K Value by using error graph.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the Data\n",
    "\n",
    "Set index_col=0 to use the first column as the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Classified Data\",index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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",
       "      <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standardize the Variables\n",
    "\n",
    "Because the KNN classifier predicts the class of a given test observation by identifying the observations that are nearest to it, the scale of the variables matters. Any variables that are on a large scale will have a much larger effect on the distance between the observations, and hence on the KNN classifier, than variables that are on a small scale."
   ]
  },
  {
   "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()"
      ]
     },
     "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": [
    {
     "data": {
      "text/html": [
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       "\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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feat = pd.DataFrame(scaled_features,columns=df.columns[:-1])\n",
    "df_feat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "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(scaled_features,df['TARGET CLASS'],\n",
    "                                                    test_size=0.30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using KNN\n",
    "\n",
    "Remember that we are trying to come up with a model to predict whether someone will TARGET CLASS or not. We'll start with k=1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions and Evaluations\n",
    "\n",
    "Let's evaluate our KNN model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[131  21]\n",
      " [  7 141]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.86      0.90       152\n",
      "           1       0.87      0.95      0.91       148\n",
      "\n",
      "    accuracy                           0.91       300\n",
      "   macro avg       0.91      0.91      0.91       300\n",
      "weighted avg       0.91      0.91      0.91       300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choosing a K Value\n",
    "\n",
    "Let's go ahead and use the elbow method to pick a good 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": "markdown",
   "metadata": {},
   "source": [
    "## From below error graph select k value"
   ]
  },
  {
   "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": {
      "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', 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')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that that after arouns K>23 the error rate just tends to hover around 0.06-0.05 Let's retrain the model with that and check the classification report!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WITH K=1\n",
      "\n",
      "\n",
      "[[131  21]\n",
      " [  7 141]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.86      0.90       152\n",
      "           1       0.87      0.95      0.91       148\n",
      "\n",
      "    accuracy                           0.91       300\n",
      "   macro avg       0.91      0.91      0.91       300\n",
      "weighted avg       0.91      0.91      0.91       300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# FIRST A QUICK COMPARISON TO OUR ORIGINAL K=1\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "\n",
    "knn.fit(X_train,y_train)\n",
    "pred = knn.predict(X_test)\n",
    "\n",
    "print('WITH K=1')\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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WITH K=23\n",
      "\n",
      "\n",
      "[[133  19]\n",
      " [  5 143]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.88      0.92       152\n",
      "           1       0.88      0.97      0.92       148\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": [
    "# 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": "3.8.8"
  }
 },
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