K-NN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from matplotlib.colors import ListedColormap\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Umur</th>\n",
       "      <th>Jenis Kelamin</th>\n",
       "      <th>Pendapatan</th>\n",
       "      <th>Membeli</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>Pria</td>\n",
       "      <td>20 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>15 juta</td>\n",
       "      <td>Tidak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>Pria</td>\n",
       "      <td>18 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>41</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>25 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>Pria</td>\n",
       "      <td>17 juta</td>\n",
       "      <td>Tidak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>35</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>22 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>Pria</td>\n",
       "      <td>19 juta</td>\n",
       "      <td>Tidak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>38</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>28 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>16 juta</td>\n",
       "      <td>Tidak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>Pria</td>\n",
       "      <td>21 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Umur Jenis Kelamin Pendapatan Membeli\n",
       "0   1    23          Pria    20 juta      Ya\n",
       "1   2    32        Wanita    15 juta   Tidak\n",
       "2   3    27          Pria    18 juta      Ya\n",
       "3   4    41        Wanita    25 juta      Ya\n",
       "4   5    29          Pria    17 juta   Tidak\n",
       "5   6    35        Wanita    22 juta      Ya\n",
       "6   7    26          Pria    19 juta   Tidak\n",
       "7   8    38        Wanita    28 juta      Ya\n",
       "8   9    27        Wanita    16 juta   Tidak\n",
       "9  10    30          Pria    21 juta      Ya"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_excel(\"Data-Pembelian.xlsx\")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Umur</th>\n",
       "      <th>Jenis Kelamin</th>\n",
       "      <th>Pendapatan</th>\n",
       "      <th>Membeli</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>Pria</td>\n",
       "      <td>20 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>15 juta</td>\n",
       "      <td>Tidak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>Pria</td>\n",
       "      <td>18 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>25 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>Pria</td>\n",
       "      <td>17 juta</td>\n",
       "      <td>Tidak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>22 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26</td>\n",
       "      <td>Pria</td>\n",
       "      <td>19 juta</td>\n",
       "      <td>Tidak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>38</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>28 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27</td>\n",
       "      <td>Wanita</td>\n",
       "      <td>16 juta</td>\n",
       "      <td>Tidak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>Pria</td>\n",
       "      <td>21 juta</td>\n",
       "      <td>Ya</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Umur Jenis Kelamin Pendapatan Membeli\n",
       "0    23          Pria    20 juta      Ya\n",
       "1    32        Wanita    15 juta   Tidak\n",
       "2    27          Pria    18 juta      Ya\n",
       "3    41        Wanita    25 juta      Ya\n",
       "4    29          Pria    17 juta   Tidak\n",
       "5    35        Wanita    22 juta      Ya\n",
       "6    26          Pria    19 juta   Tidak\n",
       "7    38        Wanita    28 juta      Ya\n",
       "8    27        Wanita    16 juta   Tidak\n",
       "9    30          Pria    21 juta      Ya"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.drop(\"ID\", axis=1, inplace=True)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Umur</th>\n",
       "      <th>Jenis Kelamin</th>\n",
       "      <th>Pendapatan</th>\n",
       "      <th>Membeli</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Umur  Jenis Kelamin  Pendapatan  Membeli\n",
       "0    23              0           5        1\n",
       "1    32              1           0        0\n",
       "2    27              0           3        1\n",
       "3    41              1           8        1\n",
       "4    29              0           2        0\n",
       "5    35              1           7        1\n",
       "6    26              0           4        0\n",
       "7    38              1           9        1\n",
       "8    27              1           1        0\n",
       "9    30              0           6        1"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "dataset[\"Jenis Kelamin\"] = le.fit_transform(dataset[\"Jenis Kelamin\"])\n",
    "dataset[\"Pendapatan\"] = le.fit_transform(dataset[\"Pendapatan\"])\n",
    "dataset[\"Membeli\"] = le.fit_transform(dataset[\"Membeli\"])\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[23,  5],\n",
       "       [32,  0],\n",
       "       [27,  3],\n",
       "       [41,  8],\n",
       "       [29,  2],\n",
       "       [35,  7],\n",
       "       [26,  4],\n",
       "       [38,  9],\n",
       "       [27,  1],\n",
       "       [30,  6]], dtype=int64)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = dataset.iloc[:, [0,2]].values\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 0, 1, 0, 1, 0, 1])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = dataset.iloc[:, 3].values\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[29,  2],\n",
       "       [30,  6],\n",
       "       [32,  0],\n",
       "       [26,  4],\n",
       "       [38,  9],\n",
       "       [41,  8],\n",
       "       [23,  5],\n",
       "       [35,  7]], dtype=int64)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[27,  3],\n",
       "       [27,  1]], dtype=int64)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 0, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.48661135, -1.0973782 ],\n",
       "       [-0.30966177,  0.3072659 ],\n",
       "       [ 0.0442374 , -1.79970025],\n",
       "       [-1.0174601 , -0.39505615],\n",
       "       [ 1.10593489,  1.36074897],\n",
       "       [ 1.63678363,  1.00958795],\n",
       "       [-1.54830884, -0.04389513],\n",
       "       [ 0.57508614,  0.65842692]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.84051051, -0.74621718],\n",
       "       [-0.84051051, -1.44853923]])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test = sc.transform(x_test)\n",
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "klasifikasi = KNeighborsClassifier(n_neighbors = 5, metric='minkowski', p=2)\n",
    "klasifikasi.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_prediction = klasifikasi.predict(x_test)\n",
    "y_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0],\n",
       "       [1, 0]], dtype=int64)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrikskonfusion = confusion_matrix(y_test, y_prediction)\n",
    "matrikskonfusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Haris\\AppData\\Local\\Temp\\ipykernel_2384\\1210696086.py:9: UserWarning: *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 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "  plt.scatter(x_set[y_set == j, 0], x_set[y_set == j, 1],\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_set, y_set = x_train, y_train\n",
    "x1, x2 = np.meshgrid(np.arange(start=x_set[:, 0].min() - 1, stop=x_set[:, 0].max() + 1, step=0.01),\n",
    "                         np.arange(start=x_set[:, 1].min() - 1, stop=x_set[:, 0].max() + 1, step=0.01))\n",
    "plt.contourf(x1, x2, klasifikasi.predict(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],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('Klasifikasi Data dengan K-NN (Data Training)')\n",
    "plt.xlabel('Umur')\n",
    "plt.ylabel('Pendapatan')\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": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}