03_KNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data =pd.read_csv('bank/bank.csv',sep=';')\n",
    "col_cat = ['job','marital','education','default','housing','loan','contact','month','poutcome']\n",
    "col_num = ['age','balance','day','duration','campaign','pdays','previous']\n",
    "data_categorical = pd.get_dummies(data[col_cat])\n",
    "data_numerical = data[col_num]\n",
    "data_set = pd.concat([data_categorical,data_numerical],axis = 1)\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from boruta import BorutaPy\n",
    "X =data_set.values\n",
    "y = data['y'].copy()\n",
    "y = y.ravel()\n",
    "rf = RandomForestClassifier(n_jobs=-1, class_weight='balanced', max_depth=5)\n",
    "feat_selector = BorutaPy(rf, n_estimators='auto', verbose=2, random_state=1)\n",
    "feat_selector.fit(X, y)\n",
    "feat_selector.support_\n",
    "feat_selector.ranking_\n",
    "X_filtered = feat_selector.transform(X)\n",
    "\n",
    "x_feat = data_set.copy()\n",
    "drop_col = x_feat.columns[feat_selector.ranking_ > 1]\n",
    "x_feat.drop(drop_col,axis = 1, inplace=True)\n",
    "\n",
    "\n",
    "y[y == 'no'] = -1\n",
    "y[y == 'yes'] = 1\n",
    "y=y.astype('float')\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_feat, y, test_size=0.30, random_state=0)\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(x_train)\n",
    "x_train = scaler.transform(x_train)\n",
    "x_test = scaler.transform(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(x_train, y_train)\n",
    "predicted_label = knn.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Classification Report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "       -1.0       0.91      0.95      0.93      1190\n",
      "        1.0       0.50      0.37      0.43       167\n",
      "\n",
      "avg / total       0.86      0.88      0.87      1357\n",
      "\n",
      "Confusion Metrics: \n",
      " [[1129   61]\n",
      " [ 105   62]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy of random forest 87.77%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "report = classification_report(y_test, predicted_label)\n",
    "print(f' Classification Report\\n{report}')\n",
    "\n",
    "from sklearn import metrics\n",
    "labels=[-1,1]\n",
    "cm = metrics.confusion_matrix(y_test, predicted_label, labels)\n",
    "print(f'Confusion Metrics: \\n {cm}')\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(cm)\n",
    "plt.title('Confusion matrix of the classifier')\n",
    "fig.colorbar(cax)\n",
    "ax.set_xticklabels([''] + labels)\n",
    "ax.set_yticklabels([''] + labels)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()\n",
    "\n",
    "score=knn.score(x_test,y_test)*100\n",
    "print(f'Test Accuracy of knn is {round(score,2)}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tunning hyper parameter k\n",
    "\n",
