random Forest:\n",
      "{'criterion': 'entropy', 'max_depth': 7, 'n_estimators': 10}\n",
      "f1_score is\n",
      "0.31039306134945116\n"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import f1_score\n",
    "print('Tuning model...')\n",
    "selected_model = RandomForestClassifier()\n",
    "hyperparameters = {'max_depth':[4, 5,6,7], 'criterion':['gini', 'entropy'], 'n_estimators':[10, 50, 100] }\n",
    "clf = GridSearchCV(selected_model, hyperparameters, cv=5, scoring=scoring)\n",
    "clf.fit(X_train, y_train)\n",
    "print(\"Best hyperparameters found on development set for Random Forest:\")\n",
    "print(clf.best_params_)\n",
    "tuned_model_RF = clf.best_estimator_\n",
    "\n",
    "y_pred = tuned_model_RF.predict(X_test)\n",
    "print( 'f1_score is')\n",
    "print( f1_score(y_test, y_pred, average='macro') )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "reflected-consumer",
   "metadata": {},
   "source": [
    "### Train and tune (via cross-validation) at least two different SVM models based on different kernel options (e.g., linear and sigmoid) and regularization parameters (different values of C)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "reflected-voltage",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "universal-dryer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_blobs(n_samples=40, centers=2, random_state=6)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)\n",
    "ax = plt.gca()\n",
    "xlim = ax.get_xlim()\n",
    "ylim = ax.get_ylim()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "australian-newton",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking diiferent parameters\n",
    "#clf = svm.SVC(kernel='linear', C=0.01)   # large C means little to no regularization\n",
    "#clf = svm.SVC(kernel='linear', C=1000)  # small C means regularization\n",
    "clf = svm.SVC(kernel='sigmoid', C=1000)\n",
    "#clf = svm.SVC(kernel='sigmoid', C=0.01)\n",
    "\n",
    "clf.fit(X, y)\n",
    "\n",
    "# create grid to evaluate model\n",
    "xx = np.linspace(xlim[0], xlim[1], 30)\n",
    "yy = np.linspace(ylim[0], ylim[1], 30)\n",
    "YY, XX = np.meshgrid(yy, xx)\n",
    "xy = np.vstack([XX.ravel(), YY.ravel()]).T\n",
    "Z = clf.decision_function(xy).reshape(XX.shape)\n",
    "\n",
    "# plot the decision function\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)\n",
    "ax = plt.gca()\n",
    "xlim = ax.get_xlim()\n",
    "ylim = ax.get_ylim()\n",
    "\n",
    "# plot decision boundary and margins\n",
    "ax.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,\n",
    "           linestyles=['--', '-', '--'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "imported-breeding",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the decision function\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)\n",
    "ax = plt.gca()\n",
    "xlim = ax.get_xlim()\n",
    "ylim = ax.get_ylim()\n",
    "\n",
    "# plot decision boundary and margins\n",
    "ax.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,\n",
    "           linestyles=['--', '-', '--'])\n",
    "\n",
    "# plot support vectors\n",
    "ax.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=100,\n",
    "           linewidth=1, facecolors='none', edgecolors='k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "centered-authorization",
   "metadata": {},
   "source": [
    "### Use the make_pipeline() method to study and describe the impact of feature selection on the performance of the tuned SVM from Step 3. You can try dimension reduction (e.g., using different n_component values for PCA) and/or data scaling (e.g., MinMaxScaler)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "structural-gnome",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "efficient-capability",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading list of algorithms to train ...\n",
      "done \n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "seed = 42 # setting the seed allows for repeatability\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "print('Reading list of algorithms to train ...')\n",
    "models = []\n",
    "models.append(( 'raw_SVM', SVC(random_state=seed) ))\n",
    "models.append(( 'scaled_SVM', make_pipeline( MinMaxScaler(), SVC(random_state=seed) )  ))\n",
    "print('done \\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "divided-precipitation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('raw_SVM', SVC(random_state=42)),\n",
       " ('scaled_SVM',\n",
       "  Pipeline(steps=[('minmaxscaler', MinMaxScaler()),\n",
       "                  ('svc', SVC(random_state=42))]))]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "streaming-tribune",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ++ NOW WORKING ON ALGORITHM raw_SVM ++\n",
      "Splitting data into 5 folds\n",
      "Training model on each split ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ................................ score: (test=0.105) total time=   0.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.6s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ................................ score: (test=0.111) total time=   0.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    1.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ................................ score: (test=0.141) total time=   0.6s\n",
      "[CV] END ................................ score: (test=0.099) total time=   0.6s\n",
      "[CV] END ................................ score: (test=0.123) total time=   0.6s\n",
      "algorithm raw_SVM f1_macro results: mean = 0.115969 (std = 0.014971)\n",
      " ++ NOW WORKING ON ALGORITHM scaled_SVM ++\n",
      "Splitting data into 5 folds\n",
      "Training model on each split ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    3.3s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ................................ score: (test=0.223) total time=   0.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.5s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ................................ score: (test=0.268) total time=   0.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    1.1s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ................................ score: (test=0.258) total time=   0.5s\n",
      "[CV] END ................................ score: (test=0.213) total time=   0.5s\n",
      "[CV] END ................................ score: (test=0.251) total time=   0.5s\n",
      "algorithm scaled_SVM f1_macro results: mean = 0.242574 (std = 0.021082)\n",
      "done \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    2.9s finished\n"
     ]
    }
   ],
   "source": [
    "\n",
    "k4folds = 5\n",
    "results = []\n",
    "names = []\n",
    "for name, model in models:   # Select each model in turn\n",
    "    print(\" ++ NOW WORKING ON ALGORITHM %s ++\" % name)\n",
    "    print(\"Splitting data into %s folds\" % k4folds)\n",
    "    kfold = model_selection.KFold(n_splits=k4folds, random_state=seed, shuffle=True)\n",
    "    print(\"Training model on each split ...\")\n",
    "    cv_results = model_selection.cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring, verbose=3)\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    msg = \"algorithm %s %s results: mean = %f (std = %f)\" % (name, scoring, cv_results.mean(), cv_results.std())\n",
    "    print(msg)\n",
    "    \n",
    "print('done \\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "signed-laundry",
   "metadata": {},
   "source": [
    "### Question 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "uniform-protein",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pl.figure(figsize=(15,5))\n",
    "fig.suptitle('Algorithm Comparison based on %s' % scoring)\n",
    "ax = fig.add_subplot(111)\n",
    "pl.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hundred-software",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}