12_naiveBayes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naïve Bayes Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Naïve Bayes Classifier is one of the simple and most effective Classification algorithms which helps in building the fast machine learning models that can make quick predictions. It is a probabilistic classifier, which means it predicts on the basis of the probability of an object.\n",
    "\n",
    "Naive Bayes algorithm falls under classification of Supervised learning.\n",
    "\n",
    "Bayes theorem, named after Thomas Bayes from the 1700s. The Naive Bayes classifier works on the principle of conditional probability, as given by the Bayes theorem.\n",
    "> Probability = P(A|B) = [ P(B|A) * P(A) ] / P(B)\n",
    "\n",
    "![image](pics/Bayes.jpg)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where is Naive Bayes Used?\n",
    "1. Face Recognition\n",
    "2. Weather Prediction \n",
    "3. Medical Diagnosis\n",
    "4. News Classification \n",
    "5. Classifying objects on the base of its features as its an Apple / Banana"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Advantages of Naive Bayes Classifier \n",
    "1. It is simple and easy to implement\n",
    "2. It doesn’t require as much training data\n",
    "3. It handles both continuous and discrete data\n",
    "4. It is highly scalable with the number of predictors and data points\n",
    "5. It is fast and can be used to make real-time predictions\n",
    "6. It is not sensitive to irrelevant features "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## lets hands on!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phool = sns.load_dataset(\"iris\")\n",
    "phool.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=phool.iloc[: , :-1]\n",
    "y=phool.iloc[: , -1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Junaid\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model = GaussianNB().fit(X,y)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split data into test and train (80/20)%rule\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test =train_test_split(X,y,test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Junaid\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'versicolor', 'setosa', 'virginica',\n",
       "       'versicolor', 'virginica', 'setosa', 'setosa', 'virginica',\n",
       "       'versicolor', 'setosa', 'virginica', 'versicolor', 'versicolor',\n",
       "       'setosa', 'versicolor', 'versicolor', 'setosa', 'setosa',\n",
