Naive Bayes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bayes Theorem\n",
    "    \n",
    "`P(A|B) = (P(B|A) * P(A) / (P(B))`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes\n",
    "- Suppose there are 2 categories; Person who goes to work by walk or drive. \n",
    "- Adding a new observation and lets predict weather a person will walk or drive.\n",
    "- Steps;\n",
    "    - Step 1 : Calculating the Bayes theorem for walk and they are named as,\n",
    "            P(Walks|X) = (P(X|Walks) * P(Walks) / (P(X)\n",
    "            1. X = Feature of new data point\n",
    "            2. P(Walks) = Prior Probability\n",
    "            3. P(X) = Marginal Probability\n",
    "            4. P(X|Walks) = Likelihood\n",
    "            5. P(Walks|X) = Posterior Porobability\n",
    "    - Step 2 : Calculating the Bayes theorem for drive and they are named as,\n",
    "            P(Drives|X) = (P(X|Drives) * P(Drives) / (P(X)\n",
    "            1. X = Feature of new data point\n",
    "            2. P(Drives) = Prior Probability\n",
    "            3. P(X) = Marginal Probability\n",
    "            4. P(X|Drives) = Likelihood\n",
    "            5. P(Drives|X) = Posterior Porobability\n",
    "    - Step 3 : P(Walks|X) vs P(Drives|X)\n",
    "          \n",
    "**Calculating for Walkers**\n",
    "- `P(Walks)` = Number of Walkers / Total Observation\n",
    "    - `P(Walks) = 10 / 30` \n",
    "\n",
    "\n",
    "- For Marginal Probability, you need to make a circle around a new data point of your own radius.\n",
    "- `P(X)` = Number of Similar Observation / Total Observation\n",
    "    - `P(X) = 4 / 30`\n",
    "    \n",
    "\n",
    "- For Likelihood, you need to make a circle around a new data point of your own radius and select only walker.\n",
    "- `P(X|Walks)` =  Number of Similar Observation Among those who Walks / Total Number of Walkers\n",
    "    - `P(X|Walks) = 3 / 10`\n",
    "\n",
    "\n",
    "- `P(Walks|X)` = [ (3/10) * (10/30) ] / (4/30)\n",
    "    - `P(Walks|X) = 0.75`\n",
    "    \n",
    "**Calculating for Drivers**\n",
    "- `P(Drives)` = Number of Drives / Total Observation\n",
    "    - `P(Drives) = 20 / 30` \n",
    "\n",
    "\n",
    "- For Marginal Probability, you need to make a circle around a new data point of your own radius.\n",
    "- `P(X)` = Number of Similar Observation / Total Observation\n",
    "    - `P(X) = 4 / 30`\n",
    "    \n",
    "\n",
    "- For Likelihood, you need to make a circle around a new data point of your own radius and select only walker.\n",
    "- `P(X|Drives)` =  Number of Similar Observation Among those who Drives / Total Number of Drivers\n",
    "    - `P(X|Drives) = 1 / 20`\n",
    "\n",
    "\n",
    "- `P(Drives|X)` = [ (1/20) * (20/30) ] / (4/30)\n",
    "    - `P(Drives|X) = 0.25`\n",
    "    \n",
    "\n",
    "**- So the P(Walks|X) > P(Drives|X)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing Packages**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Numpy allows us to work with array.\n",
    "import numpy as np\n",
    "\n",
    "# Maptplotlib which allows us to plot some chart.\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Pandas allows us to not only import the datasets but also create the matrix of features(independent) and \n",
    "# dependent variable.\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing Dataset**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The independent variable usally in the first columns of dataset and dependent variable usally in the last columns of the data sets.\n",
    "- X is Independent Variable.\n",
