KNN1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2', '_classic_test']\n"
     ]
    }
   ],
   "source": [
    "print(plt.style.available)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Classified Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('Unnamed: 0',axis=1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(df.drop('TARGET CLASS',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_features = scaler.transform(df.drop('TARGET CLASS',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_f = pd.DataFrame(scaled_features,columns=df.columns[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_f\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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate = []\n",
    "\n",
    "for i in range(1,60):\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": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x27e879b2e80>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27e876434a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(range(1,60),error_rate,ls='--',marker='o',markerfacecolor='red',markersize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using 40 as the value of 'K'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=40)\n",
    "knn.fit(X_train,y_train)\n",
    "pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[154   5]\n",
      " [  7 134]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.96      0.97      0.96       159\n",
      "          1       0.96      0.95      0.96       141\n",
      "\n",
      "avg / total       0.96      0.96      0.96       300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We got a 96% accurate result using K=40 "
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}