K_Nearest_Neighbors_(K_NN).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "K-Nearest Neighbors (K-NN).ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMlWjz1g9BFXvLDHT97X/dY"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**K-Nearest Neighbors(K-NN)**"
      ],
      "metadata": {
        "id": "TXRHgzaz6QaH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing the libraries**"
      ],
      "metadata": {
        "id": "XE45O4Np6Wi8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "KAJD-q8G548i"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing the dataset**"
      ],
      "metadata": {
        "id": "B00-lh896bUl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "6CHpbxBt6epN"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Splitting the dataset into the Training set and Test set**"
      ],
      "metadata": {
        "id": "POwyWcRs6ryt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n",
        "print(X_train)\n",
        "print(y_train)\n",
        "print(X_test)\n",
        "print(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jSrFBa986vcK",
        "outputId": "1d043588-abd1-48c9-dc82-b87654e42274"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[    44  39000]\n",
            " [    32 120000]\n",
            " [    38  50000]\n",
            " [    32 135000]\n",
            " [    52  21000]\n",
            " [    53 104000]\n",
            " [    39  42000]\n",
            " [    38  61000]\n",
            " [    36  50000]\n",
            " [    36  63000]\n",
            " [    35  25000]\n",
            " [    35  50000]\n",
            " [    42  73000]\n",
            " [    47  49000]\n",
            " [    59  29000]\n",
            " [    49  65000]\n",
            " [    45 131000]\n",
            " [    31  89000]\n",
            " [    46  82000]\n",
            " [    47  51000]\n",
            " [    26  15000]\n",
            " [    60 102000]\n",
            " [    38 112000]\n",
            " [    40 107000]\n",
            " [    42  53000]\n",
            " [    35  59000]\n",
            " [    48  41000]\n",
            " [    48 134000]\n",
            " [    38 113000]\n",
            " [    29 148000]\n",
            " [    26  15000]\n",
            " [    60  42000]\n",
            " [    24  19000]\n",
            " [    42 149000]\n",
            " [    46  96000]\n",
            " [    28  59000]\n",
            " [    39  96000]\n",
            " [    28  89000]\n",
            " [    41  72000]\n",
            " [    45  26000]\n",
            " [    33  69000]\n",
            " [    20  82000]\n",
            " [    31  74000]\n",
            " [    42  80000]\n",
            " [    35  72000]\n",
            " [    33 149000]\n",
            " [    40  71000]\n",
            " [    51 146000]\n",
            " [    46  79000]\n",
            " [    35  75000]\n",
            " [    38  51000]\n",
            " [    36  75000]\n",
            " [    37  78000]\n",
            " [    38  61000]\n",
            " [    60 108000]\n",
            " [    20  82000]\n",
            " [    57  74000]\n",
            " [    42  65000]\n",
            " [    26  80000]\n",
            " [    46 117000]\n",
            " [    35  61000]\n",
            " [    21  68000]\n",
            " [    28  44000]\n",
            " [    41  87000]\n",
            " [    37  33000]\n",
            " [    27  90000]\n",
            " [    39  42000]\n",
            " [    28 123000]\n",
            " [    31 118000]\n",
            " [    25  87000]\n",
            " [    35  71000]\n",
            " [    37  70000]\n",
            " [    35  39000]\n",
            " [    47  23000]\n",
            " [    35 147000]\n",
            " [    48 138000]\n",
            " [    26  86000]\n",
            " [    25  79000]\n",
            " [    52 138000]\n",
            " [    51  23000]\n",
            " [    35  60000]\n",
            " [    33 113000]\n",
            " [    30 107000]\n",
            " [    48  33000]\n",
            " [    41  80000]\n",
            " [    48  96000]\n",
            " [    31  18000]\n",
            " [    31  71000]\n",
            " [    43 129000]\n",
            " [    59  76000]\n",
            " [    18  44000]\n",
            " [    36 118000]\n",
            " [    42  90000]\n",
            " [    47  30000]\n",
            " [    26  43000]\n",
            " [    40  78000]\n",
            " [    46  59000]\n",
            " [    59  42000]\n",
            " [    46  74000]\n",