    "- Y is Dependent Variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
    "x = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, -1].values\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0\n",
      " 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 1 0 0 0 1 0 0 0 1 0 1\n",
      " 1 1 0 0 1 1 0 1 1 0 1 1 0 1 0 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0 1 1 0 1 0 0 1\n",
      " 1 0 1 1 0 1 1 0 0 1 0 0 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 0 1 0 1 1 1 1 0 0 0\n",
      " 1 1 0 1 1 1 1 1 0 0 0 1 1 0 0 1 0 1 0 1 1 0 1 0 1 1 0 1 1 0 0 0 1 1 0 1 0\n",
      " 0 1 0 1 0 0 1 1 0 0 1 1 0 1 1 0 0 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 1 1 0 1\n",
      " 1 1 0 1 0 1 0 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 1]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WemVnqgeA70k"
   },
   "source": [
    "**Splitting the dataset into the Training set and Test set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " [    27  54000]\n",
      " [    26 118000]]\n"
     ]
    }
   ],
   "source": [
    "# Importing Package\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Dividing training and test set.\n",
    "# The best ratio is 80 - 20 for trainging and testing respectively.\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.25, random_state = 0)\n",
    "\n",
    "print(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 1 1 1 0 0 0 0 0 0 1 1 1 0 1 0 0 1 0 1 0 1 0 0 1 1 1 1 0 1 0 1 0 0 1\n",
      " 0 0 1 0 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 0 1\n",
      " 1 1 0 0 1 1 0 0 1 1 0 1 0 0 1 1 0 1 1 1 0 0 0 0 0 1 0 0 1 1 1 1 1 0 1 1 0\n",
      " 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 0 1 1 0 0\n",
      " 0 0 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 0 0 1 0 0\n",
      " 0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 0\n",
      " 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0\n",
      " 0 0 1 0 1 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1\n",
      " 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YS8FeLHYS-nI"
   },
   "source": [
    "**Feature Scaling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Package\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "\n",
    "# Fitting and Transforming\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.58164944 -0.88670699]\n",
      " [-0.60673761  1.46173768]\n",
      " [-0.01254409 -0.5677824 ]\n",
      " [-0.60673761  1.89663484]\n",
      " [ 1.37390747 -1.40858358]\n",
      " [ 1.47293972  0.99784738]\n",
      " [ 0.08648817 -0.79972756]\n",
      " [-0.01254409 -0.24885782]\n",
      " [-0.21060859 -0.5677824 ]\n",
      " [-0.21060859 -0.19087153]\n",
      " [-0.30964085 -1.29261101]\n",
      " [-0.30964085 -0.5677824 ]\n",
      " [ 0.38358493  0.09905991]\n",
      " [ 0.8787462  -0.59677555]\n",
      " [ 2.06713324 -1.17663843]\n",
      " [ 1.07681071 -0.13288524]\n",
      " [ 0.68068169  1.78066227]\n",
      " [-0.70576986  0.56295021]\n",
      " [ 0.77971394  0.35999821]\n",
      " [ 0.8787462  -0.53878926]\n",
      " [-1.20093113 -1.58254245]\n",
      " [ 2.1661655   0.93986109]\n",
      " [-0.01254409  1.22979253]\n",
      " [ 0.18552042  1.08482681]\n",
      " [ 0.38358493 -0.48080297]\n",
      " [-0.30964085 -0.30684411]\n",
      " [ 0.97777845 -0.8287207 ]\n",
      " [ 0.97777845  1.8676417 ]\n",
      " [-0.01254409  1.25878567]\n",
      " [-0.90383437  2.27354572]\n",
      " [-1.20093113 -1.58254245]\n",
      " [ 2.1661655  -0.79972756]\n",
      " [-1.39899564 -1.46656987]\n",
      " [ 0.38358493  2.30253886]\n",
