Naïve Bayes .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4dda8001",
   "metadata": {},
   "source": [
    "- Naive Bayes is a simple yet effective classification algorithm based on Bayes theorem with the assumption of feature       independence. \n",
    "- It's called \"naive\" because it assumes that all features are independent of each other, which may not be true in reality.\n",
    "- Despite this simplifying assumption, Naive Bayes often performs well in practice and is widely used in various             applications, especially for text classification and spam filtering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "70294fa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "wine = datasets.load_wine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f8df3116",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "         1.065e+03],\n",
       "        [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n",
       "         1.050e+03],\n",
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       "         1.185e+03],\n",
       "        ...,\n",
       "        [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n",
       "         8.350e+02],\n",
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       "         8.400e+02],\n",
       "        [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n",
       "         5.600e+02]]),\n",
       " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2]),\n",
       " 'frame': None,\n",
       " 'target_names': array(['class_0', 'class_1', 'class_2'], dtype='<U7'),\n",
       " 'DESCR': '.. _wine_dataset:\\n\\nWine recognition dataset\\n------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 178 (50 in each of three classes)\\n    :Number of Attributes: 13 numeric, predictive attributes and the class\\n    :Attribute Information:\\n \\t\\t- Alcohol\\n \\t\\t- Malic acid\\n \\t\\t- Ash\\n\\t\\t- Alcalinity of ash  \\n \\t\\t- Magnesium\\n\\t\\t- Total phenols\\n \\t\\t- Flavanoids\\n \\t\\t- Nonflavanoid phenols\\n \\t\\t- Proanthocyanins\\n\\t\\t- Color intensity\\n \\t\\t- Hue\\n \\t\\t- OD280/OD315 of diluted wines\\n \\t\\t- Proline\\n\\n    - class:\\n            - class_0\\n            - class_1\\n            - class_2\\n\\t\\t\\n    :Summary Statistics:\\n    \\n    ============================= ==== ===== ======= =====\\n                                   Min   Max   Mean     SD\\n    ============================= ==== ===== ======= =====\\n    Alcohol:                      11.0  14.8    13.0   0.8\\n    Malic Acid:                   0.74  5.80    2.34  1.12\\n    Ash:                          1.36  3.23    2.36  0.27\\n    Alcalinity of Ash:            10.6  30.0    19.5   3.3\\n    Magnesium:                    70.0 162.0    99.7  14.3\\n    Total Phenols:                0.98  3.88    2.29  0.63\\n    Flavanoids:                   0.34  5.08    2.03  1.00\\n    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\\n    Proanthocyanins:              0.41  3.58    1.59  0.57\\n    Colour Intensity:              1.3  13.0     5.1   2.3\\n    Hue:                          0.48  1.71    0.96  0.23\\n    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\\n    Proline:                       278  1680     746   315\\n    ============================= ==== ===== ======= =====\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThis is a copy of UCI ML Wine recognition datasets.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\\n\\nThe data is the results of a chemical analysis of wines grown in the same\\nregion in Italy by three different cultivators. There are thirteen different\\nmeasurements taken for different constituents found in the three types of\\nwine.\\n\\nOriginal Owners: \\n\\nForina, M. et al, PARVUS - \\nAn Extendible Package for Data Exploration, Classification and Correlation. \\nInstitute of Pharmaceutical and Food Analysis and Technologies,\\nVia Brigata Salerno, 16147 Genoa, Italy.\\n\\nCitation:\\n\\nLichman, M. (2013). UCI Machine Learning Repository\\n[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\\nSchool of Information and Computer Science. \\n\\n.. topic:: References\\n\\n  (1) S. Aeberhard, D. Coomans and O. de Vel, \\n  Comparison of Classifiers in High Dimensional Settings, \\n  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Technometrics). \\n\\n  The data was used with many others for comparing various \\n  classifiers. The classes are separable, though only RDA \\n  has achieved 100% correct classification. \\n  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \\n  (All results using the leave-one-out technique) \\n\\n  (2) S. Aeberhard, D. Coomans and O. de Vel, \\n  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \\n  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Journal of Chemometrics).\\n',\n",
       " 'feature_names': ['alcohol',\n",
       "  'malic_acid',\n",
       "  'ash',\n",
       "  'alcalinity_of_ash',\n",
       "  'magnesium',\n",
       "  'total_phenols',\n",
       "  'flavanoids',\n",
       "  'nonflavanoid_phenols',\n",
       "  'proanthocyanins',\n",
       "  'color_intensity',\n",
       "  'hue',\n",