       "       'versicolor', 'versicolor', 'virginica', 'setosa', 'virginica',\n",
       "       'versicolor', 'setosa', 'setosa', 'versicolor', 'virginica'],\n",
       "      dtype='<U10')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training model on training set\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "model = GaussianNB().fit(X_train,y_train)\n",
    "# making predictions on the testing set\n",
    "prediction= model.predict(X_test)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96.66666666666667"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "score = metrics.accuracy_score(y_test,prediction)*100\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11,  0,  0],\n",
       "       [ 0, 12,  1],\n",
       "       [ 0,  0,  6]], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#confusion metrix\n",
    "from sklearn import metrics\n",
    "cm = metrics.confusion_matrix(y_test,prediction)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'guassian naive bayes accuracy= 9666.666666666668')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/V5Q6byN73/dvclx7k1UiGtwM7Bn5VSNy+5P93RrGXra3rab9e4XsS9H+TVbtTzZrekFFH0ZExRUVIuIdZAH547ZVaFuvkk0Ie1tEDCEbW/wKqrn6HlHzR1nMaK69vgv8JSJ+QTY2cw+y0geszFzUR8QNwJfzb6pzyGZwNx3zdjtwUkRMJMtynUST8WQRcR3wMPBovv9RZOfHPfn6e8km2jxFlj36HNmFgh+gebcDF0XEWcD9ZKXLph+ORbuDrMR9WURcQjbp4zRWLbmvIqX0cP5+jSN7P25ussnXycZH1gN/BuaTZSMOBc5qYwzrDOCKiDg7b/t8si8Nv+1ov1NKb0bErfnrfi9VXDsyZROGzgUujOySQveQfTnYCtg3pfSRPMD6Mdm41JfIsoWnA49XlOCbarN/+bnZkPW+kiwDmMjGRF6VUnqILMP3IHBPRPwvWXZxJ2BmSulSsnPrDeBn+Xs1mOx9b+/4sjbPe7LhHSeRTbY7lyxA3ozs2pVnVGx3PdlQiZ3JxsI1klqeONNu+ZeaTciyVsPJ/h/bhopgPaU0OSIuAr4fEUGW9RxL9mXjexXN/YjsKgLXRsQvyYZz/AC4NOUT8drbVkR8C/hWSqny34uz87Z/RDaMYh+yMZ3HVWxzHVkm9i8R8U2ycabnk405vMa2Cm3rV8D1+fjiq8jGaJ5O9uXlSqQSmdFcS6XsTiCnkF125XqyjMhp+erKf2hPJrtEzC/JxlheReNZswBfBP6Rr7+ULFj8XpNt7stf6w/ADWQTWo7MAwLIsmOfJguq/kg2/u/glNIkmncx2WViTiUbkL4J2aV8SpP/4z+G7LIhN+ev/zGy2ZntcQ1ZZu+mvKxZ2fa9wPvJSpxXkN0V6etkE2uaK1NXepUsI3wuWaZ0Htls0CWr2e/r85+rTLzJy8ZjyUrqN5CdH58kOx8gGzc4lWzW8d/IzqNnWTV7Xtlmu/qXUvoDWRZwG7Lz5nf579Pz9c+TlftnkF1G6DqyLziv5uuXko3rbQjmv0o2GW427dPmeZ+/5/uR/f3Oz9+Dr9Nk+EPKZur+jezv2/Ral2vKcrIxrzeT/R2mAbunlN5ost3XyC5B9U2yL6Ejyc6fFyv6+yrwAbKs9F/IxuxdTvaedKgtsn8nelTulH8+HUdW2v8r2fCSk1JKV1Zss4LsC+ajZBnh35EFswdVjh21rULauiFfvh3ZzPNfkX2R26cdFRdpjfLOQJ1I/i33LGBwWjMzddUF5ONJN0gp7VXrvnRV+RjUV8kygmfXuj+SOrc9zrmt5sHXP887sJT6uaXztVREDCMr0d0JLCKbpHE6cIlBpgAi4l1kl4H6KK2MudXqy8c4vpssazuELFMvSWonA82111KyMuNxZHeOmQxcSDY+R4Ks3DuU7HZ0f651Z7qoDcnGik4DTmxlqIgkqRkGmmuplNJcsvE6UrNSSpvWug9dXT7L13tDS1qjyryOZa05GUiSJEmFWJszmjUfKCtJkrqcmqcTu1NGc20ONNnjnNtq3QWpav8870Auqyv7EqLSmjWmfoLnsbqEMfUTat2FbsXSuSRJkgqxVmc0JUmSuppU4i0ga82MpiRJkgphRlOSJKlE3WkykBlNSZIkFcJAU5IkSYWwdC5JklQmS+eSJElSdcxoSpIklaiurvvc/NCMpiRJkgphoClJkqRCWDqXJEkqUV0PS+eSJElSVcxoSpIklcjJQJIkSVKVDDQlSZJUCEvnkiRJJbJ0LkmSJFXJjKYkSVKJvLyRJEmSVCUDTUmSJBXC0rkkSVKJnAwkSZIkVclAU5IkSYWwdC5JklQiS+eSJElSlcxoSpIklciMpiRJklQlA01JkiQVwtK5JElSibwFpSRJklQlM5qSJEklcjKQJEmSVCUDTUmSJBXC0rkkSVKJLJ1LkiRJVTKjKUmSVCIzmpIkSVKVDDQlSZJUCEvnkiRJJerhnYEkSZKk6pjRlCRJKpGTgSRJkqQqGWhKkiSpEJbOJUmSSmTpXJIkSd1WRFwaEdMi4qmKZYMj4vaIeCH/Oaitdgw0JUmS1NRvgYOaLDsDmJBS2hKYkD9vlaVzSZKkEtV1gutoppTuiYhNmyw+HNgn//1y4C7g9NbaMaMpSZLUzUTE2Ih4qOIxth27rZ9SmgyQ/xze1g5mNCVJkkpUtxak+VJK44BxRb/OWnCokiRJ6gSmRsQGAPnPaW3tYKApSZKk9rgROD7//XjghrZ2sHQuSZJUos5wHc2IuIps4s/QiJgEnAN8H/hjRHwGeA34WFvtGGhKkiSpkZTSMS2s2r8j7RhoSpIklagzZDTXFMdoSpIkqRAGmpIkSSqEpXNJkqQSdYY7A60pZjQlSZJUCDOakiRJJXIykCRJklQlA01JkiQVwtK5JElSiSydS5IkSVUyoylJklQiM5qSJElSlQw0JUmSVAhL55IkSSXyzkCSJElSlcxoSpIklcjJQJIkSVKVDDQlSZJUCEvnkiRJJeoRte5BecxoSpIkqRAGmpIkSSqEpXNJkqQS1Vk6lyRJkqpjRlOSJKlETgaSJEmSqmSgKUmSpEJYOpckSSqRpXNJkiSpSmY0JUmSStSdMpoGmp3YmYdvxx5bDWP2wqUc+8v7ANj3nevzmX23YJOhffncr//Nc2/Oa3bf0VsM5UsHb0NdBDc9Monf3/syAP36rMMFH9uBEQP7MGXOYs7+4+PMX7IcgGP32ozDdhpFfUr8v1ue5YEXZ5ZzoOpWRh74Xkb/9CSiRx3/ueQWnvzB1atsM/rCkxh18GiWL3qLe8f8kJmPvtDqvr0G9WOfq8+m36brM/+Vqdx19PksnbOg1ONS9+O5LFk679RueexNvvL7hxste2naAr5x9aM89ursFverC/jqodvy1d8/zCcvupcPvGsDNh3WF4Bj99yMh16axSd+di8PvTSLT+21OQCbDuvL/ttvwKcuupevXPEwpx32zm51wVmVI+rq2O0XpzD+kDO5brsT2PwT+zFg200abTPq4F3pv8Uort3qOO478Sfs/stT29x3hzOOYfIdj3Dt1scz+Y5H2OGMY0o/NnUvnstSxkCzE3v81dnMW7ys0bJXZyzktZmLWt1v25EDmDRrEW/OXszyFYkJT01mr22GA7DXNsP522NvAPC3x97g/RXLJzw1mWUrEpPnLGbSrEVsO3JAAUel7mzortswf+IbLHh5MvXLlvPSNXey8eHva7TNxofvwcQrxgMw/f5n6TVwPfqMGNzqvht/+H1MvDzbZ+Ll49n48D3KPTB1O57Lak1d1P5R2rEW1XBEbBMRp0fEzyLiwvz3bYt6PbXfsP69mTZ3ydvPp81dwrB+vQEY1LcXMxcsBWDmgqUM7Nsr26dfb6ZW7jNvCcP69y6x1+oO1h05lIWTpr/9fNGk6fQdObTxNhsOZeHrK7dZOGk6644c2uq+vdcfxOIpswBYPGUWvYcPLPAoJM9lqUEhgWZEnA5cDQTwAPBg/vtVEXFGK/uNjYiHIuKhcePGFdE1kf0hmkqk1dlJWqOimfMspSYnWrPnYmrfvlJJPJfVmh5R+0dZipoM9Blgu5RSo7puRPwEeBr4fnM7pZTGAQ0RZrr8nNsK6l73Nm3eEoYPWJmNHD6gNzPmvwXA7IVLGbJeltUcsl4v5izMspvT5y1h/cp9+vdm+vwlSGvSwkkz6Dtq2NvP1x01jEVvNp50tuiNGfTdaOU2ffNt6nqt0+K+S6bOps+IwSyeMos+IwazZNqcYg9E3Z7nspQpqnReD2zYzPIN8nWqoefenMeoweuywcA+9OwR7L/9Btz73DQA7n1+GgfvOBKAg3ccyT8alj83jf2334B1egQbDOzDqMHr8uwbc2t2DOqaZjz4HP23HMl6m46gbp2ebH70vrx+432NtnntxvvY4tgDABg2eluWzl3I4imzWt33tZvuY4vjs322OP4AXmvSprSmeS5LmaIyml8CJkTEC8Dr+bKNgS2Akwt6zW7n3KN2YKdNBzNw3XW47it7c8ldE5m3aBlfPmRbBvbtxY8+uTMvTJnPV654mKH