KNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV,cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import timeit\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score,f1_score,precision_score,recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_resampled=pd.read_csv('resampled_without_encoding.csv')\n",
    "x_resampled_encoding=pd.read_csv('resampled_with_encoding.csv')\n",
    "y_resampled=pd.read_csv('y_resampled.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data with Resampling and without OneHotEncoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit the model in sec is  0.09587529999999589\n",
      "Accuracy score: 0.8476\n",
      "Time taken to fit the model in sec is  0.11707040000001712\n",
      "Accuracy score: 0.8704\n",
      "Time taken to fit the model in sec is  0.11278870000001007\n",
      "Accuracy score: 0.8228\n",
      "Time taken to fit the model in sec is  0.11515169999995578\n",
      "Accuracy score: 0.8430\n",
      "Time taken to fit the model in sec is  0.1166932999999517\n",
      "Accuracy score: 0.8047\n",
      "Time taken to fit the model in sec is  0.11036250000006476\n",
      "Accuracy score: 0.8232\n",
      "Time taken to fit the model in sec is  0.11178739999991194\n",
      "Accuracy score: 0.7910\n"
     ]
    }
   ],
   "source": [
    "X = x_resampled.values\n",
    "Y = np.squeeze(y_resampled.values)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.2,random_state=10)\n",
    "\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "train_sc = scaler.transform(X_train)\n",
    "test_sc = scaler.transform(X_test)\n",
    "\n",
    "for k in [3,4,5,6,7,8,9]:\n",
    "    \"\"\"\n",
    "    loop through each value of K\n",
    "    fit a model with the chosen value of K\n",
    "    check for predictions\n",
    "    finally print the accuracy score\n",
    "    \"\"\"\n",
    "    model=KNeighborsClassifier(n_neighbors=k)\n",
    "\n",
    "    start_time = timeit.default_timer()\n",
    "    model.fit(train_sc,y_train)\n",
    "    elapsed = timeit.default_timer() - start_time\n",
    "    \n",
    "    print(\"Time taken to fit the model in sec is \",elapsed)\n",
    "    y_pred=model.predict(test_sc)\n",
    "    print(f\"Accuracy score: {accuracy_score(y_test, y_pred):.4f}\")\n",
    "    model.get_params()\n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit the model in sec is  14454.099714099999\n"
     ]
    }
   ],
   "source": [
    "knn=KNeighborsClassifier()\n",
    "\n",
    "parameters = {\n",
    "    'n_neighbors': range(3,30,1)\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(estimator=knn, cv=5, param_grid=parameters)\n",
    "\n",
    "start_time = timeit.default_timer()\n",
    "grid_search.fit(train_sc, y_train)\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print(\"Time taken to fit the model in sec is \",elapsed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8598833089659553"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit the model in sec is  0.11449879999781842\n"
     ]
    }
   ],
   "source": [
    "knn_final = grid_search.best_estimator_\n",
    "start_time = timeit.default_timer()\n",
    "knn_final.fit(train_sc, y_train)\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print(\"Time taken to fit the model in sec is \",elapsed)\n",
    "y_pred1 = knn_final.predict(test_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.8704\n",
      "F1 score : 0.8855\n",
      "Recall score : 0.9996\n",
      "Precision score : 0.7949\n"
     ]
    }
   ],
   "source": [
    "print(f\"Accuracy score: {accuracy_score(y_test, y_pred1):.4f}\")\n",
    "print(f\"F1 score : {f1_score(y_test, y_pred1):.4f}\")\n",
    "print(f\"Recall score : {recall_score(y_test, y_pred1):.4f}\")\n",
    "print(f\"Precision score : {precision_score(y_test, y_pred1):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test,y_pred1),annot=True,fmt='',cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.74      0.85    114386\n",
      "           1       0.79      1.00      0.89    115022\n",
      "\n",
      "    accuracy                           0.87    229408\n",
      "   macro avg       0.90      0.87      0.87    229408\n",
      "weighted avg       0.90      0.87      0.87    229408\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data with Resampling and with OneHotEncoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit the model in sec is  62795.81762\n"
     ]
    }
   ],
   "source": [
    "X = x_resampled_encoding.values\n",
    "Y = np.squeeze(y_resampled.values)\n",
    "X_train,X_test2,y_train,y_test2=train_test_split(X,Y,test_size=0.2,random_state=10)\n",
    "\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "train_sc = scaler.transform(X_train)\n",
    "test_sc = scaler.transform(X_test2)\n",
    "\n",
    "knn=KNeighborsClassifier()\n",
    "parameters = {\n",
    "    'n_neighbors': range(3,30,1)\n",
    "}\n",
    "grid_search = GridSearchCV(estimator=knn, cv=5, param_grid=parameters)\n",
    "start_time = timeit.default_timer()\n",
    "grid_search.fit(train_sc, y_train)\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print(\"Time taken to fit the model in sec is \",elapsed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=4)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=4)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9189475483180708"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit the model in sec is  0.3150145999999836\n"
     ]
    }
   ],
   "source": [
    "knn_final = KNeighborsClassifier(n_neighbors=4)#grid_search.best_estimator_\n",
    "start_time = timeit.default_timer()\n",
    "knn_final.fit(train_sc, y_train)\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print(\"Time taken to fit the model in sec is \",elapsed)\n",
    "y_pred2 = knn_final.predict(test_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.9255\n",
      "F1 score : 0.9290\n",
      "Recall score : 0.9712\n",
      "Precision score : 0.8902\n"
     ]
    }
   ],
   "source": [
    "print(f\"Accuracy score: {accuracy_score(y_test2, y_pred2):.4f}\")\n",
    "print(f\"F1 score : {f1_score(y_test2, y_pred2):.4f}\")\n",
    "print(f\"Recall score : {recall_score(y_test2, y_pred2):.4f}\")\n",
    "print(f\"Precision score : {precision_score(y_test2, y_pred2):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test2,y_pred2),annot=True,fmt='',cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.88      0.92    114386\n",
      "           1       0.89      0.97      0.93    115022\n",
      "\n",
      "    accuracy                           0.93    229408\n",
      "   macro avg       0.93      0.93      0.93    229408\n",
      "weighted avg       0.93      0.93      0.93    229408\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test2, y_pred2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One Hot Encoding improved the accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performing Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit the model in sec is  0.28994149999925867\n",
      "Time taken to perform cv for the model in sec is  3567.5441681999946\n",
      "Cv2 scores: [0.58336239 0.72679125 0.73545707 0.73332985 0.73102826]\n",
      "Mean 5-Fold R Squared: 0.7019937636017449\n"
     ]
    }
   ],
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=4)\n",
    "\n",
    "start_time = timeit.default_timer()\n",
    "knn.fit(train_sc,y_train)\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print(\"Time taken to fit the model in sec is \",elapsed)\n",
    "\n",
    "start_time = timeit.default_timer()\n",
    "#perform k fold\n",
    "cv_r2_scores_rf = cross_val_score(knn, X, Y, cv=5,scoring='r2')\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print(\"Time taken to perform cv for the model in sec is \",elapsed)\n",
    "\n",
    "print(\"Cv2 scores:\",cv_r2_scores_rf)\n",
    "print(\"Mean 5-Fold R Squared: {}\".format(np.mean(cv_r2_scores_rf)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We see that the metrics for all folds in cross_val_score are uniform so it can be concluded that the model is able to generalise"
   ]
  }
 ],
 "metadata": {
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