KNN-results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "de078070",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns, pandas as pd, numpy as np\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4ddd68a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data/classdata-fixed.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "66ebfdab",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop( 'Unnamed: 0', axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8617aa68",
   "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>Ia</th>\n",
       "      <th>Ib</th>\n",
       "      <th>Ic</th>\n",
       "      <th>Va</th>\n",
       "      <th>Vb</th>\n",
       "      <th>Vc</th>\n",
       "      <th>fault</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-151.291812</td>\n",
       "      <td>-9.677452</td>\n",
       "      <td>85.800162</td>\n",
       "      <td>0.400750</td>\n",
       "      <td>-0.132935</td>\n",
       "      <td>-0.267815</td>\n",
       "      <td>1001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-336.186183</td>\n",
       "      <td>-76.283262</td>\n",
       "      <td>18.328897</td>\n",
       "      <td>0.312732</td>\n",
       "      <td>-0.123633</td>\n",
       "      <td>-0.189099</td>\n",
       "      <td>1001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-502.891583</td>\n",
       "      <td>-174.648023</td>\n",
       "      <td>-80.924663</td>\n",
       "      <td>0.265728</td>\n",
       "      <td>-0.114301</td>\n",
       "      <td>-0.151428</td>\n",
       "      <td>1001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-593.941905</td>\n",
       "      <td>-217.703359</td>\n",
       "      <td>-124.891924</td>\n",
       "      <td>0.235511</td>\n",
       "      <td>-0.104940</td>\n",
       "      <td>-0.130570</td>\n",
       "      <td>1001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-643.663617</td>\n",
       "      <td>-224.159427</td>\n",
       "      <td>-132.282815</td>\n",
       "      <td>0.209537</td>\n",
       "      <td>-0.095554</td>\n",
       "      <td>-0.113983</td>\n",
       "      <td>1001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7856</th>\n",
       "      <td>-66.237921</td>\n",
       "      <td>38.457041</td>\n",
       "      <td>24.912239</td>\n",
       "      <td>0.094421</td>\n",
       "      <td>-0.552019</td>\n",
       "      <td>0.457598</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7857</th>\n",
       "      <td>-65.849493</td>\n",
       "      <td>37.465454</td>\n",
       "      <td>25.515675</td>\n",
       "      <td>0.103778</td>\n",
       "      <td>-0.555186</td>\n",
       "      <td>0.451407</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7858</th>\n",
       "      <td>-65.446698</td>\n",
       "      <td>36.472055</td>\n",
       "      <td>26.106554</td>\n",
       "      <td>0.113107</td>\n",
       "      <td>-0.558211</td>\n",
       "      <td>0.445104</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7859</th>\n",
       "      <td>-65.029633</td>\n",
       "      <td>35.477088</td>\n",
       "      <td>26.684731</td>\n",
       "      <td>0.122404</td>\n",
       "      <td>-0.561094</td>\n",
       "      <td>0.438690</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7860</th>\n",
       "      <td>-64.598401</td>\n",
       "      <td>34.480799</td>\n",
       "      <td>27.250065</td>\n",
       "      <td>0.131669</td>\n",
       "      <td>-0.563835</td>\n",
       "      <td>0.432166</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7861 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Ia          Ib          Ic        Va        Vb        Vc  fault\n",
       "0    -151.291812   -9.677452   85.800162  0.400750 -0.132935 -0.267815   1001\n",
       "1    -336.186183  -76.283262   18.328897  0.312732 -0.123633 -0.189099   1001\n",
       "2    -502.891583 -174.648023  -80.924663  0.265728 -0.114301 -0.151428   1001\n",
       "3    -593.941905 -217.703359 -124.891924  0.235511 -0.104940 -0.130570   1001\n",
       "4    -643.663617 -224.159427 -132.282815  0.209537 -0.095554 -0.113983   1001\n",
       "...          ...         ...         ...       ...       ...       ...    ...\n",
       "7856  -66.237921   38.457041   24.912239  0.094421 -0.552019  0.457598      0\n",
       "7857  -65.849493   37.465454   25.515675  0.103778 -0.555186  0.451407      0\n",