9/oszPrwdp135CCvqs8sT/eTY99CjLrj50Td4efpCAK74x8tc8PF3c9jOI5k6dwnf/OPjALw8fSF3PD2FK0/ekxX1iZ/89VnqreRoDUsr6vn3F3/OAbf+gOhRxwuX/Y05z7zK1iceBsDzF9/MpFvuZ9QhoznyhStYsWgJ/zjhR63uC/Dk969mn2vOZqsTDmbBa9O48+Pn1+wY1T14Lqs13ek6mlHUuI+IqAN2BUaSjUSZBDyYUlrRzibSHpbO1QX887wDuaxu/1p3Q6rKmPoJnsfqEsbUT6h5mHf2g3+oearmgvf+dynvQ2EXbE8p1QP/Lqp9SZKkzqiuG11cshsdqiRJkspkoClJkqRCeK9zSZKkEnWnyUBmNCVJklQIM5qSJEklMqMpSZIkVclAU5IkSYWwdC5JklSiOkvnkiRJUnUMNCVJklQIS+eSJEkl6hE1v9V5acxoSpIkqRBmNCVJkkrkdTQlSZKkKhloSpIkqRCWziVJkkpk6VySJEmqkhlNSZKkEnlnIEmSJKlKBpqSJEkqhKVzSZKkEjkZSJIkSaqSGU1JkqQSmdGUJEmSqmSgKUmSpEJYOpckSSqR19GUJEmSqmRGU5IkqUROBpIkSZKqZKApSZKkQlg6lyRJKpGlc0mSJKlKZjQlSZJKZEZTkiRJqpKBpiRJkgph6VySJKlE3hlIkiRJqpKBpiRJkgph6VySJKlEzjqXJEmSqmRGU5IkqURmNCVJkqQqGWhKkiSpEJbOJUmSSuR1NCVJkqQqmdGUJEkqUV2kWnehNGY0JUmSVAgDTUmSJBXC0rkkSVKJvI6mJEmSVCUzmpIkSSVyMpAkSZJUJQNNSZIkFcLSuSRJUom8M5AkSZJUJTOakiRJJerhZCBJkiSpOgaakiRJKoSlc0mSpBI5GUiSJEmqkoGmJEmSCmHpXJIkqUSd5RaUEfFl4LNAAp4ExqSUlnSkDTOakiRJaiQiRgKnALuklLYHegCf6Gg7ZjQlSZJK1KPzTAbqCfSJiGXAusCbHW3AjKYkSVI3ExFjI+KhisfYyvUppTeAHwOvAZOBuSml8R19HTOakiRJ3UxKaRwwrqX1ETEIOBzYDJgD/CkiPpVS+n1HXsdAU5IkqUSd5DqaHwBeTilNB4iIvwDvAzoUaFo6lyRJUlOvAbtFxLoREcD+wLMdbcSMpiRJUok6w+WNUkr3R8SfgUeA5cCjtFJqb4mBpiRJklaRUjoHOKeaNiydS5IkqRBmNCVJkkrUia6jWTUzmpIkSSqEGU1JkqQSdYbJQGuKGU1JkiQVwkBTkiRJhbB0LkmSVKJOcmegNcKMpiRJkgphRlOSJKlEPZwMJEmSJFXHQFOSJEmFsHQuSZJUIicDSZIkSVWKlNbaAalrbcckSVKnVfN84hOzLql5jLPD4M+U8j6s1aXzy+r2r3UXpKqNqZ/A8UdcUetuSFW5/PpjeWiXD9e6G1LVdnnoxlp3oVuxdC5JkqRCrNUZTUmSpK6mO2X5utOxSpIkqUQGmpIkSSqEpXNJkqQSeQtKSZIkqUpmNCVJkkrknYEkSZKkKhloSpIkqRCWziVJkkpU52QgSZIkqTpmNCVJkkrUw8lAkiRJUnUMNCVJklQIS+eSJEklcjKQJEmSVCUzmpIkSSXyzkCSJElSlQw0JUmSVAhL55IkSSXq4WQgSZIkqTpmNCVJkkrkZCBJkiSpSgaakiRJKoSlc0mSpBJFN8rzdZ8jlSRJUqnMaEqSJJUoovvMBjKjKUmSpEIYaEqSJKkQls4lSZJK5GQgSZIkqUoGmpIkSSqEpXNJkqQSOetckiRJqpIZTUmSpBI5GUiSJEmqkoGmJEmSCmHpXJIkqUSBk4EkSZKkqpjRlCRJKlFE98nzdZ8jlSRJUqkMNCVJklQIS+eSJEklcjKQJEmSVCUzmpIkSSVyMpAkSZJUJQNNSZIkFcLSuSRJUomcDCRJkiRVyYymJElSiaIb5fm6z5FKkiSpVAaakiRJKoSlc0mSpBJFOBlIkiRJqoqBpiRJkgph6VySJKlEzjqXJEmSqmRGU5IkqUTeGUiSJEmqkoGmJEmSCmHpXJIkqUQR3SfP12KgGRHzgdTwNP+Z8t9TSql/wX2TJElSJ9ZioJlS6ldmRyRJkroDJwM1ERF7RsSY/PehEbFZsd2SJElSZ9dmoBkR5wCnA2fmi3oBvy+yU5IkSer82jMZ6CPATsAjACmlNyPCsrokSdJq6E6TgdpzpEtTSol8YlBE9C22S5IkSeoK2pPR/GNEXAwMjIjPAScAvy62W5IkSV1Td7rXeZuBZkrpxxHxQWAesBXwrZTS7YX3TJIkSZ1aey/Y/iTQh6x8/mRx3ZEkSVJX0Z5Z558FHgA+ChwF/DsiTii6Y5IkSV1RrAX/laU9Gc2vATullGYCRMQQ4D7g0iI7JkmSpM6tPYHmJGB+xfP5wOvFdEeSJKlr606XN2rtXudfyX99A7g/Im4gG6N5OFkpXZIkSWpRaxnNhouyv5g/GtxQXHckSZLUVbQYaKaUziuzI5IkSd1BmZNxaq3NMZoRMQz4OrAd0