15. K Nearest Neighbours.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN -> K-number of nearest neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"./0 resources/Classified Data.csv\",index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### standardize the data to the same scale\n",
    "**in this case actual values are close to each other but they won't be always so its always a good idea to scale it**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler=StandardScaler()\n",
    "scaler.fit(df.drop(\"TARGET CLASS\",axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_features=scaler.transform(df.drop(\"TARGET CLASS\",axis=1))\n",
    "scaled_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['WTT', 'PTI', 'EQW', 'SBI', 'LQE', 'QWG', 'FDJ', 'PJF', 'HQE', 'NXJ'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns[:10:]   #or df.columns[:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating New Dataframe of scaled features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feat=pd.DataFrame(data=scaled_features,columns=df.columns[:10:])\n",
    "df_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df_feat\n",
    "y=df[\"TARGET CLASS\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9393939393939394"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train,y_train)\n",
    "knn.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[146   9]\n",
      " [ 11 164]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.94      0.94       155\n",
      "           1       0.95      0.94      0.94       175\n",
      "\n",
      "    accuracy                           0.94       330\n",
      "   macro avg       0.94      0.94      0.94       330\n",
      "weighted avg       0.94      0.94      0.94       330\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Elbow technique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate=[]\n",
    "\n",
    "k_rng=range(1,40)\n",
    "for k in k_rng:\n",
    "    knn=KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train,y_train)\n",
    "    pred=knn.predict(X_test)\n",
    "    error_rate.append(np.mean(pred!=y_test))##average of where my predictions were not equal to actual test values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Elbow technique')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (15,8))   ##larger than usual\n",
    "plt.plot(k_rng,error_rate,color=\"blue\",marker=\"o\",markerfacecolor=\"red\",linestyle=\":\",markersize=\"10\")\n",
    "plt.xlabel(\"K\")\n",
    "plt.ylabel(\"Error Rate\")\n",
    "plt.title(\"Elbow technique\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9515151515151515"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train,y_train)\n",
    "knn.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[146   9]\n",
      " [ 11 164]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.94      0.94       155\n",
      "           1       0.95      0.94      0.94       175\n",
      "\n",
      "    accuracy                           0.94       330\n",
      "   macro avg       0.94      0.94      0.94       330\n",
      "weighted avg       0.94      0.94      0.94       330\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(\"\\n\")\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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