KNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Classified Data',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WTT</th>\n",
       "      <th>PTI</th>\n",
       "      <th>EQW</th>\n",
       "      <th>SBI</th>\n",
       "      <th>LQE</th>\n",
       "      <th>QWG</th>\n",
       "      <th>FDJ</th>\n",
       "      <th>PJF</th>\n",
       "      <th>HQE</th>\n",
       "      <th>NXJ</th>\n",
       "      <th>TARGET CLASS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.913917</td>\n",
       "      <td>1.162073</td>\n",
       "      <td>0.567946</td>\n",
       "      <td>0.755464</td>\n",
       "      <td>0.780862</td>\n",
       "      <td>0.352608</td>\n",
       "      <td>0.759697</td>\n",
       "      <td>0.643798</td>\n",
       "      <td>0.879422</td>\n",
       "      <td>1.231409</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.635632</td>\n",
       "      <td>1.003722</td>\n",
       "      <td>0.535342</td>\n",
       "      <td>0.825645</td>\n",
       "      <td>0.924109</td>\n",
       "      <td>0.648450</td>\n",
       "      <td>0.675334</td>\n",
       "      <td>1.013546</td>\n",
       "      <td>0.621552</td>\n",
       "      <td>1.492702</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.721360</td>\n",
       "      <td>1.201493</td>\n",
       "      <td>0.921990</td>\n",
       "      <td>0.855595</td>\n",
       "      <td>1.526629</td>\n",
       "      <td>0.720781</td>\n",
       "      <td>1.626351</td>\n",
       "      <td>1.154483</td>\n",
       "      <td>0.957877</td>\n",
       "      <td>1.285597</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.234204</td>\n",
       "      <td>1.386726</td>\n",
       "      <td>0.653046</td>\n",
       "      <td>0.825624</td>\n",
       "      <td>1.142504</td>\n",
       "      <td>0.875128</td>\n",
       "      <td>1.409708</td>\n",
       "      <td>1.380003</td>\n",
       "      <td>1.522692</td>\n",
       "      <td>1.153093</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.279491</td>\n",
       "      <td>0.949750</td>\n",
       "      <td>0.627280</td>\n",
       "      <td>0.668976</td>\n",
       "      <td>1.232537</td>\n",
       "      <td>0.703727</td>\n",
       "      <td>1.115596</td>\n",
       "      <td>0.646691</td>\n",
       "      <td>1.463812</td>\n",
       "      <td>1.419167</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        WTT       PTI       EQW       SBI       LQE       QWG       FDJ  \\\n",
       "0  0.913917  1.162073  0.567946  0.755464  0.780862  0.352608  0.759697   \n",
       "1  0.635632  1.003722  0.535342  0.825645  0.924109  0.648450  0.675334   \n",
       "2  0.721360  1.201493  0.921990  0.855595  1.526629  0.720781  1.626351   \n",
       "3  1.234204  1.386726  0.653046  0.825624  1.142504  0.875128  1.409708   \n",
       "4  1.279491  0.949750  0.627280  0.668976  1.232537  0.703727  1.115596   \n",
       "\n",
       "        PJF       HQE       NXJ  TARGET CLASS  \n",
       "0  0.643798  0.879422  1.231409             1  \n",
       "1  1.013546  0.621552  1.492702             0  \n",
       "2  1.154483  0.957877  1.285597             0  \n",
       "3  1.380003  1.522692  1.153093             1  \n",
       "4  0.646691  1.463812  1.419167             1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(df.drop('TARGET CLASS',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_features = scaler.transform(df.drop('TARGET CLASS',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.12354188,  0.18590747, -0.91343069, ..., -1.48236813,\n",
       "        -0.9497194 , -0.64331425],\n",
       "       [-1.08483602, -0.43034845, -1.02531333, ..., -0.20224031,\n",
       "        -1.82805088,  0.63675862],\n",
       "       [-0.78870217,  0.33931821,  0.30151137, ...,  0.28570652,\n",
       "        -0.68249379, -0.37784986],\n",
       "       ...,\n",
       "       [ 0.64177714, -0.51308341, -0.17920486, ..., -2.36249443,\n",
       "        -0.81426092,  0.11159651],\n",
       "       [ 0.46707241, -0.98278576, -1.46519359, ..., -0.03677699,\n",
       "         0.40602453, -0.85567   ],\n",
       "       [-0.38765353, -0.59589427, -1.4313981 , ..., -0.56778932,\n",
       "         0.3369971 ,  0.01034996]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_feat = pd.DataFrame(scaled_features,columns=df.columns[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WTT</th>\n",
       "      <th>PTI</th>\n",
       "      <th>EQW</th>\n",
       "      <th>SBI</th>\n",
       "      <th>LQE</th>\n",
       "      <th>QWG</th>\n",
       "      <th>FDJ</th>\n",
       "      <th>PJF</th>\n",
       "      <th>HQE</th>\n",
       "      <th>NXJ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.123542</td>\n",
       "      <td>0.185907</td>\n",
       "      <td>-0.913431</td>\n",
       "      <td>0.319629</td>\n",
       "      <td>-1.033637</td>\n",
       "      <td>-2.308375</td>\n",
       "      <td>-0.798951</td>\n",
       "      <td>-1.482368</td>\n",
       "      <td>-0.949719</td>\n",
       "      <td>-0.643314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.084836</td>\n",
       "      <td>-0.430348</td>\n",
       "      <td>-1.025313</td>\n",
       "      <td>0.625388</td>\n",
       "      <td>-0.444847</td>\n",
       "      <td>-1.152706</td>\n",
       "      <td>-1.129797</td>\n",
       "      <td>-0.202240</td>\n",
       "      <td>-1.828051</td>\n",
       "      <td>0.636759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.788702</td>\n",
       "      <td>0.339318</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.755873</td>\n",
       "      <td>2.031693</td>\n",
       "      <td>-0.870156</td>\n",
       "      <td>2.599818</td>\n",
       "      <td>0.285707</td>\n",
       "      <td>-0.682494</td>\n",
       "      <td>-0.377850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.982841</td>\n",
       "      <td>1.060193</td>\n",
       "      <td>-0.621399</td>\n",
       "      <td>0.625299</td>\n",