       "7858  -65.446698   36.472055   26.106554  0.113107 -0.558211  0.445104      0\n",
       "7859  -65.029633   35.477088   26.684731  0.122404 -0.561094  0.438690      0\n",
       "7860  -64.598401   34.480799   27.250065  0.131669 -0.563835  0.432166      0\n",
       "\n",
       "[7861 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ca7b83cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "fault1001 = data[data[\"fault\"]==1001]\n",
    "fault1011 = data[data[\"fault\"]==1011]\n",
    "fault0110 = data[data[\"fault\"]==110]\n",
    "fault0111 = data[data[\"fault\"]==111]\n",
    "fault1111 = data[data[\"fault\"]==1111]\n",
    "fault0 = data[data[\"fault\"]==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7a32c312",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of              Ia         Ib         Ic        Va        Vb        Vc  fault\n",
       "5496  61.806321 -22.861987  21.102428  0.366320 -0.567179  0.200859      0\n",
       "5497  48.041678 -23.401108  21.273066  0.367341 -0.564257  0.196916      0\n",
       "5498  34.256533 -23.909263  21.408047  0.368258 -0.561168  0.192910      0\n",
       "5499  20.464350 -24.396165  21.501503  0.369086 -0.557916  0.188830      0\n",
       "5500   7.109677 -25.075888  21.341967  0.370321 -0.554505  0.184184      0\n",
       "...         ...        ...        ...       ...       ...       ...    ...\n",
       "7856 -66.237921  38.457041  24.912239  0.094421 -0.552019  0.457598      0\n",
       "7857 -65.849493  37.465454  25.515675  0.103778 -0.555186  0.451407      0\n",
       "7858 -65.446698  36.472055  26.106554  0.113107 -0.558211  0.445104      0\n",
       "7859 -65.029633  35.477088  26.684731  0.122404 -0.561094  0.438690      0\n",
       "7860 -64.598401  34.480799  27.250065  0.131669 -0.563835  0.432166      0\n",
       "\n",
       "[2365 rows x 7 columns]>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fault0.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cb1f72e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "41879ccc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f01e5824a0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.xlabel('Ia')\n",
    "plt.ylabel('Ib')\n",
    "plt.scatter(fault0111['Ia'], fault0111['Ib'],color=\"green\",marker='+')\n",
    "plt.scatter(fault1111['Ia'], fault1111['Ib'],color=\"blue\",marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e3d4f69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5 neighbors seems to be the best at still 100%\n",
    "# 6 negibhbors shows 0.9996% accuracy\n",
    "# 11 neighbors shows also 0.9996%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c7e55ea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d00a54b5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "feature_cols = data.columns[:-1]\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "strat_shuf_split = StratifiedShuffleSplit(n_splits=1,\n",
    "                                         test_size=0.3,\n",
    "                                         random_state=13)\n",
    "\n",
    "train_idx, test_idx = next(strat_shuf_split.split(data[feature_cols], data.fault))\n",
    "\n",
    "X_train = data.loc[train_idx, feature_cols]\n",
    "y_train = data.loc[train_idx, 'fault']\n",
    "\n",
    "X_test = data.loc[test_idx, feature_cols]\n",
    "y_test = data.loc[test_idx, 'fault']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e18afc3a",
   "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>Ia</th>\n",
       "      <th>Ib</th>\n",
       "      <th>Ic</th>\n",
       "      <th>Va</th>\n",
       "      <th>Vb</th>\n",
       "      <th>Vc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6161</th>\n",
       "      <td>-37.709198</td>\n",
       "      <td>98.788101</td>\n",
       "      <td>-64.455039</td>\n",
       "      <td>-0.600534</td>\n",
       "      <td>0.181597</td>\n",
       "      <td>0.418938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>-74.687919</td>\n",
       "      <td>-51.895067</td>\n",
       "      <td>49.567882</td>\n",
       "      <td>0.381840</td>\n",
       "      <td>-0.548580</td>\n",
       "      <td>0.166740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>872.348217</td>\n",
       "      <td>-477.672401</td>\n",
       "      <td>11.827999</td>\n",
       "      <td>0.018749</td>\n",
       "      <td>-0.287594</td>\n",
       "      <td>0.268845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5133</th>\n",
