Decision Tree Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Classification"
   ]
  },
  {
   "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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      " [    52 150000]\n",
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      " [    36  52000]\n",
      " [    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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', random_state=0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing Package\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "classifier = DecisionTreeClassifier(criterion = \"entropy\", random_state = 0)\n",
    "\n",
    "# Fitting\n",
    "classifier.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predicting a new result**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "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",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 0]\n",
      " [0 0]\n",
      " [1 0]\n",
      " [1 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",
      " [0 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",
      " [1 1]\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",
      " [1 0]\n",
      " [1 1]\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",
      " [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",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[62  6]\n",
      " [ 3 29]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.91"
      ]
     },
     "execution_count": 13,
     "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": 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_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": 15,
   "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",
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