    "\n",
    "# creating odd list of K for KNN\n",
    "neighbors = list(range(1,50,2))\n",
    "\n",
    "# subsetting just the odd ones\n",
    "#neighbors = filter(lambda x: x % 2 != 0, myList)\n",
    "\n",
    "# empty list that will hold cv scores\n",
    "cv_scores = []\n",
    "\n",
    "# perform 10-fold cross validation\n",
    "from sklearn import model_selection\n",
    "\n",
    "kfold = model_selection.KFold(n_splits=10, random_state=30) # random state =seed\n",
    "for k in neighbors:\n",
    "    knn_hp = KNeighborsClassifier(n_neighbors=k)\n",
    "    scores = model_selection.cross_val_score(knn_hp, x_train, y_train, cv= kfold, scoring='accuracy')\n",
    "    cv_scores.append(scores.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The optimal number of neighbors is 9\n"
     ]
    },
    {
     "data": {
      "image/png": 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7SaZpAbgMuD3LNQtpbNVQRg6v8DQpZlaWsiSS3ZKGACskfUDSXwOH9XZS2o7xAeAuksGNt0TEcknXSjoPQNIcSc3ARcD1kpanp78FOAu4XNLi9DUz3fdx4MOSmkjaTP4z++0WRuegRNdIzKwcZVlq90PASODvgX8hebx1WZaLR8SdwJ1dyq7JeT+f5PFU1/N+BPyom2uuIukRNqA01FR57XYzK0tZJm2cn77dDnjZ3W7UV1fy5HOFbdQ3MxuIsiy1e7ekmpztWkl3FTaswae+uoqW7XvY29bR+8FmZiUkSxtJXTqiHICI2ESGNpJy01BTSQQ8v9WPt8ysvGRJJB2SGjs3JE3hIMeDlIP6nEGJZmblJEtj+z8Av5d0f7p9FnBF4UIanBpqvMCVmZWnLI3tv5M0C3gFycjy/xsRrQWPbJDprJF4LImZlZtuH21JOjb9OQtoJBlB/izQmJZZjlEjhjK2cqhrJGZWdnqqkXyY5BHWl/LsC7KNbi8rDTVe4MrMyk9PieTu9Oe700GA1ouJHt1uZmWop15bnasg/ryHYyxHfXWVZwA2s7LTU41kg6R7gWmSXjTZYkScV7iwBqeG6ko27NjL7n3tVA6rKHY4Zmb9oqdE8pfALJIp4/O1k1gX9TVJz63ntuxmat2oIkdjZtY/elohcS/wsKR5EeFJpDJoSBe4WrtllxOJmZWNbhOJpK9GxIeA/5L0opHsfrT1Yp01EreTmFk56enRVucqiP/eH4GUgvrqSiR4ZuPOYodiZtZvenq0tTD92Tk1CpJqgckRsaQfYht0KodVcMzhY1i8ZnPvB5uZlYgs08jfJ2mspHHA48CNkr5c+NAGp1Maa3ls9SY6OjyvpZmVhyyz/1ZHxFbgAuDGiDgVeG1hwxq8ZjXWsG13Gytbthc7FDOzfpElkQyVVE+yjvqvD+biks6V9KSkJklX59l/lqRFktokXdhl3+8kbZb06y7l35P0VJ613AeEUxprAVi0elORIzEz6x9ZEsm1wF1AU0TMl3QksKK3kyRVANcBrweOBy6RdHyXw1YDlwM35bnEF4F3dHP5j0bEzPS1OMM99Jsj60ZRXTWMRc+4ncTMykOWaeR/BvwsZ3sV8OYM155LknxWAUi6GTgf+GPOtZ5O971ofdqIuEfSqzJ8zoAyZIg4pbHGNRIzKxtZGtu/kDa2D5N0j6RWSW/PcO0jgDU5281p2aHwGUlLJH1F0oh8B0i6QtICSQtaWvp3POWsxlpWrN/Oll37+vVzzcyKIcujrbPTxvY3kiSDo4GPZjhPecoORVemTwDHAnOAccDH8x0UETdExOyImD1hwoRD8LHZzUrbSdwN2MzKQZZEMiz9+QbgJxGxMeO1m4HJOduTSBbH6pOIWBeJPcCNJI/QBpSTJ1cjwaJn/HjLzEpflkTyK0l/AmYD90iaAGSZA2Q+MEPSNEnDgYuBF80ifLDSHmRIEvAmYFlfr3mojakcxjGHj3E7iZmVhV4TSURcDZwGzI6IfcAOkkbz3s5rAz5A0uPrCeCWiFgu6VpJ5wFImiOpGbgIuF7S8s7zJT1A0sj/F5KaJZ2T7vqxpKXAUqAO+Nfst9t/TmmsZfGazR6YaGYlr9deW6kjgNdJqswp+0FvJ0XEncCdXcquyXk/n+SRV75zz+ymfFAs8TursYafPLqappbtHH34mGKHY2ZWMFl6bX0a+Eb6ejXwBcAz//Zi1pSkwf0xP94ysxKXpY3kQuAvgOci4l3AyUDeLrf2giPrRlEz0gMTzaz0ZUkkuyKiA2iTNBZYDxxZ2LAGP0mcMtkDE82s9GVJJAsk1QDfARYCi4BHCxpVifDARDMrB1mmSPm79O23Jf0OGOv1SLLpbCdZvGYzrzy6fwdFmpn1l56W2p3V076IWFSYkErHyZNrGJIOTHQiMbNS1VON5Es97AtgUHTDLabRI4ZytAcmmlmJ62