       "  'od280/od315_of_diluted_wines',\n",
       "  'proline']}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "40d18d89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'frame', 'target', 'target_names']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8d058fd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alcohol',\n",
       " 'malic_acid',\n",
       " 'ash',\n",
       " 'alcalinity_of_ash',\n",
       " 'magnesium',\n",
       " 'total_phenols',\n",
       " 'flavanoids',\n",
       " 'nonflavanoid_phenols',\n",
       " 'proanthocyanins',\n",
       " 'color_intensity',\n",
       " 'hue',\n",
       " 'od280/od315_of_diluted_wines',\n",
       " 'proline']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f83252fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
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       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "90ae096a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['class_0', 'class_1', 'class_2'], dtype='<U7')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "61097a45",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2    13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3    14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4    13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "\n",
       "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0        3.06                  0.28             2.29             5.64  1.04   \n",
       "1        2.76                  0.26             1.28             4.38  1.05   \n",
       "2        3.24                  0.30             2.81             5.68  1.03   \n",
       "3        3.49                  0.24             2.18             7.80  0.86   \n",
       "4        2.69                  0.39             1.82             4.32  1.04   \n",
       "\n",
       "   od280/od315_of_diluted_wines  proline  \n",
       "0                          3.92   1065.0  \n",
       "1                          3.40   1050.0  \n",
       "2                          3.17   1185.0  \n",
       "3                          3.45   1480.0  \n",
       "4                          2.93    735.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(wine.data,columns=wine.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cdbc7ee5",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>13.05</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.04</td>\n",
       "      <td>12.4</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2.72</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.17</td>\n",
       "      <td>2.91</td>\n",
       "      <td>7.20</td>\n",
       "      <td>1.12</td>\n",
       "      <td>2.91</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>13.83</td>\n",
       "      <td>1.65</td>\n",
       "      <td>2.60</td>\n",
       "      <td>17.2</td>\n",
       "      <td>94.0</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.60</td>\n",
       "      <td>1.24</td>\n",
       "      <td>3.37</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>13.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.42</td>\n",
       "      <td>14.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>3.88</td>\n",
       "      <td>3.74</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.87</td>\n",
       "      <td>7.05</td>\n",
       "      <td>1.01</td>\n",
       "      <td>3.26</td>\n",
       "      <td>1190.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>13.77</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.68</td>\n",
       "      <td>17.1</td>\n",
       "      <td>115.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.79</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.68</td>\n",
       "      <td>6.30</td>\n",
       "      <td>1.13</td>\n",
       "      <td>2.93</td>\n",
       "      <td>1375.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>13.74</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.25</td>\n",
       "      <td>16.4</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.62</td>\n",
       "      <td>5.85</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3.20</td>\n",
       "      <td>1060.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>13.56</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.46</td>\n",
       "      <td>20.5</td>\n",
       "      <td>116.0</td>\n",
       "      <td>2.96</td>\n",
       "      <td>2.78</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.45</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.98</td>\n",
       "      <td>3.03</td>\n",
       "      <td>1120.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>14.22</td>\n",
       "      <td>1.70</td>\n",
       "      <td>2.30</td>\n",
       "      <td>16.3</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.26</td>\n",
       "      <td>2.03</td>\n",
       "      <td>6.38</td>\n",
       "      <td>0.94</td>\n",
       "      <td>3.31</td>\n",
       "      <td>970.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>13.29</td>\n",
       "      <td>1.97</td>\n",
       "      <td>2.68</td>\n",
       "      <td>16.8</td>\n",