      " [ 0.77971394  0.76590222]\n",
      " [-1.00286662 -0.30684411]\n",
      " [ 0.08648817  0.76590222]\n",
      " [-1.00286662  0.56295021]\n",
      " [ 0.28455268  0.07006676]\n",
      " [ 0.68068169 -1.26361786]\n",
      " [-0.50770535 -0.01691267]\n",
      " [-1.79512465  0.35999821]\n",
      " [-0.70576986  0.12805305]\n",
      " [ 0.38358493  0.30201192]\n",
      " [-0.30964085  0.07006676]\n",
      " [-0.50770535  2.30253886]\n",
      " [ 0.18552042  0.04107362]\n",
      " [ 1.27487521  2.21555943]\n",
      " [ 0.77971394  0.27301877]\n",
      " [-0.30964085  0.1570462 ]\n",
      " [-0.01254409 -0.53878926]\n",
      " [-0.21060859  0.1570462 ]\n",
      " [-0.11157634  0.24402563]\n",
      " [-0.01254409 -0.24885782]\n",
      " [ 2.1661655   1.11381995]\n",
      " [-1.79512465  0.35999821]\n",
      " [ 1.86906873  0.12805305]\n",
      " [ 0.38358493 -0.13288524]\n",
      " [-1.20093113  0.30201192]\n",
      " [ 0.77971394  1.37475825]\n",
      " [-0.30964085 -0.24885782]\n",
      " [-1.6960924  -0.04590581]\n",
      " [-1.00286662 -0.74174127]\n",
      " [ 0.28455268  0.50496393]\n",
      " [-0.11157634 -1.06066585]\n",
      " [-1.10189888  0.59194336]\n",
      " [ 0.08648817 -0.79972756]\n",
      " [-1.00286662  1.54871711]\n",
      " [-0.70576986  1.40375139]\n",
      " [-1.29996338  0.50496393]\n",
      " [-0.30964085  0.04107362]\n",
      " [-0.11157634  0.01208048]\n",
      " [-0.30964085 -0.88670699]\n",
      " [ 0.8787462  -1.3505973 ]\n",
      " [-0.30964085  2.24455257]\n",
      " [ 0.97777845  1.98361427]\n",
      " [-1.20093113  0.47597078]\n",
      " [-1.29996338  0.27301877]\n",
      " [ 1.37390747  1.98361427]\n",
      " [ 1.27487521 -1.3505973 ]\n",
      " [-0.30964085 -0.27785096]\n",
      " [-0.50770535  1.25878567]\n",
      " [-0.80480212  1.08482681]\n",
      " [ 0.97777845 -1.06066585]\n",
      " [ 0.28455268  0.30201192]\n",
      " [ 0.97777845  0.76590222]\n",
      " [-0.70576986 -1.49556302]\n",
      " [-0.70576986  0.04107362]\n",
      " [ 0.48261718  1.72267598]\n",
      " [ 2.06713324  0.18603934]\n",
      " [-1.99318916 -0.74174127]\n",
      " [-0.21060859  1.40375139]\n",
      " [ 0.38358493  0.59194336]\n",
      " [ 0.8787462  -1.14764529]\n",
      " [-1.20093113 -0.77073441]\n",
      " [ 0.18552042  0.24402563]\n",
      " [ 0.77971394 -0.30684411]\n",
      " [ 2.06713324 -0.79972756]\n",
      " [ 0.77971394  0.12805305]\n",
      " [-0.30964085  0.6209365 ]\n",
      " [-1.00286662 -0.30684411]\n",
      " [ 0.18552042 -0.3648304 ]\n",
      " [ 2.06713324  2.12857999]\n",
      " [ 1.86906873 -1.26361786]\n",
      " [ 1.37390747 -0.91570013]\n",
      " [ 0.8787462   1.25878567]\n",
      " [ 1.47293972  2.12857999]\n",
      " [-0.30964085 -1.23462472]\n",
      " [ 1.96810099  0.91086794]\n",
      " [ 0.68068169 -0.71274813]\n",
      " [-1.49802789  0.35999821]\n",
      " [ 0.77971394 -1.3505973 ]\n",
      " [ 0.38358493 -0.13288524]\n",
      " [-1.00286662  0.41798449]\n",
      " [-0.01254409 -0.30684411]\n",
      " [-1.20093113  0.41798449]\n",
      " [-0.90383437 -1.20563157]\n",
      " [-0.11157634  0.04107362]\n",
      " [-1.59706014 -0.42281668]\n",
      " [ 0.97777845 -1.00267957]\n",
      " [ 1.07681071 -1.20563157]\n",
      " [-0.01254409 -0.13288524]\n",
      " [-1.10189888 -1.52455616]\n",
      " [ 0.77971394 -1.20563157]\n",
      " [ 0.97777845  2.07059371]\n",
      " [-1.20093113 -1.52455616]\n",
      " [-0.30964085  0.79489537]\n",
      " [ 0.08648817 -0.30684411]\n",
      " [-1.39899564 -1.23462472]\n",
      " [-0.60673761 -1.49556302]\n",
