Naive Bayes Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"Social_Network_Ads.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data.iloc[:,[2,3]].values\n",
    "y=data.iloc[:,4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_x=StandardScaler()\n",
    "x_train=sc_x.fit_transform(x_train)\n",
    "x_test=sc_x.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "classifier = GaussianNB()\n",
    "classifier.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1,\n",
       "       1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[64  4]\n",
      " [ 5 27]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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qQj749XVlACo5lsvyS1xj93PDm89njXsYIdeSs91CSiSh6MIqmSyutBtOURYYMnxAqPGss2vdLraN3Maw9mGZNQKqyraubexat6vviwvUuQHw4k184L41wJqy0W/t/0Z2bxlYsVuwnUJtSELRhVEyWV1pN4Ki9DKsO7bsYeiItoprd2ypzzje8zc/D+fAoNGDsps7mXcM1fM3By8V0YAGwJsvvvq3irF5uRnmPqoRSSi6MEomqyvteleUfoZ11SOvMmnq/mWfeU93nmWLOtMSNRK923tZ/eNKXVHvNI0B8KLSfeQXUxAGDh/IqGNH1Uq0hiMJRbdsUWeZ8gF/JRPWANXKXRTmHrKIn2EdM2UYSxe+lDmXm1FOUxuASipjCje8eTwAa5aMKzMMA4cPMoMQgiQUXVGZBFEyYQxQLd1FYe4hi1QzrKsf31pxH2nHYdJ+/6xhBqAPnHgClMYUvjHgZLq377B4QgiSUnReSsaLMAao1u6ioPeQRbJqWL1I+/2ziBmAfnDpnr9UjHnHE4TxM8bXRKZ6IE1FF8YAxRGvaJaVZpYNa9beP4uYAYiJctfRo8B25uXmlhkFO7SWLkENUNR4Rb2tNCd0LuW4ZxcwpGsjO9pHsmzibFZ3TA/02lob1iik/f5ZxAxAIhwLlBuFG9483uIIdULUeEU9rTQndC5l+orrac13AzC0ayPTV1wPEMoI1MKwRiXt988iqRkAERkD/BwYhXN2bb6qXpmWPElTeTbh/oodAlgcIQtEjVckudKM27V03LML9ir/Iq35bo57dkFgAxCUtDOe0n7/LJLmDqAHuERVl4nIMOAfInKPqj6Vokw15E0VbqPKFFSLIaRFlHhFUivNJFxLQ7o2wszT4KOfgoM7YEMnXHc1QxbdHUnWorxuY5Vmami9Z1wlQWoGQFXXAesKP28TkeXAoUCTGAA3x9aly6hRg51R7iuplWYSrqWuWbMZePFFMHCQMzBqNHx+Ll0DhsLu/svqZ6yWLnyJBVes6v/EEannjKskyEQMQETGA1OBiuO6IjIHmAMwdmx7TeVKE7fL6BsDTmb3ll1lBiFtd1G9BTuDEvW+klppJuJa+uin9in/IgMHOeM/CF5SwE09xUGamdQNgIgMBRYAF6tqxV+Gqs4H5gNMmzZM3c83C+7U028MODn1+EEc/+RhVtq12m3EcV9JrDSTcC21H+Bds95vPCiWcVMfpGoARGQAjvK/SVV/k6Ys9Ua5QagMKNci5TTqP3mYlXYtdxth7+v400cxZdoBSA40Dyse3sRDd66PVSZIxrUUh1HxSiPdsWWSZdzUAWlmAQnwE2C5qn43LTkag/KAcmXl02SCyVGVR5iVdi1dCmHu6/jTR3H48QfsLREsLXD48QcARDICnrn5OFk5ce6CXlixrUx+cMoKv7BiW2A5vdJIV/3m/zDp/FNij4M0aswpLdLcAZwEvA94XEQeLYx9RVXvSFGmhsBd+dR9SjkuV1HUFWmYlXYtXQph7mvKtHLlCSAiTJl2QL8NQNXcfKbHqvDGTKmsby8ijJkyLJD8fmmkY379bZYOn2QNgDJOmllA9wPZ7KzQYLh3B3GlmkYNdoZZadfyEE+Y+xKf2vB+40EIm5sfZVUc1bAO6droOx53HMQCy/GTehDYqC3lu4Py2EF/4gZR/snDrLRrfYgn6H1p3nH7eI33l2pK1U3UVXHXzl4GDqlUA107ewPJuqN9JEM95NrRPjLQ68NggeX4yWpvG6MmOLED5+tK8r29rFmyeu9X0qx+fCtLF77E9