            " [    35  91000]\n",
            " [    28  59000]\n",
            " [    40  57000]\n",
            " [    59 143000]\n",
            " [    57  26000]\n",
            " [    52  38000]\n",
            " [    47 113000]\n",
            " [    53 143000]\n",
            " [    35  27000]\n",
            " [    58 101000]\n",
            " [    45  45000]\n",
            " [    23  82000]\n",
            " [    46  23000]\n",
            " [    42  65000]\n",
            " [    28  84000]\n",
            " [    38  59000]\n",
            " [    26  84000]\n",
            " [    29  28000]\n",
            " [    37  71000]\n",
            " [    22  55000]\n",
            " [    48  35000]\n",
            " [    49  28000]\n",
            " [    38  65000]\n",
            " [    27  17000]\n",
            " [    46  28000]\n",
            " [    48 141000]\n",
            " [    26  17000]\n",
            " [    35  97000]\n",
            " [    39  59000]\n",
            " [    24  27000]\n",
            " [    32  18000]\n",
            " [    46  88000]\n",
            " [    35  58000]\n",
            " [    56  60000]\n",
            " [    47  34000]\n",
            " [    40  72000]\n",
            " [    32 100000]\n",
            " [    19  21000]\n",
            " [    25  90000]\n",
            " [    35  88000]\n",
            " [    28  32000]\n",
            " [    50  20000]\n",
            " [    40  59000]\n",
            " [    50  44000]\n",
            " [    35  72000]\n",
            " [    40 142000]\n",
            " [    46  32000]\n",
            " [    39  71000]\n",
            " [    20  74000]\n",
            " [    29  75000]\n",
            " [    31  76000]\n",
            " [    47  25000]\n",
            " [    40  61000]\n",
            " [    34 112000]\n",
            " [    38  80000]\n",
            " [    42  75000]\n",
            " [    47  47000]\n",
            " [    39  75000]\n",
            " [    19  25000]\n",
            " [    37  80000]\n",
            " [    36  60000]\n",
            " [    41  52000]\n",
            " [    36 125000]\n",
            " [    48  29000]\n",
            " [    36 126000]\n",
            " [    51 134000]\n",
            " [    27  57000]\n",
            " [    38  71000]\n",
            " [    39  61000]\n",
            " [    22  27000]\n",
            " [    33  60000]\n",
            " [    48  74000]\n",
            " [    58  23000]\n",
            " [    53  72000]\n",
            " [    32 117000]\n",
            " [    54  70000]\n",
            " [    30  80000]\n",
            " [    58  95000]\n",
            " [    26  52000]\n",
            " [    45  79000]\n",
            " [    24  55000]\n",
            " [    40  75000]\n",
            " [    33  28000]\n",
            " [    44 139000]\n",
            " [    22  18000]\n",
            " [    33  51000]\n",
            " [    43 133000]\n",
            " [    24  32000]\n",
            " [    46  22000]\n",
            " [    35  55000]\n",
            " [    54 104000]\n",
            " [    48 119000]\n",
            " [    35  53000]\n",
            " [    37 144000]\n",
            " [    23  66000]\n",
            " [    37 137000]\n",
            " [    31  58000]\n",
            " [    33  41000]\n",
            " [    45  22000]\n",
            " [    30  15000]\n",
            " [    19  19000]\n",
            " [    49  74000]\n",
            " [    39 122000]\n",
            " [    35  73000]\n",
            " [    39  71000]\n",
            " [    24  23000]\n",
            " [    41  72000]\n",
            " [    29  83000]\n",
            " [    54  26000]\n",
            " [    35  44000]\n",
            " [    37  75000]\n",
            " [    29  47000]\n",
            " [    31  68000]\n",
            " [    42  54000]\n",
            " [    30 135000]\n",
            " [    52 114000]\n",
            " [    50  36000]\n",
            " [    56 133000]\n",
            " [    29  61000]\n",
            " [    30  89000]\n",
            " [    26  16000]\n",
            " [    33  31000]\n",
            " [    41  72000]\n",
            " [    36  33000]\n",
            " [    55 125000]\n",
            " [    48 131000]\n",
            " [    41  71000]\n",
            " [    30  62000]\n",
            " [    37  72000]\n",
            " [    41  63000]\n",
            " [    58  47000]\n",
            " [    30 116000]\n",
            " [    20  49000]\n",
            " [    37  74000]\n",