      " [ 0.77971394  0.53395707]\n",
      " [-0.30964085 -0.33583725]\n",
      " [ 1.77003648 -0.27785096]\n",
      " [ 0.8787462  -1.03167271]\n",
      " [ 0.18552042  0.07006676]\n",
      " [-0.60673761  0.8818748 ]\n",
      " [-1.89415691 -1.40858358]\n",
      " [-1.29996338  0.59194336]\n",
      " [-0.30964085  0.53395707]\n",
      " [-1.00286662 -1.089659  ]\n",
      " [ 1.17584296 -1.43757673]\n",
      " [ 0.18552042 -0.30684411]\n",
      " [ 1.17584296 -0.74174127]\n",
      " [-0.30964085  0.07006676]\n",
      " [ 0.18552042  2.09958685]\n",
      " [ 0.77971394 -1.089659  ]\n",
      " [ 0.08648817  0.04107362]\n",
      " [-1.79512465  0.12805305]\n",
      " [-0.90383437  0.1570462 ]\n",
      " [-0.70576986  0.18603934]\n",
      " [ 0.8787462  -1.29261101]\n",
      " [ 0.18552042 -0.24885782]\n",
      " [-0.4086731   1.22979253]\n",
      " [-0.01254409  0.30201192]\n",
      " [ 0.38358493  0.1570462 ]\n",
      " [ 0.8787462  -0.65476184]\n",
      " [ 0.08648817  0.1570462 ]\n",
      " [-1.89415691 -1.29261101]\n",
      " [-0.11157634  0.30201192]\n",
      " [-0.21060859 -0.27785096]\n",
      " [ 0.28455268 -0.50979612]\n",
      " [-0.21060859  1.6067034 ]\n",
      " [ 0.97777845 -1.17663843]\n",
      " [-0.21060859  1.63569655]\n",
      " [ 1.27487521  1.8676417 ]\n",
      " [-1.10189888 -0.3648304 ]\n",
      " [-0.01254409  0.04107362]\n",
      " [ 0.08648817 -0.24885782]\n",
      " [-1.59706014 -1.23462472]\n",
      " [-0.50770535 -0.27785096]\n",
      " [ 0.97777845  0.12805305]\n",
      " [ 1.96810099 -1.3505973 ]\n",
      " [ 1.47293972  0.07006676]\n",
      " [-0.60673761  1.37475825]\n",
      " [ 1.57197197  0.01208048]\n",
      " [-0.80480212  0.30201192]\n",
      " [ 1.96810099  0.73690908]\n",
      " [-1.20093113 -0.50979612]\n",
      " [ 0.68068169  0.27301877]\n",
      " [-1.39899564 -0.42281668]\n",
      " [ 0.18552042  0.1570462 ]\n",
      " [-0.50770535 -1.20563157]\n",
      " [ 0.58164944  2.01260742]\n",
      " [-1.59706014 -1.49556302]\n",
      " [-0.50770535 -0.53878926]\n",
      " [ 0.48261718  1.83864855]\n",
      " [-1.39899564 -1.089659  ]\n",
      " [ 0.77971394 -1.37959044]\n",
      " [-0.30964085 -0.42281668]\n",
      " [ 1.57197197  0.99784738]\n",
      " [ 0.97777845  1.43274454]\n",
      " [-0.30964085 -0.48080297]\n",
      " [-0.11157634  2.15757314]\n",
      " [-1.49802789 -0.1038921 ]\n",
      " [-0.11157634  1.95462113]\n",
      " [-0.70576986 -0.33583725]\n",
      " [-0.50770535 -0.8287207 ]\n",
      " [ 0.68068169 -1.37959044]\n",
      " [-0.80480212 -1.58254245]\n",
      " [-1.89415691 -1.46656987]\n",
      " [ 1.07681071  0.12805305]\n",
      " [ 0.08648817  1.51972397]\n",
      " [-0.30964085  0.09905991]\n",
      " [ 0.08648817  0.04107362]\n",
      " [-1.39899564 -1.3505973 ]\n",
      " [ 0.28455268  0.07006676]\n",
      " [-0.90383437  0.38899135]\n",
      " [ 1.57197197 -1.26361786]\n",
      " [-0.30964085 -0.74174127]\n",
      " [-0.11157634  0.1570462 ]\n",
      " [-0.90383437 -0.65476184]\n",
      " [-0.70576986 -0.04590581]\n",
      " [ 0.38358493 -0.45180983]\n",
      " [-0.80480212  1.89663484]\n",
      " [ 1.37390747  1.28777882]\n",
      " [ 1.17584296 -0.97368642]\n",
      " [ 1.77003648  1.83864855]\n",
      " [-0.90383437 -0.24885782]\n",
      " [-0.80480212  0.56295021]\n",
      " [-1.20093113 -1.5535493 ]\n",
      " [-0.50770535 -1.11865214]\n",
      " [ 0.28455268  0.07006676]\n",
      " [-0.21060859 -1.06066585]\n",
      " [ 1.67100423  1.6067034 ]\n",
      " [ 0.97777845  1.78066227]\n",
