Naive-Bayes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "44596dcf",
   "metadata": {},
   "source": [
    "Naive bayes theorm uses bayes theorm for conditional probability with a naive assumption that the features are not correlated to each other and tries to find conditional probability of target variable given the probabilities of features. We will use titanic survival dataset here and using naive bayes classifier find out the survival probability of titanic travellers. We will use sklearn library and python for GaussianNB classifier to train our model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bde38147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('titanic.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7ab1b953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9d3766ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age     Fare\n",
       "0         0       3    male  22.0   7.2500\n",
       "1         1       1  female  38.0  71.2833\n",
       "2         1       3  female  26.0   7.9250\n",
       "3         1       1  female  35.0  53.1000\n",
       "4         0       3    male  35.0   8.0500"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['PassengerId', 'Name', 'SibSp',\n",
    "       'Parch', 'Ticket', 'Cabin', 'Embarked'],axis='columns',inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "34642438",
   "metadata": {},
   "outputs": [],
   "source": [
    "target=df.Survived\n",
    "inputs=df.drop('Survived',axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2a31507f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex   Age     Fare\n",
       "0       3    1  22.0   7.2500\n",
       "1       1    2  38.0  71.2833\n",
       "2       3    2  26.0   7.9250\n",
       "3       1    2  35.0  53.1000\n",
       "4       3    1  35.0   8.0500"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ML doesnt handle any string data so \n",
    "inputs.Sex=inputs.Sex.replace({'male':1,\n",
    "               'female':2})\n",
    "#inputs.Sex=inputs.Sex.map({'male':1,\n",
    " #              'female':2})\n",
    "inputs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4cf22fa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass      0\n",
       "Sex         0\n",
       "Age       177\n",
       "Fare        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#inputs.columns[inputs.isna().any()]\n",
    "inputs.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3da32c7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of      Pclass  Sex        Age     Fare\n",
       "0         3    1  22.000000   7.2500\n",
       "1         1    2  38.000000  71.2833\n",
       "2         3    2  26.000000   7.9250\n",
       "3         1    2  35.000000  53.1000\n",
       "4         3    1  35.000000   8.0500\n",
       "..      ...  ...        ...      ...\n",
       "886       2    1  27.000000  13.0000\n",
       "887       1    2  19.000000  30.0000\n",
       "888       3    2  29.699118  23.4500\n",
       "889       1    1  26.000000  30.0000\n",
       "890       3    1  32.000000   7.7500\n",
       "\n",
       "[891 rows x 4 columns]>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f6a1bdd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex   Age     Fare\n",
       "0       3    1  22.0   7.2500\n",
       "1       1    2  38.0  71.2833\n",
       "2       3    2  26.0   7.9250\n",
       "3       1    2  35.0  53.1000\n",
       "4       3    1  35.0   8.0500"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs.Age=inputs.Age.fillna(inputs.Age.mean())\n",
    "inputs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a4d524d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(inputs,target,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bed62e8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(712, 4)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "13a9aea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "365b5c46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "clf=GaussianNB()\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "85ec4088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7597765363128491"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6a49105e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>29.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>26.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>29.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>7.7750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>7.7750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex        Age     Fare\n",
       "839       1    1  29.699118  29.7000\n",
       "53        2    2  29.000000  26.0000\n",
       "837       3    1  29.699118   8.0500\n",
       "323       2    2  22.000000  29.0000\n",
       "149       2    1  42.000000  13.0000\n",
       "231       3    1  29.000000   7.7750\n",
       "379       3    1  19.000000   7.7750\n",
       "29        3    1  29.699118   7.8958\n",
       "38        3    2  18.000000  18.0000\n",
       "105       3    1  28.000000   7.8958"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "40b66637",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "839    1\n",
       "53     1\n",
       "837    0\n",
       "323    1\n",
       "149    0\n",
       "231    0\n",
       "379    0\n",
       "29     0\n",
       "38     0\n",
       "105    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "07d8de50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 1, 0, 0, 0, 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(X_test[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e2c21a06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.79045169, 0.20954831],\n",
       "       [0.23559349, 0.76440651],\n",
       "       [0.9652563 , 0.0347437 ],\n",
       "       [0.21012801, 0.78987199],\n",
       "       [0.9330813 , 0.0669187 ],\n",
       "       [0.96496023, 0.03503977],\n",
       "       [0.95808772, 0.04191228],\n",
       "       [0.9652399 , 0.0347601 ],\n",
       "       [0.35190109, 0.64809891],\n",
       "       [0.96454953, 0.03545047]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict_proba(X_test[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fb45b80",
   "metadata": {},
   "source": [
    "**Now, we will build email spam classifier using naive bayes algorithm. We will use sklearn CountVectorizer to convert email text into a matrix of numbers and then use sklearn MultinomialNB classifier to train our model.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3ec1f2d4",
   "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>Category</th>\n",
       "      <th>Message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Category                                            Message\n",
       "0      ham  Go until jurong point, crazy.. Available only ...\n",
       "1      ham                      Ok lar... Joking wif u oni...\n",
       "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3      ham  U dun say so early hor... U c already then say...\n",
       "4      ham  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('spam.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cd87f07d",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">Message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ham</th>\n",