       "      <td>102.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.23</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.66</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.07</td>\n",
       "      <td>2.84</td>\n",
       "      <td>1270.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>13.72</td>\n",
       "      <td>1.43</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.7</td>\n",
       "      <td>108.0</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0.19</td>\n",
       "      <td>2.04</td>\n",
       "      <td>6.80</td>\n",
       "      <td>0.89</td>\n",
       "      <td>2.87</td>\n",
       "      <td>1285.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>12.37</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.36</td>\n",
       "      <td>10.6</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.82</td>\n",
       "      <td>520.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>12.33</td>\n",
       "      <td>1.10</td>\n",
       "      <td>2.28</td>\n",
       "      <td>16.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.41</td>\n",
       "      <td>3.27</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.67</td>\n",
       "      <td>680.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>12.64</td>\n",
       "      <td>1.36</td>\n",
       "      <td>2.02</td>\n",
       "      <td>16.8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.02</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.62</td>\n",
       "      <td>5.75</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.59</td>\n",
       "      <td>450.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>13.67</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.92</td>\n",
       "      <td>18.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.79</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.73</td>\n",
       "      <td>3.80</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2.46</td>\n",
       "      <td>630.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>12.37</td>\n",
       "      <td>1.13</td>\n",
       "      <td>2.16</td>\n",
       "      <td>19.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.87</td>\n",
       "      <td>4.45</td>\n",
       "      <td>1.22</td>\n",
       "      <td>2.87</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>12.17</td>\n",
       "      <td>1.45</td>\n",
       "      <td>2.53</td>\n",
       "      <td>19.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.03</td>\n",
       "      <td>2.95</td>\n",
       "      <td>1.45</td>\n",
       "      <td>2.23</td>\n",
       "      <td>355.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>12.37</td>\n",
       "      <td>1.21</td>\n",
       "      <td>2.56</td>\n",
       "      <td>18.1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.42</td>\n",
       "      <td>2.65</td>\n",
       "      <td>0.37</td>\n",
       "      <td>2.08</td>\n",
       "      <td>4.60</td>\n",
       "      <td>1.19</td>\n",
       "      <td>2.30</td>\n",
       "      <td>678.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>13.11</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.70</td>\n",
       "      <td>15.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.98</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.26</td>\n",
       "      <td>2.28</td>\n",
       "      <td>5.30</td>\n",
       "      <td>1.12</td>\n",
       "      <td>3.18</td>\n",
       "      <td>502.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>12.37</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.92</td>\n",
       "      <td>19.6</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.04</td>\n",
       "      <td>4.68</td>\n",
       "      <td>1.12</td>\n",
       "      <td>3.48</td>\n",
       "      <td>510.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>13.34</td>\n",
       "      <td>0.94</td>\n",
       "      <td>2.36</td>\n",
       "      <td>17.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2.53</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.42</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.93</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>12.21</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.75</td>\n",
       "      <td>16.8</td>\n",
       "      <td>151.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1.28</td>\n",
       "      <td>3.07</td>\n",
       "      <td>718.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "50    13.05        1.73  2.04               12.4       92.0           2.72   \n",
       "51    13.83        1.65  2.60               17.2       94.0           2.45   \n",
       "52    13.82        1.75  2.42               14.0      111.0           3.88   \n",
       "53    13.77        1.90  2.68               17.1      115.0           3.00   \n",
       "54    13.74        1.67  2.25               16.4      118.0           2.60   \n",
       "55    13.56        1.73  2.46               20.5      116.0           2.96   \n",
       "56    14.22        1.70  2.30               16.3      118.0           3.20   \n",
       "57    13.29        1.97  2.68               16.8      102.0           3.00   \n",