      " [ 0.28455268  0.04107362]\n",
      " [-0.80480212 -0.21986468]\n",
      " [-0.11157634  0.07006676]\n",
      " [ 0.28455268 -0.19087153]\n",
      " [ 1.96810099 -0.65476184]\n",
      " [-0.80480212  1.3457651 ]\n",
      " [-1.79512465 -0.59677555]\n",
      " [-0.11157634  0.12805305]\n",
      " [ 0.28455268 -0.30684411]\n",
      " [ 1.07681071  0.56295021]\n",
      " [-1.00286662  0.27301877]\n",
      " [ 1.47293972  0.35999821]\n",
      " [ 0.18552042 -0.3648304 ]\n",
      " [ 2.1661655  -1.03167271]\n",
      " [-0.30964085  1.11381995]\n",
      " [-1.6960924   0.07006676]\n",
      " [-0.01254409  0.04107362]\n",
      " [ 0.08648817  1.05583366]\n",
      " [-0.11157634 -0.3648304 ]\n",
      " [-1.20093113  0.07006676]\n",
      " [-0.30964085 -1.3505973 ]\n",
      " [ 1.57197197  1.11381995]\n",
      " [-0.80480212 -1.52455616]\n",
      " [ 0.08648817  1.8676417 ]\n",
      " [-0.90383437 -0.77073441]\n",
      " [-0.50770535 -0.77073441]\n",
      " [-0.30964085 -0.91570013]\n",
      " [ 0.28455268 -0.71274813]\n",
      " [ 0.28455268  0.07006676]\n",
      " [ 0.08648817  1.8676417 ]\n",
      " [-1.10189888  1.95462113]\n",
      " [-1.6960924  -1.5535493 ]\n",
      " [-1.20093113 -1.089659  ]\n",
      " [-0.70576986 -0.1038921 ]\n",
      " [ 0.08648817  0.09905991]\n",
      " [ 0.28455268  0.27301877]\n",
      " [ 0.8787462  -0.5677824 ]\n",
      " [ 0.28455268 -1.14764529]\n",
      " [-0.11157634  0.67892279]\n",
      " [ 2.1661655  -0.68375498]\n",
      " [-1.29996338 -1.37959044]\n",
      " [-1.00286662 -0.94469328]\n",
      " [-0.01254409 -0.42281668]\n",
      " [-0.21060859 -0.45180983]\n",
      " [-1.79512465 -0.97368642]\n",
      " [ 1.77003648  0.99784738]\n",
      " [ 0.18552042 -0.3648304 ]\n",
      " [ 0.38358493  1.11381995]\n",
      " [-1.79512465 -1.3505973 ]\n",
      " [ 0.18552042 -0.13288524]\n",
      " [ 0.8787462  -1.43757673]\n",
      " [-1.99318916  0.47597078]\n",
      " [-0.30964085  0.27301877]\n",
      " [ 1.86906873 -1.06066585]\n",
      " [-0.4086731   0.07006676]\n",
      " [ 1.07681071 -0.88670699]\n",
      " [-1.10189888 -1.11865214]\n",
      " [-1.89415691  0.01208048]\n",
      " [ 0.08648817  0.27301877]\n",
      " [-1.20093113  0.33100506]\n",
      " [-1.29996338  0.30201192]\n",
      " [-1.00286662  0.44697764]\n",
      " [ 1.67100423 -0.88670699]\n",
      " [ 1.17584296  0.53395707]\n",
      " [ 1.07681071  0.53395707]\n",
      " [ 1.37390747  2.331532  ]\n",
      " [-0.30964085 -0.13288524]\n",
      " [ 0.38358493 -0.45180983]\n",
      " [-0.4086731  -0.77073441]\n",
      " [-0.11157634 -0.50979612]\n",
      " [ 0.97777845 -1.14764529]\n",
      " [-0.90383437 -0.77073441]\n",
      " [-0.21060859 -0.50979612]\n",
      " [-1.10189888 -0.45180983]\n",
      " [-1.20093113  1.40375139]]\n"
     ]
    }
   ],
   "source": [
    "print(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.80480212  0.50496393]\n",
      " [-0.01254409 -0.5677824 ]\n",
      " [-0.30964085  0.1570462 ]\n",
      " [-0.80480212  0.27301877]\n",
      " [-0.30964085 -0.5677824 ]\n",
      " [-1.10189888 -1.43757673]\n",
      " [-0.70576986 -1.58254245]\n",
      " [-0.21060859  2.15757314]\n",
      " [-1.99318916 -0.04590581]\n",
      " [ 0.8787462  -0.77073441]\n",
      " [-0.80480212 -0.59677555]\n",
      " [-1.00286662 -0.42281668]\n",
      " [-0.11157634 -0.42281668]\n",
      " [ 0.08648817  0.21503249]\n",
      " [-1.79512465  0.47597078]\n",
      " [-0.60673761  1.37475825]\n",
      " [-0.11157634  0.21503249]\n",
      " [-1.89415691  0.44697764]\n",
      " [ 1.67100423  1.75166912]\n",