s3dqCrbN3ezdOFLnoorzLW1ZMXDm5w+rCWoKise3tTvOXe0j3QOZ938B/jT35zvM0/zVKrVVsW1YNnE2fTkyndmPbk2lk2cHft7+e32LLDcf2wHYBQoP4iWVNzATZgdRBYP8RT95HFmAb3wr1/g8H87ESk5nKWfn8sLhz4IrtT8qKvi9sEe25cq426KLqn+FpMLg5VyiB8zAIYn1eIGaR9AyxoP3bk+1rTPMe95EzKwfFUtAwcx5j1v4iHXKdqosZE4YiurO6YnovAr3sdKOcSOGQCjT0rjBmYMkifMqj7qqrjeVtVZ3AXWM2YAjFCUGgP3aeR6NAZZzCsPsyqPuiq2VXVzI+4AVpaZNm2YPvzwtLTFMDyYl5tR8qg+qphOOHo/TnrnobS07lv99vbkeeD36QaX3Zk94KzKsxD0riVZNM71ygWLL/iHqlYoT9sBGLGQRgA5KsfPGl2m/AFaWnMcP2t0qorGVuV26KtWmAEwYueydVth9enQNYItG3Zy7/wVPLFobeYMQdQMmCRpdl+3HfqqDWYAjHjpnAorz4a848MefvAQZn/1KAYM2sMjt2W7t0F/MVdF/Nihr9pgBsCIl9Wn71X+e8m38Y6vTOYdf7i1MLDvBHItqpb6EfUULJirIimsf29tMANgxEvXiADjxeql7jLW1YPHca+0H7prnWcQ+KG71gWew1wVyVBv6an1ihkAI17aN0PX/t7jFZSWsS7vieyOFySx0o4j2NoIroosurAsEF4b+jQAInIhTsOWV2sgj5FlbuqEuc/C810wth0unwjnuWrOTLizLAYAQK7bGa/KvlIUC956LjM/MoHhBw9iy4ZdPPbAxsyutOvdVZFlF1azB8JrQZAdwCjg7yKyDPgpcLfW0+EBIx5u6oQ5K2Bnoczlc13OYyg3Ah2PON8LWUC0b3aUf3G8LzqnMvurR+01ICNGDeaEt7cxoM07MyfKSjsO5ZdVV0XQVX1WDatRG/o0AKr67yJyKfA24IPAVSJyC/ATVf3fpAU0MsLcZ/cp/yI78864exfQ8Uhwhe/eVfzki9BavqJuG9hKPp9HPNpHhAnYuomr929xrqy4KsIYtkZwYRn9J1AMQFVVRNYD64EeYH/gVhG5R1W/kKSARkZ4vivceBC8dhW5AzwvzeXi7x0Ul/LLmqsijGGrdxeWEY0++wGIyKdF5B/At4EHgKNV9RPA64H4i34b2WRse7jxIHjtKjaEc520D2rpd++CRq0vH7aYXE93+e8gLhfWhM6lzH7wEt6/+AJmP3gJEzqXRp7TiJcgO4CRwHtU9bnSQVXNi8iZyYhlZI7LJ5av1gEG55zx/uK1e7juavj8XCjWwgcniJzbAz1DKi6X/EYu0isYvngLWxjOY0f8a+DSxGH991nMlvGi2qre6x6WLnwp9vuq2te4BqWjjWBUNQAikgNmq+plXs+r6vJEpDKyR9HP31cWUBjGtjtun1IW3Q0HtsLFF5UHkaEyuyjfBd+9khFsAWAEWzhh+c94efkrPDPi+D5PGofx32c5W8aNn2F7YcU2z3tYuvAlFrj6DEQlbF9jIx2qGoDCKv+fIjJWVZ+vdq3RBJzXEU3hu/HbVZz1LJzwTe/XlGYXXXEl3HFX2dNt7GH2uN+wYNJpe1NJd2z1V+xB/ff1lC3jZ9hqeQ9h+hob6RHEBTQaeFJEHgJ2FAdV9R2JSWU0B2F3Fe7solsWe1836bSyVNKhI9o44e2jgf6v1ustW8bLsJ38nsM8r03iHmrZLN7oP0EMwLzEpTCalyi7Ci8XEsDHL6yoR9Q2sJVjThrJkh8+0a/aQ7phA9JRKadu2BB6rrSoZcbPsomzy2IAkFyzeKP/9JkFpKpLvL5qIZxhVOXyiY7LqJTBORjpbVBGjBpIvreXNUtWs2bJmlBvJfN/ALt3lQ/u3uWM1wlJZvy4Wd0xnaVTLmB7+0gU2N4+kqVTLjD/f8YIUgriBOAHwBFAG9AC7FDV/RKWzTCq4+dCGuhTj6hnE5eN/f/g+S4263AWLZ7JE3JMoO5lO+5/mKFcDh/9FBzc4aSrXnc1O+5/GE48N9Jt1Cq7qNaH1oI2i89CdlUWZEiDIC6gq4BzgP8BpgHvByYlKZTRRHRO7X/ZCPB2IXV61CMqZAwVXUYj2MLswb+DnfDEEqWvSqTLJs5m+n3X07ro7r1jPbk2lk25wPP6oAql1tlFWTu0loXsqizIkBZBTwI/IyItqtoL/ExE7ESHER1X8xi69nceQzgj4MarHpFHxhA788wedzuz12wqa2Pp1bmsuJI97tkFDOnayI72kSybOJvVHdMrlP0LK7Yxaer+gRRKPWUXJUEW7j8LMqRFEAOwU0TagEdF5NvAOqDyRI5hhMWneQyrT49mACB4xlDhMFqxEuk+Q1C5I/ByaXitHg8//gBEyktX+CmUessuipss3H8WZEiLIAbgfTh+/wuBzwBjiKkEhIj8FDgT2KCqR8Uxp1FHBGoeExN+GUOuUhbuRjV99TH2Wj26lX8RL4XSbLV43Lslv65sfvefhK++2X4HpQTJAnpOVXep6lZVnaeqn1XVZ2J6/+uBWTHNZdQbnk1iqoxHwS9jyLOUxb5GNU7GkH+toTCrRC+FUsvMnLQp7paGjmhDRBg6oo22gTnc1eVVlRdWbAv0+ulnHcqEo6PlozTT78CNrwEQkcdF5DG/rzjeXFX/DGyKYy6jDplwp1Pnp5RAzWP6wXkdMH8KjGsHwfk+f0rVMwiX5Zf0aQj8VolupeanUFY/vpWlC19i++ZuVJXtm7tZuvClhvQ9e+2Wci25ih2TiDBmyrBAry+61qKw+vGtrHrkVfK9iqqS71VWPfJqQ/4O3FRzAWWi0JuIzAHmAIyNUnnSyB5Rm8eEpZ+HzirjA/sCxX51d1Y98ipjpgwL5KrIWmZOUoTZLXldG9ZXHyYTa9LU/cm1OIZIWmDS1P15+cWdDf978TUA7uqfaaGq84H5ANOmDbNOZI1GmOYxUVNGI+I2BONnTKiaW//QnetrJls94Odr97s26Ou9rg2T2mlZQFWwg2BGJkgqZbQfXJZfUpYtBOMbXlHEgdduqbfH8b23tPZdkjtM+e4wSt2ygKrjdRDsNUkKZRgVJJky2g+CpI0a5fjtlrzG/Cq3Br02jFJv5iygVA+CicivgFOAA0XkReAyVf1JHHMbDUYtU0ZDEDZttNnxi3eE6cEc5NowSj1sY6BGItWDYKoarYiK0Ty0+9T3CZMymlgM4U0VbiHbDaRLGKVe6xpJWSLoQbAcCRwEM4zATPCo7xMmZTTJGELBsFx2n2NYFly6micWpbsbmNC51LNshee1DVgILaxSb5ZMLDd9GoBiNpCI9AJ/AF5S1fopgm40BlFTRpOKIXgYltlfdTbITyxKZzcQph9vMxdCM6ofBLtWRF5b+Hk48E/g58AjImKuG6P2dDzitIqc8QXnexjFnVQMwcewzP7GBC7LXwlo1ZPESVCtH2/FtQkdrkqbpE4NNxrVSkGcrKpPFn7+ILBSVY8GXg98IXHJDCNOwpad6JwKf/0KLPm2871zqvd1VQ3LsWUniZ+/vzZttcP0423UFMhGNWxxU80AlC4h3gr8DkBV7XSLUX+EKTtRdOt07Q/IvniBlxEIYFickhJX7u1GljR+fXe9xv1SHes9BbJRDVvcVDMAm0XkTBGZCpwE3AUgIq3AoFoIZxix0fEIdPwd6AXU+d7xd283UrV4gZvAhuXYgkuIxI3Asomz6cmVy+/Xj7dRC6E1qmGLm2oG4GM4mT8/Ay4uWfnPBG5PWjAjRW7qhPEPQm6x8/2mKsogzLVp0jkVOt+Ac5BdnO+db/Be1YeJF3Q8ApNvhfZXAXW+T77VJz5R7hJa/6jzLzXh6P2YffEk3n/Zkcy+eFJkP3WYfryNWoyuUQ1b3FSrBbQSj1LNqno3cHflK4yG4KZOmLMCdhb+eZ7rch5DZSE1v2sf2AJ3bCzv0+tXhO2mzsqevlA51o8ibmWEyQIKe+YgTD0jyg+PDW3tSiQLJ2g/3uL7+BVJq9f00GbO7Q+DuMvWZplp04bpww9PS1uMxmb8g96NU8a1w5oTg10rOF6WIoNz3qWX3QYEoE1AFfYEeH0Ylny7IJgbdbKKSnGndoLj1vFd2fefzb+ex4hRgyvGt2/uZsEVq/o9b1TlPeHo/TjpnYeW1ejp7cmz8h/Bq5wa2eGCxRf8Q1UrlGegUhBGE/G8h0L3G/e71r2m2Jl3VvRuBT732XLlD9DtsSDxe30YejZC64He426SKlPtcRJ5xCjvcFqUYGUcuf3HzxpdpvzBKdhW2u7SzgzUP312BDMSJIv+8923p2IAACAASURBVAN81gQHtFbKe0BL8HnDGJCgrw/DdVfB7l3lY7t3OeNeRDlz4IVfZlHrTs/LowQr40iBbB/s/bv163Vs1Ce+OwAR+Wy1F6rqd+MXp4kI42uvKT4uwd29lfK2CQyg3F3jdv8U8Wrm49en14uozYBuuR1e6YGPfgoO7oANnXDd1fCnu+HmU6LNHQS/GIR0O+6lkuf27O6NFKysdQqkpVbWL9VcQMWebFOAN+CUgQA4C/hzkkI1BV7ujzhcHVHZ1Os9vkOp0OzdCiNbYWjLvoDtawbBIo9g6RkeuemXTwweA/Ds3RuCse2w6G7nq5RxNeoy55dZ1DsEDv/VXtfQlg07uXf+Cp5YtLbftYTiKG/s16zd7/2M+sTXBVRoAD8POBA4TlUvUdVLcE4CH1YrARuWML72WhJ2pb2xxwkO509xvj+63fu6WzZUupCgsk/vTw+Hnx0RqndvIEI1hU+AagfGStxNw//la8y+51cArFmypl9vFUcK5EN3rdvbrKVIvlcrxiy1sr4JYuLHUn4quBsYn4g0zYSf+yPtvsdeq/LBOdidh7zH9W5X8cYe73k39ni7vOZPqcwugvh3QcX54k4vDUrIaqal5aXD7gTiSIGM2rzFqA+CGIBfAA+JyG9xfADvxikKZ0TBT9H6rUg/uRLmr3UOsrYAcw6BayYHfz+vfHsv5eenKM9f7j2vj8fIk7RdXv1sCh8L/cgsuix/JfNyF/Wv0cy9d8H8BdC1EdpHwsTZEPBcQJGozVu8qOezBY1IoHMAInIccHLh4Z9VtfY9+GjAcwBBlfInV8IP11aOfyKgEfDKtw+bWx/0fMCBf4GNIayC4LiPDF/m5WYABDYC7nLQ4JSC8DsNXCvc6anguJAa4eRx1vE7BxA0DXQwsFVVrwReFBHrexcH53WU+8/9lPF8D+VfbdxNtYCzF17pqV5BXKgcv3KykxlUygCcYLEXB7RmLxUWvD+DqGm7fq/vY97S8hFBCFMOupZYhc7s0acLSEQuw2kGPwWnLtAA4EacAnFGLfBbUAddaIcJON/UCR96et+BrOe6nMd+mX63bCjfhZzX4ZSCKHVXfeQQOGm4d8bP1p59cYOkU2GD7ri8UnQ/9HR5dlKprND3vDd1wgeXl7/+g8udz+qG9X2mA1eLCbi7fw3p2ggzT6tIeR3izoCqMVahM3sE2QG8G3gHsANAVdeyL0XUqAV+562CnsPyCyx7jV+0qvI0brcW0kA9cAd9b+p0FFrROPXiPIbKjJ9hufJ0T6i+M4lCUak/1+VEsoqK1msVP/dZOPGtcPMf4E9/c76f/DZvWS9a6T3vJ1eWr+o/9nTl6/cA164NvDvz2gkU3T1DuzYiON2/mHkafH4ujBoNuZzz/fNz6ZqVbidXq9CZPYIYgG51AgUKICKxNIQ3QjDnkHDjbsKkQPpl8QSlr/MNpS4vvzMHSaTChnGDTTrFU4Ey87TKazf2es977dpyo+BnQP1CcD6fgdsIeLl75KOfgoGuEhMDBzk7ghSxCp3ZI4gBuEVEfgSMEJGPAvcCP05WLKOMayY7Ad/iir+F4AFgcBSve/UdR249wEjXNiSMu8lvZ5JEXCCMXB+/MLoCjVpjsUo68L6+Amu8u38d7P17bT9gaEShopGF0tNxl96ud4I0hf8vEXkrsBUnDvBVVb0nccmMcq6ZHC7t003QFMiRLd5ZPEPEcQWV7tYH4AR9S6l2vsHtgz9jZLn/G5KLC/jJVTQ2pf77Q33ex61YB+dgUA6OnVlZYiKov31oC3T1Vn6uVQ+oHbs3JrCjfaTj9illQ6eza3GRBVeLX2ppLYijSF6j0ecOQES+par3qOrnVfVzqnqPiHyrFsIZJdSqcJxfFs+PPE7o/uyISqXs5246Y2Slr/yG9fCBUbWJC/hlMW3uqfTf73zZ+9pdr1Tuoua/P7i7yE2bwPs6wFVgreJxFe7umlHR/av3xz8i31XuFurt2uPravFbFU/oXMrsBy/h/YsvYPaDlzChc2lguWpNkJW9ZSFVEsQF9FaPMY/eeEZihAlgRuW8Dn9FHyRt1c/ddMdGb1/5HRvL5/Q7QxC0aJwfd3g3Sq/IpNqZh+9937ty6PyrKu//kHOCu4uGSGXZizs2OgFmd8A5gMG7LL+EJ+Rofp8/s6z718qndqLf+SasXwf5vPP9O5c7h8NcFFfFQ0e0ISJ7V8XHj+2sCC5PX3F9Jo2A3z24jYBlIVVSrRroJ4BPAhNF5LGSp4YBDyQtmFFCXwHMuMsb+LmLwpwmdo+/z+cksdsH34J3emuIytOesoYxIPfe7RhaT7fOKeXX7h7h3WfGyw+/Q2FHQY7nuuCba5yA8+fm7jMixR3EfwH0LXPRFfRU77GMPWUsALMfvISWezfCvfs6t7YAx7WPrDgI5rcqnvL215L7ufdZgiiHyZI4CVxtZV86dxxF8hqNajGAXwJ3Av8JfKlkfJuqbkpUKqMcvwDmXpdFDUpKRy1fHbT2UdQzD+Ata1i8KocCtC4uL8cxsxMOHFV53dYtzmq+WlzgqV3wVZ+MnY9fCPx3IFHHz3iONUvG7X3sGRj2Gfdb/cpBBwWeIyhJ+eCDruyXLer0PInczFlI1aqBblHVNap6rqo+B+zCWRcNFZGxNZOwGQnaeKWFcCd8oxD2NLGboKmo7qyivsa98JI1KEUZZ55W7pYp+vRLzzf8cC380KPRTHc3DB4SLC7gk7HDyOAG/AP3rSHXkt+bGrqj3Tve4TXut/rVl73jIH5zB8FvpX78rNGRMnOCni/IQhZS1ggSBD5LRFYBq4ElwBqcnUFkRGSWiKwQkWdE5Et9v6IJ8PL3b8tXBmYH5/xXxUnk0UctXx04FdUvAOoz7hUcD3v/bpl8DlJ5KvBFdzv+9VJ/+84d0OZyNfjFBTb4rD4H+pSP9uHSPX8BnPMByybOrggM9+TaWDax8iCYX27+itufDDxHUPxW6u2DW/r031cjzPmC1Y9vZcEVq/j5vKdYcMWqplb+ECwI/B/ACcBKVZ0AzCSGGICItABX4wSUjwTOFZEjo85b9/j1yd2vtVJR+TUzSaKkdJjTxH4ECSJv8jmI5jXuFxz3a2vph1umsAepFt0N57wD3vJG5/t+w72v81rtX3e1Uxa6lCploqtRPB+wZNVhLJ1yQVlg2K8QnN+q+KHnOwLPERS/lXrUNpO2su8/Qf5T9qjqRhHJiUhOVe+LKQ30eOAZVX0WQERuBt4JPBXD3PWL3+p1Uw+88qbysQe2eFcJ9Ut5jELY8tX95QCfcwhebjA/t9QgcWQL4gYa4rGz8HPL+I278cnD91ztL7kbJh8ZUwP6Y7ksfznzcnNZ3TE9sLL2LfscYo4gePngVbXCAED4zJw0zxfUM0F2AJtFZChOG8ibRORKIGK9AAAOBV4oefxiYawMEZkjIg+LyMMvv9wE0fowK22/1Ea/8SgkeZq4jBAuIF9j2Vspa+lJ6iItOOcb3OR9chy8FPiRgyrHrrsauneXj/XsdsbdXH9EzA3onUVC0MqhtcRrpd6109uP2cyZObUkiAF4J04A+DPAXcD/4vQFjorXf3rFAXpVna+q01R12kEHNUG+bpi6PWGrfEY9SBa0fHUUwriAqhlLt6zXTIYbXOcbbvA4yAZw1B8h7/oM812w+peV5TiefGNlmY7Jj8Mxv4H2VwF1vh/1G/jg8+Xvf6PP+0ekWC8oi7h98A/dtc7qA6VIkFIQOwBEZD9gYYzv/SIwpuTxYUDAAvcNTJjWhUFTK6OmcNaSMC6gpNxSft271v4TDmt3fi+HtTslrsG7TMdNd8Hcayp/h34Nf6J0e/PAyQpaw/gZ4/s9h7vM9LKJs2NvKBNH+0qj//TZEUxEPgZ8HWcXkMdZv6iqRvovE5FWYCVOUPkl4O/Av6nqk36vabiOYFEJ2ukraDevLHDg/d4VSUe2VsZAoP81/iFcV7Qwrw9zbdRub1WYl5tBrqWFsW8Kn7Wd1a5iRv+I0hHsc8BrVXW8qk5U1QlRlT+AqvYAFwJ3A8uBW6opf8ODoH75qCmctSSMCwiCu6X8AsYXrQrmGgtzDiLMe0Xt9laFgcN3k+8Nc4JuH1ntKmbES5AsoP8Fdibx5qp6B3BHEnM3DUGqfAZ1FaWBewV/QKv3DiCqrH7GbmPAyqNhjGiY94rj5LMPX3z1b3v7CYclzGlio34JsgP4MrBURH4kIt8vfiUtmBEjYQLLSeIORH/So5vW1h6nSmbcsgY1IH6r+moB54qT2wHPIVRLUw1b+6gKa5asCf2aMKeJjfoliAH4EfAn4K/AP0q+jHqhZimcVfA6tOXVDnEPTknooLIGzW4KczbCawX/Go90T4Ahucr72hxDlnTQbm99MH7Gc/SnO02Y08S1xBq6xEuQpUqPqn42cUmMZAnaECYOvLJavMpB++mlTb3wysl9v0+Y7KZbQqQVemUcLfYpzfDUrsqxMO6bcYXGODFnARVdax94fjGbdTh/WnIq22YEz94uBnqTzgIKgzV0iZ8gBuA+EZmDkwK6d2lkFUENT9xZLcWiaWHw6tLlZbz66j9cil+fAU88jqjE4JP35IyR0bu9uXEZxhFs4Uz9A3/trCwHXY24TwJHJWjZZyM4QQzAvxW+f7lkTIEaO5CNTOIO4obNLBLKdwJhWkImld20qafyvpIiiVPbHoaxjT2Ra/mnjTV0iZ8+YwCFtE/3lyl/w9uvX83d7BWI/vgh/W8JGUeBOi8OaKm8r6SoYeXWes/gCVr22QiOrwEQkbcUvr/H66t2IhqhCVP2IUqJiDB191vwDkRfM7k8j3+Tj6/FS6klkd00OAdIuPuKQg0rt27Bp0ppnRCm7LMRjGouoBk42T9ekSMFfpOIREY0wgRGo5aICLN6nXNI/GcWwpTNGCJOS0Y3RQVeDMB+YJSTnRSUU0bAg1v714CmaKziLgXhWSIDFu2c2f85M4CVjYifIKUgJqjq6r7GaoGVgghAmLIPUUtEHPgX7+Bqu0CP9k+hRS3Z4CurT4kJN4NzTjnpoEHjcQWjE7T/8Lj2cmPlV9I7aikIjxIZ8973XsbPmND/OY26xa8URJAg8ALgONfYrcDr4xDMiJk4TqwGXtn7lG4e2uJdtycIYVb1YQii/KHQT6A1eD+B57sqdzanPgqLPNJGZ46Ae48tH3v/cu95f7Q2mgHw2m29r//TGY2JrwEQkcOB1wLDXT7//YCBSQtm9JMwLpSwJSLcq0o/pepXtycoSZxZaCF4KuemHic4XeqWGejjQvL6rO49ttIIzBwBHxxdmd7qZ2P62dK4L9YsWW27AGMv1XYAU4AzgRGUxwG2AR9NUigjAmFKJIe51ite4E7hLFLLGkNBq4GGyeMfLHDD+vIG8HuoNCJt4h9wdq/0q8VbasRl+SUseOu5nHbhJPOhG0AVA6Cqvwd+LyInquqDNZTJiEIYF0qYa70yfpRKI1DLGkNhgtjj+vDNl7JLIe+ybN0elq43RIkFv0Nrfni1qoxK51Te+YXX0drmRL6HjmjjpHfaSdpmJkgtoHeLyH4iMkBEFonIKyJyfuKSGeWESdcM07kr6LV+cQElvRpDYUo0h6kFFNT90otT4jkI1eIq7v/CHN6tKqOy6h17lX+RltYcx8/y6F9sNAVBDMDbVHUrjjvoRWAy8PlEpTLK8TpwNWdF/9o69hc/t04xYyjJNpF+hAliJ3HiFoIHl6t9fj93tar8eTKtIukd4jncPjjG0qNGXREkC6h4zvoM4Fequkkkge2p4U+YmjdJEUf7xaD++qCECWKHPXEbNAsoKNU+v1oW6msiJhy9n50Z6IMgBmChiDyN0xLykyJyELA7WbGMMrLQ0StqemYSfYnDGKW+8vNL8crtf6XbOwtoZMDVc1LprWEoxmyagLCVQ5vVWARpCv8lEfkWsFVVe0VkJ/DO5EUz9pKVjl5RVqpJ7GLCKFUvY9EmoFpee8hvVX5TJ3xwefm1A4ArY6zi2V86p1Y2sC82ti9l2xbYb0TFsG5tPEUXpnJoM5eZrlYL6AslD09V1V4AVd0BfDppwZqCoIHdrHT0CorXfSW1iwkaxD6vwynzUFywtwAfHg0/OyJYEPu8jsprfxbCV59UHKdzKqw8G7r2B8T5vvJsZ9zN9/8Lusv7/NLdjXz/O4HfbkLnUmY/eAnvX3wBsx+8hAmdS6PJnxBhKodWMxaNTrUg8DklP3/Z9dysBGRpLsIohCx09CrK3JfB8rsvvzaJUXcxfjJ5tZ905/bfsN75Oe6MKS/CZCyFYfXpkC/v3EW+zRl3s2oxfOvrsH4d5PPO9299nR33PxzorSZ0LmX6iusZ2rURAYZ2bWT6iuszaQTCVA5t5jLT1VxA4vOz12MjLGFdImkHCoP68P3uq8cnoOrXajGKTA9scZR76fi1aysPrcUVSA9SzC2pHVBXpUvHd/zyiXSf/yfaFt29d6gn18ayKRcEeqvjnl1Aa758B9Ga785kn4FlizrL3DrgVA594Tf3M/vBb5d1OduxZRJDR7RVzNEMZaar7QDU52evx0ZYshDYDUPQFayf/N3ew76tFqPINN+j17DfX2zUz7vYAa10Z/HDtc54KUn1Lmj3+fy8xs/rYCFnsb19JApsbx/J0ikXwKmzAvXZ9esnkMU+A6sf38rShS+xfXM3qsr2zd2sunExk268tGIHs+XRVbiLYqoqL6zYlo7wNaTaDuB1IrIVZ7U/qPAzhcdWCygqWQnsBiWowQqTbQPRWi36vU+YOaN+3vN9SkfPdxVziyON1osJdzo+/1I3UK7bGffgCTma7Se+Y9/LQwRAd7SPZKiHst/RHuKQXQ1Z/fjWsnuY/eC3PXcwo484EHdqu4gwZsowHrpzfU1kTYtqpSDsdEiSJKUQkiKowfK7r9157xO2Uf7KwhR486JaLZ+g+L2/ezyuNNCKsxSdcCrBsoA86CsAWpoa+cLYLzDpxkvLlGhPro1lE2eHu4eAxJ2a6bdTkYMO8r6+yWMARpJkIS88DEENlt99+dW9n3NI/2WqpvzdB7napLKeT08Mnkw/I+Rl2KLGcfxiHvOB8/pW+PNyMyrGqgVA3TuDSeefwiq+wZhfl/vQ/fz/URR4EqmZfjsYffllpKPy99IMMQAzAGmSdmA3DGGLzLnHi4/j7HzlV+DN7yCXOw6RBy5aGe13MOeQ+A2bHzGcpXCXgt6xZY9nAFTzeO4MxrznTSx4vu/3iqrAw+TxB2XZxNlMX3F9xQ5m1e1PMun8gyoCxs3QajJILSDDcAiaBumXmnnNZOg5BfQU53sU5Q/Vz0e4ZfU6xQvBO3/5cc1kp3tX6fmCqN28/EggccCvz674aIagbpGoufVJpGau7pjO0ikXVATBH3q+oyJgvHThSw1/CAxS2gGIyL8AXwOOAI5X1WCJyM1M3HV0kiKJkg9+ZMWNds3kZBS+m0iJA4/itPkux6/P7nEzOzx3Bl07e5l9cd/9BKIqcL+dSVS3zOqO6Z4uK3fAuFlIawfwBPAe4M8pvX99kYVqoEFJ6sCTH0F3JSN91jp+41mkhifCvXYGvT15BrTnGDqiDRHZ69bxShsNcxAr6Ps3i1umlqTy16+qy4GK1CvDhyxUAw1KVs83XDkJPvR0eSC4TZzxeiHCjmde7iKgMjD7woptTJq6f4WvfunCl1i68KWya1sH5Bg4pFxl+Pnl/Q5iBVXgfjuTpFbpVgzOyC5ZVapeZPV8g5/yhMo+ved1ZNflFiFx4M0Xvq4iMHv48QdULMSKSn3BFavKlOD7LzvSc14vt04cCjyMWyZrGUf1QmIGQETuBUZ5PDW30G4y6DxzgDkAY9NWImmRpFINo+iCXHv5RO+VdhbON3hV+AxaSiKpOEZSlFUIXc9RM9d5Bmb9duFeSj2sX75WfvUsZhzVC4nFAFT1VFU9yuMrsPIvzDNfVaep6rSDDmr8gxmeJOX7DRNb8Lv2kyvLM34e2OKUWC7F/ThOwrTKdBOmlESScYy4qagQOpqzPndMqAwa9Ti0l1W/fBYzjuoFSwOtB5KqBhomYOt37bVry43CtWvLa+aD8zgJ5Rk1OO7nQvPLDI3qcotirMLgUSG0bWCrp1L3wysN1Ku+ThbSJePIOAoz3kiklQb6buAHwEHA7SLyqKqeloYsdUMSh8bCxBaqNYWv9riv10chanDcz7Xmd7o3isutlumxPhVCJecUOSt1+7gf751ip7cVzGK6ZBjXlFesIGrAup5JZQegqr9V1cNUtV1VO0z5p0SYCpVR4w1j2+NfAUcNjvu51uYcEr/LrZbpsT4VQjVf6fP3iwHkWoUJR+8XqEpo2gR1TRVjBe40ViCTO5taYC6gRiRMp7E2lwLwC9h6KUu/LF73+OAcnDEy/rMMfk1m/Mbdnwt4u9aumRy/y62WmVwHPIV7K6aqvqd7vRjQlvNUllk0AkFdU30FexdcsYqfz3uqIvupkbE00EYjrKshaMDWK43yjJHl2TLgKPsPjII7NpZnCyVylsHP3+Qx7ltIbYpzgMxN3C63JDO53D2Be9twW2ERId+rSIjqq7XKjKlVDn4zB3v9sB1AFkkis8UvsBsmYOs+deu3Ur5mcuXp3CRWwJt8orVe47U+oewmqUwur57APYM9L5Wcc5q3FHcjlL6IW1n6uWXC7DSCztHMwV4/zABkjaQyW8IEdsMo5aClGJLoiBVmTr/mMWGa10QhqUwur57APr45r8Cu5pV8b2XJB78gcNzKMo6G7EHnyGoaa5qYCyhr+K1UL1oZ7MBWGFdDLU/tJtEAJ8ycYer2J0USmVw+GT/u7J7iyr+ltVxR5lpy7N7RQ8+enjIXDBA4MyaKCycOt0y1OdyF69zlLZql5IMfZgCyht/qe2PvvtLF1fz6YZTiGSO9a9mfkUCLvyQqd4aZM2jnrnqjfXPB/RPg0sHe1q59cAu/nrfC87m+lGXUU7hxVP30m6MoT6lcSxe+xIIrVgWeu9ExA5A1gvbU9QughlGKd/g0876lszKIG8fKNYkVcNA5s7ADSIIJd8LT76X0X1nziuTK3UAtrTlff7/fAbEgOf9RyyjEkYPvNYfX+YZmKe8QBjMAWcOrlo4ffruFoEox6m6jnmjUHQA4Ln91PQ7z8giRwGrulyCuobiKxrnnsIyfYJgByCJBMzPiOJwVZbdRT1RrH1nPrD4dtPzf2LfMej4PLZVbnjDuFrdS79rZW1EiGpyAc1DXUByni91zzL54UiINZRoNywLKGl6pmeB9uCpqCqHXQTA/slh6Ogw1bKZSU3yCwBWLiN274B8PVbiBVJUXVmwL9FZe6ZYD2nMVqaXFTJuo2T1RsIyfYJgByBp+K3IleAphmHMEtdpthMFP/ijnI5JKw0wbn7IPbNkM69c5q/716+A7l6PjJnqWghgzZVigt/Ly97e05tjTla84hesXcK6VCyarheuyhrmAska1YKXXiVU3YU4CV9ttlNqF4krZqx9AcZ7+Bozdc7pPF8dZoz+JIHTK/OGbK5n1qdfTNnDfv3Jv1x646kpa7r1971hPro2Wf/+G5xxBlbLfdV5ZRMfNTKanbxiyWLgua9gOIGuEDVa6V8UXrQx+4rVahU/3Shngg8vLD6h9YHnlWJhDa16H3q71qcVf7zX6E+CGN4/nkdte5q6rny5b6T5w23oeeOlQtrePRIHt7SNZOuWCyCdhw7zeXDD1ge0AskaYYKXXat8PL2XvFwQe11652zjwL5W7hV4qDVOYgLHXoTc/j1RSNfrrmDVLxjF+xgRe3UZlbnvHdFZ3TC8fi5hyGSZls9Y9fY3+YQYga4Q5yOWlQP3w8uGHea+NIfIlgyrlMGUYkqjRX8fMy80I/ZqoSjns680Fk33MAGSNMAe5gipaP6WexOlcCK6U/ZS6m2KFUa/Ko/WexdMPisp//IwJoV8bVSmbUm8szABkkaDBSj8XzshWGNoSTKnHHRgNo5SrKf9x7ZXynzQ8fmNVZ0RR/obhxgxAPePnwrlyUvyK0Z0ZVIqXsg5CtXhHLWr01xnfGHAyYMrfiA/LAqpnapnb/vFDvMc/cUiwctBeNOrhrAT4xoCTyffmTPkbsWI7gHqnVqviayY73+evdVw3LTi9c4vj/SGpGESDYW4fIynMABjBuWZyNIXvRZO7dfrClL+RJOYCMoyMYsrfSBozAIaRQUz5G7XADIBhZAxT/katMANgGJnhUVP+Rk2xILBhZIR5uYsAYfyM8WmLYjQJZgAMI2VK6/qY8jdqiRkAw0gRO91rpEkqMQAR+Y6IPC0ij4nIb0XEp6+dYTQu83Iz7HSvkSppBYHvAY5S1WOAlcCXU5LDMFLBgr1GFkjFBaSqfyx5+Ffg7DTkMIxa863938juLQMBU/5G+mQhBvAh4Nd+T4rIHGAOwNgmbf5hNAa26jeyRmIGQETuBUZ5PDVXVX9fuGYu0APc5DePqs4H5gNMmzbMryCxYWSWG948njVLxgGm/I1skZgBUNVTqz0vIh8AzgRmqqopdqMhsVW/kWVScQGJyCzgi8AMVd2ZhgyGkSS26jfqgbRiAFcB7cA9IgLwV1X9eEqyGEasFFf9A4cPYtSxXl5Qw8gGaWUBvSaN9zWMZLmfebm5gK36jfogC1lAhlH3OKv+GbbqN+oKMwCGEQlb9Rv1ixkAw+gXjxaqd84g19LC2DeNTVsgwwiNGQDDCEnR3QO26jfqGzMAhhGY4qrfFL/RGJgBMIw+Kfr5zd1jNBZmAAyjCkV3jyl+oxExA2AYHpR36TJ3j9GYmAEwjBKshIPRTJgBMAygNJ/f3D1Gs2AGwGhy9gV47RSv0WyYATCaFFP8hmEGwGgq9vn4TfEbhhkAoykoDe6a4jcMBzMARkNjWT2G4Y8ZAKNhsXaMhlEdqad2vCLyMvBc2nIkwIHAK2kLkQCNel/QuPfWqPcFjXtvQe5rnKoe5B6sKwPQqIjIw6o6CzsmrwAABERJREFULW054qZR7wsa994a9b6gce8tyn3l4hbGMAzDqA/MABiGYTQpZgCywfy0BUiIRr0vaNx7a9T7gsa9t37fl8UADMMwmhTbARiGYTQpZgAMwzCaFDMAGUFEviMiT4vIYyLyWxEZkbZMcSAi/yIiT4pIXkTqPgVPRGaJyAoReUZEvpS2PHEhIj8VkQ0i8kTassSJiIwRkftEZHnh7/CitGWKCxEZKCIPicg/C/c2L+wcZgCywz3AUap6DLAS+HLK8sTFE8B7gD+nLUhURKQFuBo4HTgSOFdEjkxXqti4HpiVthAJ0ANcoqpHACcAn2qg31kX8BZVfR1wLDBLRE4IM4EZgIygqn9U1Z7Cw78Ch6UpT1yo6nJVXZG2HDFxPPCMqj6rqt3AzcA7U5YpFlT1z8CmtOWIG1Vdp6rLCj9vA5YDh6YrVTyow/bCwwGFr1BZPWYAssmHgDvTFsKo4FDghZLHL9IgyqQZEJHxwFTgb+lKEh8i0iIijwIbgHtUNdS9WTG4GiIi9wJedYjnqurvC9fMxdm23lRL2aIQ5L4aBPEYszzqOkBEhgILgItVdWva8sSFqvYCxxZihr8VkaNUNXAcxwxADVHVU6s9LyIfAM4EZmodHdDo674aiBeBMSWPDwPWpiSLERARGYCj/G9S1d+kLU8SqOpmEVmME8cJbADMBZQRRGQW8EXgHaq6M215DE/+DkwSkQki0gacA/whZZmMKoiIAD8Blqvqd9OWJ05E5KBitqCIDAJOBZ4OM4cZgOxwFTAMuEdEHhWRa9MWKA5E5N0i8iJwInC7iNydtkz9pRCkvxC4GyeYeIuqPpmuVPEgIr8CHgSmiMiLIvLhtGWKiZOA9wFvKfxfPSoiZ6QtVEyMBu4TkcdwFif3qOptYSawUhCGYRhNiu0ADMMwmhQzAIZhGE2KGQDDMIwmxQyAYRhGk2IGwDAMo0kxA2AYASmktKqIHJ62LIYRB2YADCM45wL34xwAM4y6xwyAYQSgUEvmJODDFAyAiORE5JpCLfbbROQOETm78NzrRWSJiPxDRO4WkdEpim8YnpgBMIxgvAu4S1VXAptE5DicPgfjgaOBj+Ccdi7WnvkBcLaqvh74KXB5GkIbRjWsGJxhBONc4IrCzzcXHg8A/kdV88B6Ebmv8PwU4Cicsh4ALcC62oprGH1jBsAw+kBERgJvAY4SEcVR6Ar81u8lwJOqemKNRDSMfmEuIMPom7OBn6vqOFUdr6pjgNXAK8DsQiygAzilcP0K4CAR2esSEpHXpiG4YVTDDIBh9M25VK72FwCH4PQIeAL4EU6nqS2FdpFnA98SkX8CjwLTayeuYQTDqoEaRgREZKiqbi+4iR4CTlLV9WnLZRhBsBiAYUTjtkJTjjbgG6b8jXrCdgCGYRhNisUADMMwmhQzAIZhGE2KGQDDMIwmxQyAYRhGk2IGwDAMo0n5/wEPluIeM4m8TQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = x_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('yellow', 'purple')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'pink'))(i), label = j)\n",
    "plt.title('Naive Bayes Classifier (Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = x_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('white', 'black')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('black', 'white'))(i), label = j)\n",
    "plt.title('Naive Bayes Classifier (Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}