            " [    41  59000]\n",
            " [    49  89000]\n",
            " [    28  79000]\n",
            " [    53  82000]\n",
            " [    40  57000]\n",
            " [    60  34000]\n",
            " [    35 108000]\n",
            " [    21  72000]\n",
            " [    38  71000]\n",
            " [    39 106000]\n",
            " [    37  57000]\n",
            " [    26  72000]\n",
            " [    35  23000]\n",
            " [    54 108000]\n",
            " [    30  17000]\n",
            " [    39 134000]\n",
            " [    29  43000]\n",
            " [    33  43000]\n",
            " [    35  38000]\n",
            " [    41  45000]\n",
            " [    41  72000]\n",
            " [    39 134000]\n",
            " [    27 137000]\n",
            " [    21  16000]\n",
            " [    26  32000]\n",
            " [    31  66000]\n",
            " [    39  73000]\n",
            " [    41  79000]\n",
            " [    47  50000]\n",
            " [    41  30000]\n",
            " [    37  93000]\n",
            " [    60  46000]\n",
            " [    25  22000]\n",
            " [    28  37000]\n",
            " [    38  55000]\n",
            " [    36  54000]\n",
            " [    20  36000]\n",
            " [    56 104000]\n",
            " [    40  57000]\n",
            " [    42 108000]\n",
            " [    20  23000]\n",
            " [    40  65000]\n",
            " [    47  20000]\n",
            " [    18  86000]\n",
            " [    35  79000]\n",
            " [    57  33000]\n",
            " [    34  72000]\n",
            " [    49  39000]\n",
            " [    27  31000]\n",
            " [    19  70000]\n",
            " [    39  79000]\n",
            " [    26  81000]\n",
            " [    25  80000]\n",
            " [    28  85000]\n",
            " [    55  39000]\n",
            " [    50  88000]\n",
            " [    49  88000]\n",
            " [    52 150000]\n",
            " [    35  65000]\n",
            " [    42  54000]\n",
            " [    34  43000]\n",
            " [    37  52000]\n",
            " [    48  30000]\n",
            " [    29  43000]\n",
            " [    36  52000]\n",
            " [    27  54000]\n",
            " [    26 118000]]\n",
            "[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",
            "[[    30  87000]\n",
            " [    38  50000]\n",
            " [    35  75000]\n",
            " [    30  79000]\n",
            " [    35  50000]\n",
            " [    27  20000]\n",
            " [    31  15000]\n",
            " [    36 144000]\n",
            " [    18  68000]\n",
            " [    47  43000]\n",
            " [    30  49000]\n",
            " [    28  55000]\n",
            " [    37  55000]\n",
            " [    39  77000]\n",
            " [    20  86000]\n",
            " [    32 117000]\n",
            " [    37  77000]\n",
            " [    19  85000]\n",
            " [    55 130000]\n",
            " [    35  22000]\n",
            " [    35  47000]\n",
            " [    47 144000]\n",
            " [    41  51000]\n",
            " [    47 105000]\n",
            " [    23  28000]\n",
            " [    49 141000]\n",
            " [    28  87000]\n",
            " [    29  80000]\n",
            " [    37  62000]\n",
            " [    32  86000]\n",
            " [    21  88000]\n",
            " [    37  79000]\n",
            " [    57  60000]\n",
            " [    37  53000]\n",
            " [    24  58000]\n",
            " [    18  52000]\n",
            " [    22  81000]\n",
            " [    34  43000]\n",
            " [    31  34000]\n",
            " [    49  36000]\n",
            " [    27  88000]\n",
            " [    41  52000]\n",
            " [    27  84000]\n",
            " [    35  20000]\n",
            " [    43 112000]\n",
            " [    27  58000]\n",
            " [    37  80000]\n",
            " [    52  90000]\n",
            " [    26  30000]\n",
            " [    49  86000]\n",
            " [    57 122000]\n",
            " [    34  25000]\n",
            " [    35  57000]\n",
            " [    34 115000]\n",
            " [    59  88000]\n",
            " [    45  32000]\n",
            " [    29  83000]\n",
            " [    26  80000]\n",
            " [    49  28000]\n",
            " [    23  20000]\n",
            " [    32  18000]\n",
            " [    60  42000]\n",
            " [    19  76000]\n",
            " [    36  99000]\n",
            " [    19  26000]\n",