       "      <td>-440.454076</td>\n",
       "      <td>883.343053</td>\n",
       "      <td>-442.891240</td>\n",
       "      <td>0.022700</td>\n",
       "      <td>0.019633</td>\n",
       "      <td>-0.042333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>356.075223</td>\n",
       "      <td>-22.281544</td>\n",
       "      <td>46.334163</td>\n",
       "      <td>0.321854</td>\n",
       "      <td>-0.604672</td>\n",
       "      <td>0.282817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7345</th>\n",
       "      <td>-21.781412</td>\n",
       "      <td>92.148342</td>\n",
       "      <td>-73.379962</td>\n",
       "      <td>-0.618634</td>\n",
       "      <td>0.319590</td>\n",
       "      <td>0.299043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6550</th>\n",
       "      <td>-27.231385</td>\n",
       "      <td>95.964644</td>\n",
       "      <td>-71.985515</td>\n",
       "      <td>-0.617587</td>\n",
       "      <td>0.278883</td>\n",
       "      <td>0.338704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4602</th>\n",
       "      <td>884.427831</td>\n",
       "      <td>-417.029381</td>\n",
       "      <td>-467.395200</td>\n",
       "      <td>0.021054</td>\n",
       "      <td>-0.042415</td>\n",
       "      <td>0.021361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1428</th>\n",
       "      <td>487.592534</td>\n",
       "      <td>380.006894</td>\n",
       "      <td>54.091190</td>\n",
       "      <td>0.042123</td>\n",
       "      <td>-0.359371</td>\n",
       "      <td>0.317248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5598</th>\n",
       "      <td>50.201163</td>\n",
       "      <td>-18.362002</td>\n",
       "      <td>-31.735232</td>\n",
       "      <td>0.390440</td>\n",
       "      <td>0.208581</td>\n",
       "      <td>-0.599021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5502 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Ia          Ib          Ic        Va        Vb        Vc\n",
       "6161  -37.709198   98.788101  -64.455039 -0.600534  0.181597  0.418938\n",
       "342   -74.687919  -51.895067   49.567882  0.381840 -0.548580  0.166740\n",
       "1364  872.348217 -477.672401   11.827999  0.018749 -0.287594  0.268845\n",
       "5133 -440.454076  883.343053 -442.891240  0.022700  0.019633 -0.042333\n",
       "310   356.075223  -22.281544   46.334163  0.321854 -0.604672  0.282817\n",
       "...          ...         ...         ...       ...       ...       ...\n",
       "7345  -21.781412   92.148342  -73.379962 -0.618634  0.319590  0.299043\n",
       "6550  -27.231385   95.964644  -71.985515 -0.617587  0.278883  0.338704\n",
       "4602  884.427831 -417.029381 -467.395200  0.021054 -0.042415  0.021361\n",
       "1428  487.592534  380.006894   54.091190  0.042123 -0.359371  0.317248\n",
       "5598   50.201163  -18.362002  -31.735232  0.390440  0.208581 -0.599021\n",
       "\n",
       "[5502 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3097966e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=6)</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=6)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=6)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=6)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a8ab4671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8291649003815176"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "64b97d36",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "y_pred = knn.predict(X_test)\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ec98c0c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(132.0, 0.5, 'Truth')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "plt.figure(figsize=(17,15))\n",
    "sn.heatmap(cm, annot=True)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "55cf3c2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99       710\n",
      "         110       0.97      0.97      0.97       301\n",
      "         111       0.47      0.66      0.55       329\n",
      "        1001       0.95      0.99      0.97       339\n",
      "        1011       0.94      0.95      0.94       340\n",
      "        1111       0.42      0.23      0.30       340\n",
      "\n",
      "    accuracy                           0.83      2359\n",
      "   macro avg       0.79      0.80      0.79      2359\n",
      "weighted avg       0.82      0.83      0.82      2359\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}