       "      <td>0.452820</td>\n",
       "      <td>-0.267220</td>\n",
       "      <td>1.750208</td>\n",
       "      <td>1.066491</td>\n",
       "      <td>1.241325</td>\n",
       "      <td>-1.026987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.139275</td>\n",
       "      <td>-0.640392</td>\n",
       "      <td>-0.709819</td>\n",
       "      <td>-0.057175</td>\n",
       "      <td>0.822886</td>\n",
       "      <td>-0.936773</td>\n",
       "      <td>0.596782</td>\n",
       "      <td>-1.472352</td>\n",
       "      <td>1.040772</td>\n",
       "      <td>0.276510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        WTT       PTI       EQW       SBI       LQE       QWG       FDJ  \\\n",
       "0 -0.123542  0.185907 -0.913431  0.319629 -1.033637 -2.308375 -0.798951   \n",
       "1 -1.084836 -0.430348 -1.025313  0.625388 -0.444847 -1.152706 -1.129797   \n",
       "2 -0.788702  0.339318  0.301511  0.755873  2.031693 -0.870156  2.599818   \n",
       "3  0.982841  1.060193 -0.621399  0.625299  0.452820 -0.267220  1.750208   \n",
       "4  1.139275 -0.640392 -0.709819 -0.057175  0.822886 -0.936773  0.596782   \n",
       "\n",
       "        PJF       HQE       NXJ  \n",
       "0 -1.482368 -0.949719 -0.643314  \n",
       "1 -0.202240 -1.828051  0.636759  \n",
       "2  0.285707 -0.682494 -0.377850  \n",
       "3  1.066491  1.241325 -1.026987  \n",
       "4 -1.472352  1.040772  0.276510  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_feat\n",
    "y = df['TARGET CLASS']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[151   8]\n",
      " [ 15 126]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.95      0.93       159\n",
      "           1       0.94      0.89      0.92       141\n",
      "\n",
      "    accuracy                           0.92       300\n",
      "   macro avg       0.92      0.92      0.92       300\n",
      "weighted avg       0.92      0.92      0.92       300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,pred))\n",
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate = []\n",
    "\n",
    "for i in range(1,100):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(X_train,y_train)\n",
    "    pred_i = knn.predict(X_test)\n",
    "    error_rate.append(np.mean(pred_i != y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (39,) and (99,)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-37-faf4b6510493>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m40\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0merror_rate\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'blue'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mls\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'dashed'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmarker\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'o'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmarkerfacecolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'red'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmarkersize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Error rate vs K Value'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'K'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Y'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\work\\virtual environmnets\\ml\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2759\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mdocstring\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2760\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscalex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2761\u001b[1;33m     return gca().plot(\n\u001b[0m\u001b[0;32m   2762\u001b[0m         *args, scalex=scalex, scaley=scaley, **({\"data\": data} if data\n\u001b[0;32m   2763\u001b[0m         is not None else {}), **kwargs)\n",
      "\u001b[1;32md:\\work\\virtual environmnets\\ml\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1644\u001b[0m         \"\"\"\n\u001b[0;32m   1645\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1646\u001b[1;33m         \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1647\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1648\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\work\\virtual environmnets\\ml\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    214\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m                 \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 216\u001b[1;33m             \u001b[1;32myield\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    217\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    218\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\work\\virtual environmnets\\ml\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m    340\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    341\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 342\u001b[1;33m             raise ValueError(f\"x and y must have same first dimension, but \"\n\u001b[0m\u001b[0;32m    343\u001b[0m                              f\"have shapes {x.shape} and {y.shape}\")\n\u001b[0;32m    344\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (39,) and (99,)"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,40),error_rate,color='blue',ls='dashed',marker='o',markerfacecolor='red',markersize=10)\n",
    "plt.title('Error rate vs K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[151   8]\n",
      " [  7 134]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.95      0.95       159\n",
      "           1       0.94      0.95      0.95       141\n",
      "\n",
      "    accuracy                           0.95       300\n",
      "   macro avg       0.95      0.95      0.95       300\n",
      "weighted avg       0.95      0.95      0.95       300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=39)\n",
    "knn.fit(X_train,y_train)\n",
    "pred = knn.predict(X_test)\n",
    "print(confusion_matrix(y_test,pred))\n",
    "print('\\n')\n",
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}