       "58    13.72        1.43  2.50               16.7      108.0           3.40   \n",
       "59    12.37        0.94  1.36               10.6       88.0           1.98   \n",
       "60    12.33        1.10  2.28               16.0      101.0           2.05   \n",
       "61    12.64        1.36  2.02               16.8      100.0           2.02   \n",
       "62    13.67        1.25  1.92               18.0       94.0           2.10   \n",
       "63    12.37        1.13  2.16               19.0       87.0           3.50   \n",
       "64    12.17        1.45  2.53               19.0      104.0           1.89   \n",
       "65    12.37        1.21  2.56               18.1       98.0           2.42   \n",
       "66    13.11        1.01  1.70               15.0       78.0           2.98   \n",
       "67    12.37        1.17  1.92               19.6       78.0           2.11   \n",
       "68    13.34        0.94  2.36               17.0      110.0           2.53   \n",
       "69    12.21        1.19  1.75               16.8      151.0           1.85   \n",
       "\n",
       "    flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "50        3.27                  0.17             2.91             7.20  1.12   \n",
       "51        2.99                  0.22             2.29             5.60  1.24   \n",
       "52        3.74                  0.32             1.87             7.05  1.01   \n",
       "53        2.79                  0.39             1.68             6.30  1.13   \n",
       "54        2.90                  0.21             1.62             5.85  0.92   \n",
       "55        2.78                  0.20             2.45             6.25  0.98   \n",
       "56        3.00                  0.26             2.03             6.38  0.94   \n",
       "57        3.23                  0.31             1.66             6.00  1.07   \n",
       "58        3.67                  0.19             2.04             6.80  0.89   \n",
       "59        0.57                  0.28             0.42             1.95  1.05   \n",
       "60        1.09                  0.63             0.41             3.27  1.25   \n",
       "61        1.41                  0.53             0.62             5.75  0.98   \n",
       "62        1.79                  0.32             0.73             3.80  1.23   \n",
       "63        3.10                  0.19             1.87             4.45  1.22   \n",
       "64        1.75                  0.45             1.03             2.95  1.45   \n",
       "65        2.65                  0.37             2.08             4.60  1.19   \n",
       "66        3.18                  0.26             2.28             5.30  1.12   \n",
       "67        2.00                  0.27             1.04             4.68  1.12   \n",
       "68        1.30                  0.55             0.42             3.17  1.02   \n",
       "69        1.28                  0.14             2.50             2.85  1.28   \n",
       "\n",
       "    od280/od315_of_diluted_wines  proline  target  \n",
       "50                          2.91   1150.0       0  \n",
       "51                          3.37   1265.0       0  \n",
       "52                          3.26   1190.0       0  \n",
       "53                          2.93   1375.0       0  \n",
       "54                          3.20   1060.0       0  \n",
       "55                          3.03   1120.0       0  \n",
       "56                          3.31    970.0       0  \n",
       "57                          2.84   1270.0       0  \n",
       "58                          2.87   1285.0       0  \n",
       "59                          1.82    520.0       1  \n",
       "60                          1.67    680.0       1  \n",
       "61                          1.59    450.0       1  \n",
       "62                          2.46    630.0       1  \n",
       "63                          2.87    420.0       1  \n",
       "64                          2.23    355.0       1  \n",
       "65                          2.30    678.0       1  \n",
       "66                          3.18    502.0       1  \n",
       "67                          3.48    510.0       1  \n",
       "68                          1.93    750.0       1  \n",
       "69                          3.07    718.0       1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'] = wine.target\n",
    "df[50:70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8d45f675",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.3, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a476c058",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB\n",
    "model = GaussianNB()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2e7852c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "59301309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7777777777777778"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mn = MultinomialNB()\n",
    "mn.fit(X_train,y_train)\n",
    "mn.score(X_test,y_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
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 },
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