       "      <td>4825</td>\n",
       "      <td>4516</td>\n",
       "      <td>Sorry, I'll call later</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spam</th>\n",
       "      <td>747</td>\n",
       "      <td>641</td>\n",
       "      <td>Please call our customer service representativ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Message                                                            \\\n",
       "           count unique                                                top   \n",
       "Category                                                                     \n",
       "ham         4825   4516                             Sorry, I'll call later   \n",
       "spam         747    641  Please call our customer service representativ...   \n",
       "\n",
       "               \n",
       "         freq  \n",
       "Category       \n",
       "ham        30  \n",
       "spam        4  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Category').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "53bb6793",
   "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>Category</th>\n",
       "      <th>Message</th>\n",
       "      <th>spam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Category                                            Message  spam\n",
       "0      ham  Go until jurong point, crazy.. Available only ...     0\n",
       "1      ham                      Ok lar... Joking wif u oni...     0\n",
       "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...     1\n",
       "3      ham  U dun say so early hor... U c already then say...     0\n",
       "4      ham  Nah I don't think he goes to usf, he lives aro...     0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['spam']=df.Category.map({\n",
    "    'ham':0,\n",
    "    'spam':1\n",
    "})\n",
    "df.head()\n",
    "\n",
    "# df['spam']=df['Category'].apply(lambda x: 1 if x=='spam' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ed599c44",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(df.Message,df.spam,test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3b758773",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "v=CountVectorizer()\n",
    "X_train_count=v.fit_transform(X_train.values) # emails text converted into counts or number frequency \n",
    "X_train_count.toarray()[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eca57054",
   "metadata": {},
   "source": [
    "- **We use Bernoulli Naive bayes when all our features are binary**\n",
    "- **Multinomial NB when we have discrete data** (like movie rating 1-5 with each frequency of occurence, just like text word counts)\n",
    "- **Gaussian NB when all of our features are continuous**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "15f727b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "clf=MultinomialNB()\n",
    "clf.fit(X_train_count,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "94e31fb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emails = [\n",
    "    'Hey mohan, can we get together to watch footbal game tomorrow?',\n",
    "    'Upto 20% discount on parking, exclusive offer just for you. Dont miss this reward!'\n",
    "]\n",
    "emails_count = v.transform(emails)\n",
    "clf.predict(emails_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "59ac2519",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9885139985642498"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating the score by first converting X_test into counts\n",
    "X_test_count=v.transform(X_test)\n",
    "clf.score(X_test_count,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883d0d17",
   "metadata": {},
   "source": [
    "**Sklearn pipeline allows us to handle pre processing transformations easily with its convenient api.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bc8b50d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "clfP=Pipeline([\n",
    "    ('vectorizer',CountVectorizer()),\n",
    "    ('nb',MultinomialNB())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a1d8dab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('vectorizer', CountVectorizer()), ('nb', MultinomialNB())])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clfP.fit(X_train,y_train)# here no need to change in count vector,becoz we already using in pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e0be1f69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9885139985642498"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clfP.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "59f36360",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clfP.predict(emails)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab5d5ab6",
   "metadata": {},
   "source": [
    "### Naive Bayes: Exercise\n",
    "Use wine dataset from sklearn.datasets to classify wines into 3 categories. Load the dataset and split it into test and train. After that train the model using Gaussian and Multinominal classifier and post which model performs better. Use the trained model to perform some predictions on test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a86b1734",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "wine = datasets.load_wine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b9ae0a77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'frame', 'target', 'target_names']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "36865d83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.423e+01, 1.710e+00, 2.430e+00, 1.560e+01, 1.270e+02, 2.800e+00,\n",
       "        3.060e+00, 2.800e-01, 2.290e+00, 5.640e+00, 1.040e+00, 3.920e+00,\n",
       "        1.065e+03],\n",
       "       [1.320e+01, 1.780e+00, 2.140e+00, 1.120e+01, 1.000e+02, 2.650e+00,\n",
       "        2.760e+00, 2.600e-01, 1.280e+00, 4.380e+00, 1.050e+00, 3.400e+00,\n",
       "        1.050e+03]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.data[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fff0e0d0",
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "01d3d5f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['class_0', 'class_1', 'class_2'], dtype='<U7')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b3c6f647",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 6,
     "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": 7,
   "id": "56ddfdfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'] = wine.target\n",
    "df[50:70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c3958360",
   "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": 9,
   "id": "ca98623f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "id": "75c92d7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cd17fe81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7777777777777778"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mn = MultinomialNB()\n",
    "mn.fit(X_train,y_train)\n",
    "mn.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afdb1b59",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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