K Nearest Neighbors Algorithm (KNN).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-NN\n",
    "- KNN is used to identify the new observation that in which category it will be assigned.\n",
    "- Steps;\n",
    "    - `Step1` : Choose the number K of neighbors. (defualt value of K is 5)\n",
    "    - `Step2` : Take the K nearest neighbors of the new data point, acoording to the Euclidean distance.\n",
    "    - `Step3` : Among these K neighbors, count the number of data point in each category.\n",
    "    - `Step4` : Assign the new data point to the category where you counted the most neighbors."
   ]
  },
  {
   "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",
     "text": [
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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",
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      " 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",
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      " 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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      " [    55  39000]\n",
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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 KNN model on the Training set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing Package\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "classifier = KNeighborsClassifier(n_neighbors = 5, metric = \"minkowski\", p = 2)\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",
      " [1 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",
      " [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",
      " [0 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",
      " [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": [
      "[[64  4]\n",
      " [ 3 29]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.93"
      ]
     },
     "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": 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('K-NN (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('K-NN (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
}