      " [-0.30964085 -1.37959044]\n",
      " [-0.30964085 -0.65476184]\n",
      " [ 0.8787462   2.15757314]\n",
      " [ 0.28455268 -0.53878926]\n",
      " [ 0.8787462   1.02684052]\n",
      " [-1.49802789 -1.20563157]\n",
      " [ 1.07681071  2.07059371]\n",
      " [-1.00286662  0.50496393]\n",
      " [-0.90383437  0.30201192]\n",
      " [-0.11157634 -0.21986468]\n",
      " [-0.60673761  0.47597078]\n",
      " [-1.6960924   0.53395707]\n",
      " [-0.11157634  0.27301877]\n",
      " [ 1.86906873 -0.27785096]\n",
      " [-0.11157634 -0.48080297]\n",
      " [-1.39899564 -0.33583725]\n",
      " [-1.99318916 -0.50979612]\n",
      " [-1.59706014  0.33100506]\n",
      " [-0.4086731  -0.77073441]\n",
      " [-0.70576986 -1.03167271]\n",
      " [ 1.07681071 -0.97368642]\n",
      " [-1.10189888  0.53395707]\n",
      " [ 0.28455268 -0.50979612]\n",
      " [-1.10189888  0.41798449]\n",
      " [-0.30964085 -1.43757673]\n",
      " [ 0.48261718  1.22979253]\n",
      " [-1.10189888 -0.33583725]\n",
      " [-0.11157634  0.30201192]\n",
      " [ 1.37390747  0.59194336]\n",
      " [-1.20093113 -1.14764529]\n",
      " [ 1.07681071  0.47597078]\n",
      " [ 1.86906873  1.51972397]\n",
      " [-0.4086731  -1.29261101]\n",
      " [-0.30964085 -0.3648304 ]\n",
      " [-0.4086731   1.31677196]\n",
      " [ 2.06713324  0.53395707]\n",
      " [ 0.68068169 -1.089659  ]\n",
      " [-0.90383437  0.38899135]\n",
      " [-1.20093113  0.30201192]\n",
      " [ 1.07681071 -1.20563157]\n",
      " [-1.49802789 -1.43757673]\n",
      " [-0.60673761 -1.49556302]\n",
      " [ 2.1661655  -0.79972756]\n",
      " [-1.89415691  0.18603934]\n",
      " [-0.21060859  0.85288166]\n",
      " [-1.89415691 -1.26361786]\n",
      " [ 2.1661655   0.38899135]\n",
      " [-1.39899564  0.56295021]\n",
      " [-1.10189888 -0.33583725]\n",
      " [ 0.18552042 -0.65476184]\n",
      " [ 0.38358493  0.01208048]\n",
      " [-0.60673761  2.331532  ]\n",
      " [-0.30964085  0.21503249]\n",
      " [-1.59706014 -0.19087153]\n",
      " [ 0.68068169 -1.37959044]\n",
      " [-1.10189888  0.56295021]\n",
      " [-1.99318916  0.35999821]\n",
      " [ 0.38358493  0.27301877]\n",
      " [ 0.18552042 -0.27785096]\n",
      " [ 1.47293972 -1.03167271]\n",
      " [ 0.8787462   1.08482681]\n",
      " [ 1.96810099  2.15757314]\n",
      " [ 2.06713324  0.38899135]\n",
      " [-1.39899564 -0.42281668]\n",
      " [-1.20093113 -1.00267957]\n",
      " [ 1.96810099 -0.91570013]\n",
      " [ 0.38358493  0.30201192]\n",
      " [ 0.18552042  0.1570462 ]\n",
      " [ 2.06713324  1.75166912]\n",
      " [ 0.77971394 -0.8287207 ]\n",
      " [ 0.28455268 -0.27785096]\n",
      " [ 0.38358493 -0.16187839]\n",
      " [-0.11157634  2.21555943]\n",
      " [-1.49802789 -0.62576869]\n",
      " [-1.29996338 -1.06066585]\n",
      " [-1.39899564  0.41798449]\n",
      " [-1.10189888  0.76590222]\n",
      " [-1.49802789 -0.19087153]\n",
      " [ 0.97777845 -1.06066585]\n",
      " [ 0.97777845  0.59194336]\n",
      " [ 0.38358493  0.99784738]]\n"
     ]
    }
   ],
   "source": [
    "print(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eiU6D2QFRjxY"
   },
   "source": [
    "**Training the Naive Bayes on the Training set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing Package\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "classifier = GaussianNB()\n",
    "\n",
    "# Fitting\n",
    "classifier.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predicting a new result**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "print(classifier.predict(sc.transform([[30, 87000]])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aPYA5W1pDBOE"