LtheUppvwL7JUmSpE6uPZOBrgSuAQ4DPg8cD0wvslOSJEldVXeaDNSeIx2SUroEWJZSujuldAKwW8H9kiRJUifXnozmsvzn5Ig4FHgTGFVclyRJktQVtCfQ/HZEDAC+Cvwc6A98udBeSZIkdVFOBqqQUro5/3UusG+x3ZEkSVJX0doF239OdoH2ZqWUTimkR5IkSeoSWstoPlRaL7RGjDzwvYz+6UlEjzr+c8ktPPmDq1fZZvSFJzHq4NEsX/QW9475ITMffaHVfXsN6sc+V59Nv03XZ/4rU7nr6PNZOmdBqcelru8zJ+/OjruMYt7cJZx16k0AHH38zuz43lGsWF7PtCnz+c3P72PRwmWr7PuunTbkk5/dhbq64O7bJ/LXvzwNQN/1evGF097P0OF9mTFtIRf96B4WLVwKwGFHbs/7P/AO6usTv//1gzz12OTyDlbdwqbfOoUBe+7C8tlzefroLza7zUanfY4Be+xC/ZK3eOXcn7Lo+ZcA6L/7zmx82mehrgczrh/PlMuvBaBH//V4x/e+Tq8NhrN08jRePOMHrJi/sLRj0poT7ZqL3TW0eKQppctbe5TZSbUt6urY7RenMP6QM7luuxPY/BP7MWDbTRptM+rgXem/xSiu3eo47jvxJ+z+y1Pb3HeHM45h8h2PcO3WxzP5jkfY4YxjSj82dX333vEiPz5/QqNlTz8+mbNOuYlvfulmprw5j8OO3H6V/aIuOO7EXfnf8+/gzC/exG57bcqGowYAcOiR2/PME5M5/Qs38MwTkznsyO0A2HDUAEbvuQnf+OJN/Pi8Ozj+86OJuu4zXkrlmHHTBF744rktrh+wx3vovdGGPPWRE3n1Oxex8Zn/k62oq2Pj00/kP6ecx9MfO4nBB76f3pttBMAGnz6KeQ88zlMf/TzzHnicEZ8+qoQjUXcWEQMj4s8R8VxEPBsRu3e0je4TUndxQ3fdhvkT32DBy5OpX7acl665k40Pf1+jbTY+fA8mXjEegOn3P0uvgevRZ8TgVvfd+MPvY+Ll2T4TLx/PxofvUe6BqVt4/plpLFzwVqNlTz02mfr6bPTOi8/PYNCQvqvst/mWQ5g6eT7Tpy5gxfJ67r/3VXYenf2jvPOuo7j3zixDdO+dL61cPnoj7r/3VZYvr2fGtAVMnTyfzbccUuThqRta8OjTLJ/XcvVn4N6jmXnLnQAsfOp5evbryzpDBtF3uy156/XJLH1jKmn5cmaN/wcD9x6d77MrM2++A4CZN9/BoH1GF38gKkRE1PzRThcCt6aUtgHeDTzb0WM10Owi1h05lIWTVl5Hf9Gk6fQdObTxNhsOZeHrK7dZOGk6644c2uq+vdcfxOIpswBYPGUWvYcPLPAopObt9YEtePKRN1ZZPmjwusyasbJ0OGvmQgYN7gNA/4F9mDt7MQBzZy+m/4De+T59muyziEGD1y2y+9Iq1hk2hKVTVn7uLp06k3WGD6HX8CEsnTpj5fJpM+g1PPsi1HPwQJbNnA3Aspmz6TloYKl9VvcSEf2B9wOXAKSUlqaU5nS0HQPNLqK5LycpNZnL1dwXmJTat69UIx86anvqV9Rz390vr7Ku2XO3rQbb/01eKk4Ln8fNrfDzWDWyOdmdIC+LiEcj4jcRsWppqQ2lzzqPiDEppctaWDcWGAtw8cUXs87qvEA3tXDSDPqOGvb283VHDWPRmzMbbbPojRn03WjlNn3zbep6rdPivkumzqbPiMEsnjKLPiMGs2TanGIPRKqwx76bs+Muo/jBt25vdv2smYsYPHTl597gIX2ZMyvLYs6bs5gBg7Ks5oBBfZg3dwkAs1fZZ11mz1pU4FFIq1o2bSa9RgyDx7NKZK/1h7Bs+ixinZ70Wn9lNarX8KEsm55VlZbPmsM6QwaxbOZs1hkyiOWz59Si61oT1oLvDlG3MubKjUspjat43hPYGfhiSun+iLgQOAM4uyOv01pG8yHg4VYeq+u8llaklMallHZJKe0yduzYljZTM2Y8+Bz9txzJepuOoG6dnmx+9L68fuN9jbZ57cb72OLYAwAYNnpbls5dyOIps1rd97Wb7mOL47N9tjj+AF5r0qZUlHfttCGHfnQ7fvrdO1m6dEWz27z8wkzW36AfQ4evR4+edYzecxMefeB1AB59YBJ77rs5AHvuuzmPPDApX/46o/fchJ496xg6fD3W36AfL70ws9n2paLMufsBhhySXZq67/Zbs2LBIpbNnM3CZ16g90Yb0mvD9YmePRl8wF7Muef+lfscth8AQw7bjzl3P1Cz/qvzq4y58se4JptMAiallO7Pn/+ZLPDskCgiJR8RT7S0CtgqpfRf7WgmXVa3/xrsVdc36uBd2fX/ZZcoeuGyv/HEd//A1iceBsDzF2fX3d/tF6cw8sD3smLREv5xwo+Y+fB/WtwX4L8G92efa85mvY2Hs+C1adz58fNZOnt+bQ6wkxpTP4Hjj7ii1t1Yq/3PV/Zkm+3XZ73+vZk3ZzHXXf0Ehx25HT3X6cGC+dkkoRefn8Hlv7qfgYP6cMLJu/OTC7JJETu8Z0M+ecJ7qesR3PP3idz056cA6NuvFyd97f0MGdqXmTMWctEP72HhguzyRh86anve/4EtWLGinj9c8hBPPPJmbQ68E7n8+mN5aJcP17obncZm3zmNfu/Znp4D+7N85hzeHHcV0bMHANOvvRWAjb9+Iv3ft3N2eaPzfsaiZycC2Yz0jb7yWehRx8wb/87kS/8EQI8B/bLLG40YxtIp07PLG7Uy4UjN2+WhG2s/fqZ+Qu1zmnX7t/k+RMQ/gM+mlJ6PiHOBvimlr3XkZdoMNCNiGHA68E6gd8PylNJ+rewzFTgQmN10FXBfSmnDdvTNQFNdgoGmugIDTXUVBpq59gWaOwK/AXoBLwFjUkpNY7tWtede51cC1wCHAp8HjicbHNqam4H1UkqPNV0REXd1pIOSJEkqXx7H7VJNG+0JNIeklC6JiFNTSncDd0fE3W107DOtrPvvjnZSkiSpy0j1te5BadoTaDbc821yRBwKvAmMKq5LkiRJ6graE2h+OyIGAF8Ffg70B75caK8kSZK6KjOaK6WUbs5/nQvsW2x3JEmS1FW0GWhGxGU0c2nRlNIJhfRIkiRJXUJ7Suc3V/zeG/gI2ThNSZIkdZSl85VSStdWPo+Iq4C/F9YjSZIkdQntyWg2tSWw8ZruiCRJUrdQb0bzbRExn8ZjNKeQ3SlIkiRJalF7Suf9yuiIJEmSupa6tjaIiAntWSZJkqR2SPW1f5SkxYxmRPQG1gWGRsQgoOHm6/2BDUvomyRJkjqx1krnJwJfIgsqH2ZloDkPuKjYbkmSJHVRXt4IUkoXAhdGxBdTSj8vsU+SJEnqAtocownUR8TAhicRMSgivlBclyRJktQVtCfQ/FxKaU7Dk5TSbOBzhfVIkiSpK6v1RKASS/ftCTTrIqJhfCYR0QPoVVyXJEmS1BW0585AtwF/jIhfkV24/fPArYX2SpIkSZ1eewLN04GxwP+QzTwfD/y6yE5JkiR1Wd3oFpRtls5TSvUppV+llI5KKR0JPA04C12SJEmtak9Gk4jYETgGOBp4GfhLgX2SJEnquryOJkTEVsAnyALMmcA1QKSU9i2pb5IkSerEWstoPgf8A/hQSmkiQER8uZReSZIkqdNrLdA8kiyjeWdE3ApczcrbUEqSJGl1dKPSeYuTgVJK16WUjga2Ae4CvgysHxH/FxEHlNQ/SZIkdVLtmXW+MKV0ZUrpMGAU8BhwRtEdkyRJ6pJqfVegtezOQG9LKc1KKV2cUtqvqA5JkiSpa+hQoClJkiS1V7uuoylJkqQ1I6UVte5CabO7zWhKkiSpEGY0JUmSyuS9ziVJkqTqGGhKkiSpEJbOJUmSyuSdgSRJkqTqmNGUJEkqkxlNSZIkqToGmpIkSSqEpXNJkqQyWTqXJEmSqmOgKUmSpEJYOpckSSqTpXNJkiSpOmY0JUmSylRvRlOSJEmqioGmJEmSCmHpXJIkqUxOBpIkSZKqY0ZTkiSpTGY0JUmSpOoYaEqSJKkQls4lSZLKZOlckiRJqo4ZTUmSpDJ5ZyBJkiSpOgaakiRJKoSlc0mSpDI5GUiSJEmqjhlNSZKkMpnRlCRJkqpjoClJkqRCWDqXJEkqk9fRlCRJkqpjRlOSJKlM9anWPSiNGU1JkiQVwkBTkiRJhbB0LkmSVCYnA0mSJEnVMdCUJElSISydS5IklcnSuSRJklQdM5qSJEll8jqakiRJUnUMNCVJklQIS+eSJEllcjKQJEmSVB0zmpIkSWUyoylJkiRVx0BTkiRJhbB0LkmSVCavoylJkiRVx4ymJElSmZwMJEmSJFXHQFOSJEmFiJTW2gGpa23HJElSpxW17kB6+Js1j3HiPd8u5X1Yq8doXla3f627IFVtTP0Ez2V1emPqJwB31rob0hqwb6070K2s1YGmJElSl+NkIEmSJKk6BpqSJEkqhKVzSZKkMlk6lyRJkqpjRlOSJKlEa8OlJcu6xpMZTUmSJBXCQFOSJEmFsHQuSZJUJicDSZIkSdUx0JQkSVIhLJ1LkiSVydK5JEmSVB0zmpIkSWWqr/11NMtiRlOSJEmriIgeEfFoRNy8um0YaEqSJKk5pwLPVtOAgaYkSVKZ6utr/2hDRIwCDgV+U82hGmhKkiR1MxExNiIeqniMbbLJT4GvA1VNkXcykCRJUpnWgssbpZTGAeOaWxcRhwHTUkoPR8Q+1byOGU1JkiRV2gP4cES8AlwN7BcRv1+dhgw0JUmS9LaU0pkppVEppU2BTwB3pJQ+tTptWTqXJEkqUze6jqaBpiRJkpqVUroLuGt19zfQlCRJKtNaMBmoLI7RlCRJUiEMNCVJklQIS+eSJEllsnQuSZIkVceMpiRJUpm60eWNzGhKkiSpEAaakiRJKoSlc0mSpDI5GUiSJEmqjhlNSZKkMpnRlCRJkqpjoClJkqRCWDqXJEkqk9fRlCRJkqpjoClJkqRCWDqXJEkqk7POJUmSpOqY0ZQkSSpRWuFkIEmSJKkqBpqSJEkqhKVzSZKkMnkdTUmSJKk6ZjQlSZLK5GQgSZIkqToGmpIkSSqEpXNJkqQSJScDSZIkSdUxoylJklQmJwNJkiRJ1THQlCRJUiEsnUuSJJVpRX2te1AaM5qSJEkqhBlNSZKkEnl5I0mSJKlKBpqSJEkqhKVzSZKkMnkdTUmSJKk6BpqSJEkqhKVzSZKkMjnrXJIkSaqOGU1JkqQSJScDSZIkSdUx0JQkSVIhLJ1LkiSVqb6+1j0ojRlNSZIkFcKMpiRJUpmcDCRJkiRVx0BTkiRJhbB0LkmSVKLknYEkSZKk6pjRlCRJKpOTgSRJkqTqGGhKkiSpEJbOJUmSymTpXJIkSaqOGU1JkqQSeXkjSZIkqUoGmpIkSSqEpXNJkqQyraivdQ9KY0azCxl54Hv56LO/5cj//I53nf6JZrcZfeFJHPmf33H4Y79myE5btrlvr0H9OOC2H3Lk85dzwG0/pNfA9Qo/DnVvnsfqKubNW8Qpp1zMQQedw8EHn8ujj77UaH1KiW9/+xo++MGz+dCHLuDpp197e9099zzNgQeewwc/eDbjxt369vI5cxYyZsxPOeCAsxkz5qfMnbuwtOORVoeBZhcRdXXs9otTGH/ImVy33Qls/on9GLDtJo22GXXwrvTfYhTXbnUc9534E3b/5alt7rvDGccw+Y5HuHbr45l8xyPscMYxpR+bug/PY3Ul3/nOH9lrr+249dbzuOGGb/KOd4xotP6ee57ilVemMX78+VxwwSc599w/ALBiRT3nn38Vv/nNyfz1r+dw880PMnHimwCMG3cru+++DePHX8Duu2/DuHG3lX5cql6qTzV/lMVAs4sYuus2zJ/4Bgtenkz9suW8dM2dbHz4+xpts/HhezDxivEATL//WXoNXI8+Iwa3uu/GH34fEy/P9pl4+Xg2PnyPcg9M3YrnsbqKBQsW8+CDL3DUUdm51qtXT/r3X7fRNhMmPMERR+xGRLDjjpszb95ipk2byxNPvMImmwxno42G0atXTw499L1MmPBExT67A3DEEbvz978/Xu6BSR1UWKAZEdtExP4RsV6T5QcV9Zrd2bojh7Jw0vS3ny+aNJ2+I4c23mbDoSx8feU2CydNZ92RQ1vdt/f6g1g8ZRYAi6fMovfwgQUehbo7z2N1Fa+/PoPBg9fjzDMv54gjvsNZZ13BokVvNdpm6tQ5jBgx6O3nI0YMZOrUOUydOrvR8vXXH8jUqbMBmDlzHsOHDwBg+PABzJo1v4SjkVZfIYFmRJwC3AB8EXgqIg6vWP3dVvYbGxEPRcRD48aNK6JrXVbEqstSapIab2YbUmrfvlIJPI/VVSxfXs8zz7zOMcfszfXXn0WfPr1WKXM3d35GQHOnbTR3gqvzWpFq/yhJURnNzwHvSSkdAewDnB0Rp+brWvy/JaU0LqW0S0ppl7FjxxbUta5p4aQZ9B017O3n644axqI3ZzbaZtEbM+i70cpt+ubbtLbvkqmz6TNiMAB9RgxmybQ5BR6FujvPY3UVI0YMZMSIgbz73ZsBcNBBO/PMM6812WYQU6bMfvv5lClzGD584CrLp07NlgMMGdKfadPmAjBt2lwGD+5X8JFI1Skq0OyRUloAkFJ6hSzYPDgifkIrgaZW34wHn6P/liNZb9MR1K3Tk82P3pfXb7yv0Tav3XgfWxx7AADDRm/L0rkLWTxlVqv7vnbTfWxxfLbPFscfwGtN2pTWJM9jdRXDhg1gxIjBvPTSFAD+9a/neMc7Nmi0zX777cD11/+blBKPPfYS/fr1ZvjwAbzrXZvwyivTeP31GSxdupy//vVB9ttvh4p9/gXA9df/i/3336HcA5M6qKjraE6JiB1TSo8BpJQWRMRhwKXAuwp6zW4trajn31/8OQfc+gOiRx0vXPY35jzzKlufeBgAz198M5NuuZ9Rh4zmyBeuYMWiJfzjhB+1ui/Ak9+/mn2uOZutTjiYBa9N486Pn1+zY1TX53msruTss4/mtNMuZdmyFWy00VC+973juOqqewA45pj3s/fe23P33U/xwQ+eTZ8+vfjud48HoGfPHnzrW0fz2c/+jBUr6jnyyPex5ZYbAjB27IF86Uu/5s9//icbbDCYCy+0+tcpdaNbUEYRY5giYhSwPKU0pZl1e6SU/tmOZtJldfuv8b5JZRtTPwHPZXV2Y+onAHfWuhvSGrBvzSuri886pOaRZp/v3FLK+1BIRjOlNKmVde0JMiVJkrqkVOJknFrzOpqSJEkqhIGmJEmSClHUZCBJkiQ1pxtNBjKjKUmSpEKY0ZQkSSrTivpa96A0ZjQlSZJUCANNSZIkFcLSuSRJUomSk4EkSZKk6pjRlCRJKpN3BpIkSZKqY6ApSZKkQlg6lyRJKlHqPpfRNKMpSZKkYpjRlCRJKlGqj1p3oTRmNCVJklQIA01JkiQVwtK5JElSieqdDCRJkiRVx4ymJElSiVJyMpAkSZJUFQNNSZIkFcLSuSRJUom8M5AkSZJUJQNNSZIkFcLSuSRJUom8BaUkSZJUJTOakiRJJfLOQJIkSVKVDDQlSZJUCEvnkiRJJeoMk4EiYiPgd8AIoB4Yl1K6sKPtGGhKkiSpqeXAV1NKj0REP+DhiLg9pfRMRxox0JQkSSpRZ7gzUEppMjA5/31+RDwLjAQ6FGg6RlOSJKmbiYixEfFQxWNsK9tuCuwE3N/R1zGjKUmS1M2klMYB49raLiLWA64FvpRSmtfR1zHQlCRJKlFKa/9kIICIWIcsyLwypfSX1WnD0rkkSZIaiYgALgGeTSn9ZHXbMaMpSZJUos4wGQjYAzgWeDIiHsuXfSOldEtHGjHQlCRJUiMppXuBqmv8ls4lSZJUCDOakiRJJarvBHcGWlPMaEqSJKkQZjQlSZJK1EkmA60RZjQlSZJUCANNSZIkFcLSuSRJUomSk4EkSZKk6hhoSpIkqRCWziVJkkrkrHNJkiSpSmY0JUmSSpSSk4EkSZKkqhhoSpIkqRCWziVJkkpU72QgSZIkqTpmNCVJkkrk5Y0kSZKkKhloSpIkqRCWziVJkkqU6r2OpiRJklQVM5qSJEklcjKQJEmSVCUDTUmSJBXC0rkkSVKJ6p0MJEmSJFXHjKYkSVKJnAwkSZIkVclAU5IkSYWwdC5JklSilJwMJEmSJFXFjKYkSVKJnAwkSZIkVclAU5IkSYWwdC5JklSi5J2BJEmSpOoYaEqSJKkQls4lSZJKVO+sc0mSJKk6ZjQlSZJKVL8i1boLpTGjKUmSpEIYaEqSJKkQls4lSZJK5GQgSZIkqUqRUvcZkKrGImJsSmlcrfshVctzWV2F53L38MjoD9c8+Nr5/htLuT2RGc3ubWytOyCtIZ7L6io8l9WlGGhKkiSpEE4GkiRJKlH9ilr3oDxmNLs3xwGpq/BcVlfhuawuxYxmN+aAc3UVnsvqKjyXu4f6+prPBSqNGU1JkiQVwkBTkiRJhTDQ7KYi4qCIeD4iJkbEGbXuj7Q6IuLSiJgWEU/Vui/S6oqIjSLizoh4NiKejohTa90nFat+Re0fZTHQ7IYiogdwEXAw8E7gmIh4Z217Ja2W3wIH1boTUpWWA19NKW0L7Aac5GeyugonA3VPuwITU0ovAUTE1cDhwDM17ZXUQSmleyJi01r3Q6pGSmkyMDn/fX5EPAuMxM/kLsvJQOrqRgKvVzyflC+TJNVQ/sVpJ+D+GndFWiMMNLun5u5v2n2+XknSWigi1gOuBb6UUppX6/5Ia4Kl8+5pErBRxfNRwJs16oskdXsRsQ5ZkHllSukvte6PilVfX+selMeMZvf0ILBlRGwWEb2ATwA31rhPktQtRUQAlwDPppR+Uuv+SGuSGc1uKKW0PCJOBm4DegCXppSernG3pA6LiKuAfYChETEJOCeldElteyV12B7AscCTEfFYvuwbKaVbatclFal+RfcZrWag2U3lH2B+iKlTSykdU+s+SNVKKd1L82PnpU7P0rkkSZIKYUZTkiSpRCucDCRJkiRVx0BTkiRJhbB0LkmSVKLuNOvcjKYkSZIKYUZTkiSpRN4ZSJIkSaqSgaYkSZIKYelckiSpRPX1TgaSJEmSqmJGU5IkqUT1K2rdg/KY0ZQkSVIhDDQlSZJUCEvnkiRJJXIykCRJklQlM5qSJEklWuGdgSRJkqTqGGhKkiSpEJbOJUmSSlS/wslAkiRJUlXMaEqSJJWo3slAkiRJUnUMNCVJklQIS+eSJEklcjKQJEmSVCUDTUmSJBXC0rkkSVKJnHUuSZIkVcmMpiRJUonq650MJEmSJFXFQFOSJEmFsHQuSZJUohUrat2D8pjRlCRJUiHMaEqSJJXIyUCSJElSlQw0JUmSVAhL55IkSSWqdzKQJEmSVB0zmpIkSSXqLJOBIuIg4EKgB/CblNL3O9qGGU1JkiQ1EhE9gIuAg4F3AsdExDs72o6BpiRJkpraFZiYUnoppbQUuBo4vKONWDqXJEkq0Zj6CVHrPkTEWGBsxaJxKaVxFc9HAq9XPJ8EjO7o6xhoSpIkdTN5UDmulU2aC4Y7PLjU0rkkSZKamgRsVPF8FPBmRxsx0JQkSVJTDwJbRsRmEdEL+ARwY0cbsXQuSZKkRlJKyyPiZOA2sssbXZpSerqj7URKneNaTpIkSepcLJ1LkiSpEAaakiRJKoSBpiRJkgphoClJkqRCGGhKkiSpEAaakiRJKoSBpiRJkgrx/wF+/Q7QDasKHwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "sns.heatmap(cm,annot=True,fmt=\".3f\",linewidths=.5,square=True,cmap = \"Spectral\")\n",
    "plt.ylabel('Actual label')\n",
    "all_sample_title= \"guassian naive bayes accuracy= {0}\".format(score*100)\n",
    "plt.title (all_sample_title,size=15)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "db9da9d256f457015986a8acd5c7819a5914eb9a575ecff51b37f60a7d912a81"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}