            " [    60  83000]\n",
            " [    24  89000]\n",
            " [    27  58000]\n",
            " [    40  47000]\n",
            " [    42  70000]\n",
            " [    32 150000]\n",
            " [    35  77000]\n",
            " [    22  63000]\n",
            " [    45  22000]\n",
            " [    27  89000]\n",
            " [    18  82000]\n",
            " [    42  79000]\n",
            " [    40  60000]\n",
            " [    53  34000]\n",
            " [    47 107000]\n",
            " [    58 144000]\n",
            " [    59  83000]\n",
            " [    24  55000]\n",
            " [    26  35000]\n",
            " [    58  38000]\n",
            " [    42  80000]\n",
            " [    40  75000]\n",
            " [    59 130000]\n",
            " [    46  41000]\n",
            " [    41  60000]\n",
            " [    42  64000]\n",
            " [    37 146000]\n",
            " [    23  48000]\n",
            " [    25  33000]\n",
            " [    24  84000]\n",
            " [    27  96000]\n",
            " [    23  63000]\n",
            " [    48  33000]\n",
            " [    48  90000]\n",
            " [    42 104000]]\n",
            "[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0\n",
            " 0 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 0 1 1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1\n",
            " 0 0 0 0 1 1 1 0 0 0 1 1 0 1 1 0 0 1 0 0 0 1 0 1 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Feature Scaling**"
      ],
      "metadata": {
        "id": "cx8PTiN760zE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)\n",
        "print(X_train)\n",
        "print(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OUCa7VKn63xM",
        "outputId": "023248bb-6140-4f96-f6f0-880ef9417ede"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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",
            "[[-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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Training the K-NN model on the Training set**"
      ],
      "metadata": {
        "id": "HL8EuXqg67Bd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w0KZSXne6-Xc",
        "outputId": "442b2ac6-2aa6-490e-e5d7-a76646a75ff8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predicting a new result**"
      ],
      "metadata": {
        "id": "h4VZ_CDc7BAM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classifier.predict(sc.transform([[30,87000]])))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bIwYcRj_7Gl9",
        "outputId": "ee488b59-ba15-4119-d876-2a0baeed984e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predicting the Test set results**"
      ],
      "metadata": {
        "id": "DRIM5N5I7DtE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = classifier.predict(X_test)\n",
        "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AglCyeDU7LIM",
        "outputId": "c5ced908-2e6a-4f42-96aa-65bc9c7ed1cb"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Making the Confusion Matrix**"
      ],
      "metadata": {
        "id": "UyeiNcRU7SSc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ohcrYSWS7ROk",
        "outputId": "2a96847e-ea75-4064-fe24-8df40c2b8f08"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[64  4]\n",
            " [ 3 29]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.93"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualising the Training set results**"
      ],
      "metadata": {
        "id": "qq7Rj-kx7ZO0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = sc.inverse_transform(X_train), y_train\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",
        "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",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('K-NN (Training set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "nZ37OdWC7b7c",
        "outputId": "d395826c-b3fc-4482-bdcb-b496bd3389cf"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualising the Test set results**"
      ],
      "metadata": {
        "id": "BzCX1lEh7kbE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = sc.inverse_transform(X_test), y_test\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",
        "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",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('K-NN (Test set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "8lYszZ7g7mL8",
        "outputId": "c0d5aa2c-e6bd-4357-cb5c-be48ed318967"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}