KNN With Python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/Classified Data',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "    <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",
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       "    <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",
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       "  </tbody>\n",
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      ],
      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(df.drop('TARGET CLASS',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_features = scaler.transform(df.drop('TARGET CLASS',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features = pd.DataFrame(scaled_features,columns=df.columns[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "    <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",
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      ],
      "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/faiz/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_features\n",
    "y = df[\"TARGET CLASS\"]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[159   0]\n",
      " [  0 141]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00       159\n",
      "          1       1.00      1.00      1.00       141\n",
      "\n",
      "avg / total       1.00      1.00      1.00       300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,pred))\n",
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Choosing a K Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate = []\n",
    "\n",
    "# Will take some time\n",
    "for i in range(1,40):\n",
    "    \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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Error Rate')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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5efRiuCEaYCAiIiKJULKWpFiTtbZtITdXddZEREQkPkrWklBdDUceCaWlza9rjGtdU8ua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\nayIiIiIRpmRNREREJML+DzUQhxaPWF9yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6322d85c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,40),error_rate,color='blue', linestyle='dashed', marker='o',\n",
    "         markerfacecolor='red', markersize=10)\n",
    "plt.title('Error Rate vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Error Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WITH K=23\n",
      "\n",
      "\n",
      "[[150   9]\n",
      " [ 10 131]]\n",
      "\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.94      0.94      0.94       159\n",
      "          1       0.94      0.93      0.93       141\n",
      "\n",
      "avg / total       0.94      0.94      0.94       300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# NOW WITH K=23\n",
    "knn = KNeighborsClassifier(n_neighbors=23)\n",
    "\n",
    "knn.fit(X_train,y_train)\n",
    "pred = knn.predict(X_test)\n",
    "\n",
    "print('WITH K=23')\n",
    "print('\\n')\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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}