   },
   "source": [
    "**Predicting the Test set results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [1 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 1]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 0]\n",
      " [1 1]\n",
      " [0 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]]\n"
     ]
    }
   ],
   "source": [
    "# Predicting \n",
    "y_pred = classifier.predict(x_test)\n",
    "\n",
    "# Concatenating and Reshaping\n",
    "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))\n",
    "\n",
    "# First column is the predicted value and Second column is the real value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Making the confusion Matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[65  3]\n",
      " [ 7 25]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing Package\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "\n",
    "# Confusion Maxtrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "\n",
    "# Accuracy Score\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualising the Training Set results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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 the *color* keyword-argument or provide a 2-D array with a single row if you intend 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 the *color* keyword-argument or provide a 2-D array with a single row if you intend 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": [
    "# Importing Package\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "x_set, y_set = sc.inverse_transform(x_train), y_train\n",
    "\n",
    "x1, x2 = np.meshgrid(np.arange(start = x_set[:, 0].min() - 10, stop = x_set[:, 0].max() + 10, step = 1),\n",
    "                     np.arange(start = x_set[:, 1].min() - 1000, stop = x_set[:, 1].max() + 1000, step = 1))\n",
    "\n",
    "plt.contourf(x1, x2, classifier.predict(sc.transform(np.array([x1.ravel(), x2.ravel()]).T)).reshape(x1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "\n",
    "plt.xlim(x1.min(), x1.max())\n",
    "plt.ylim(x2.min(), x2.max())\n",
    "\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], c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "\n",
    "plt.title('Naive Bayes (Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualising the Test Set results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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 the *color* keyword-argument or provide a 2-D array with a single row if you intend 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 the *color* keyword-argument or provide a 2-D array with a single row if you intend 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": [
    "# Importing Package\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "x_set, y_set = sc.inverse_transform(x_test), y_test\n",
    "\n",
    "x1, x2 = np.meshgrid(np.arange(start = x_set[:, 0].min() - 10, stop = x_set[:, 0].max() + 10, step = 1),\n",
    "                     np.arange(start = x_set[:, 1].min() - 1000, stop = x_set[:, 1].max() + 1000, step = 1))\n",
    "\n",
    "plt.contourf(x1, x2, classifier.predict(sc.transform(np.array([x1.ravel(), x2.ravel()]).T)).reshape(x1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "\n",
    "plt.xlim(x1.min(), x1.max())\n",
    "plt.ylim(x2.min(), x2.max())\n",
    "\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], c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "\n",
    "plt.title('Naive Bayes (Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}