mp3Vf3ZyClataUWn71+Fo6OoIhQ/JNP2ZmNrhlGUfy/rSxvXO7VtLf9XSOvWBWYy3bdrfR5BUTzaxEZem19Z6I2D8YIiI2Ae8pXEilZVZjkoM9gaOZlaosiWRIOkEisH/lw+GFC6m0TKsbRe3IYW4nMbOSlWWurbuAWyR9m6SR/b3A7woaVQmRxCmNtSxa7RHuZlaastRIPg7cA7wPeH/6/mOFDKrUzGqsoWn9drbs9MBEMys9WaaR74iIb0fEhSRtIw9FRHvhQysdnSsmPrbGj7fMrPRk6bV1X7pm+zhgMXCjpC8XPrTSsX9goh9vmVkJyvJoqzpds/0C4MaIOBV4bWHDKi2jRgzlmIljPaW8mZWkLIlkaLq87VuAXxc4npI1q7GGxau9YqKZlZ4sieRakp5bTRExX9KRwIosF5d0rqQnJTVJujrP/rMkLZLUJunCLvt+J2mzpF93KZ8m6RFJKyT9NF0PfsCb1VjLtj1trFjvgYlmVlqyNLb/LCJO6pwFOCJWRcSbezsvHW9yHfB64HjgEknHdzlsNXA5cFOeS3wReEee8s8DX4mIGcAm4N29xTIQdM4E7PEkZlZquk0kkj6W/vyGpK93fWW49lySWsyqiNgL3Aycn3tARDydTknf0fXkiLgH2NYlJpFMFvnztOj7wJsyxFJ0U8ePZNyo4R7hbmYlp6cBiU+kPxe8xGsfAazJ2W4GXv4Sr9VpPLA5ItpyrnlEH6/ZL7xiopmVqp5m//1V+vP7L/Ha+aa67WtLc+ZrSroCuAKgsbGxjx97aMyaUss9f1rP5p17qRk5KJp2zMx61dPCVnf0dGJEnNfLtZuByTnbk4C12UPLqxWokTQ0rZV0e82IuAG4AWD27NkDoqvUKekEjo+t2cyrjzmsyNGYmR0aPT3aOo3k0dRPgEfIXxvoyXxghqRpwLPAxcDbXkqQnSIiJN0LXEjS5nIZcHtfrtmfTp6UDEx87JlNTiRmVjJ66rU1Efgk8DLga8DrgNaIuD8i7u/twmmN4QMkXYefAG6JiOWSrpV0HoCkOZKagYuA6yUt7zxf0gPAz4C/kNQs6Zx018eBD0tqImkz+c+Du+XiGTViKMdOHOsR7mZWUnpqI2knmeX3d5JGAJcA90m6NiK+keXiEXEncGeXsmty3s8neTyV79wzuylfRdIjbFCaNaWGXz62lvaOoMIrJppZCehxHImkEZIuAH5EMvPv14Fb+yOwUjWrsZbte9pYsX5b7webmQ0CPTW2f5/ksdZvgX+OiGX9FlUJ65wJeNEzmzl24tgiR2Nm1nc91UjeARwNXAn8QdLW9LVN0tb+Ca/0TOkcmOjxJGZWInpqI8kyD5cdJEnMavTARDMrHU4WRXBKYy2rWnaweefeYodiZtZnTiRFsH/FRHcDNrMS4ERSBCdPrqZiiPx4y8xKghNJEYwcPpRjJ45xIjGzkuBEUiSzGmtZvHoz7V4x0cwGOSeSIpk1pYYde9v58/MemGhmg5sTSZHsH5jox1tmNsg5kRRJ47iRjB81nEXPuOeWmQ1uTiRFIolTGmt5zDUSMxvknEiKaNaUGla17mDTDg9MNLPBy4mkiPYPTFzjWomZDV5OJEV00qR0YKLbScxsEHMiKaKRw4dyXL0HJprZ4OZEUmSzGmt5fI0HJprZ4FXQRCLpXElPSmqSdHWe/WdJWiSpTdKFXfZdJmlF+rosp/y+9JqL09dhhbyHQpszdRw79rZz9x+fK3YoZmYvScESiaQK4Drg9cDxwCWSju9y2GrgcuCmLueOAz4NvJxkffZPS6rNOeTSiJiZvtYX6Bb6xbkvm8hx9WP59B3L2bZ7X7HDMTM7aIWskcwFmiJiVUTsBW4Gzs89ICKejoglQEeXc88B7o6IjRGxCbgbOLeAsRbNsIohfO6CE1m/bQ9fvOvJYodjZnbQCplIjgDW5Gw3p2WH4twb08da/yhJ+S4g6QpJCyQtaGlpOZi4+93Jk2u47LSp/PDhZ9zwbmaDTiETSb4v+Kwtyj2de2lEnAicmb7eke8CEXFDRMyOiNkTJkzI+LHFc9U5xzBxbCWf+MVS9rV3raCZmQ1chUwkzcDknO1JwNq+nhsRz6Y/t5G0rcztc6QDwOgRQ/mX81/Gk89v44b/XVXscMzMMitkIpkPzJA0TdJw4GLgjozn3gWcLak2bWQ/G7hL0lBJdQCShgFvBJYVIPaieO3xh/OGEyfytXtW8FTrjmKHY2aWScESSUS0AR8gSQpPALdExHJJ10o6D0DSHEnNwEXA9ZKWp+duBP6FJBnNB65Ny0aQJJQlwGLgWeA7hbqHYvj0X53AiIoh/MNtS4nw2BIzG/hUDl9Ws2fPjgULFhQ7jMx+9PAzfOqXy/jSRSfz5lMnFTscMytTkhZGxOzejvPI9gHobXMbOXVKLf/6mz+yYfueYodjZkW2Y08bDza1svCZjazftnvAPa0YWuwA7MWGDBH/dsGJ/OXXH+Azv3mCL791ZrFDMrN+tK+9g8fXbObBpg082NTKotWbaMuZRqlqWAWTx1XROG4kk8eNpDF9TR43ksm1I6kaXtGv8TqRDFBHHz6G977yKL7xP0389awjOHPGwO/CbGYvTUTw5+e382BTKw82tfLwqg3s2NuOBC9rqOb/nHkkpx01no6OYPXGnftfazbu5A8rN7Bzb/sB15swZsT+5PKRs49mUu3IgsbvRDKAvf/V0/n1knX8w23LuOtDZ/X7XxlmVjhrN+/anzgeXLmBlm3JY+xpdaN40ylHcMb0Ok47ajw1I4f3eJ2IYOOOvQcklzUbd7F6404efWojFUPyjtk+pJxIBrDKYRV89q9P5JLvPMzX7lnB1a8/ttghmVlGHR1By/Y9yRf8hhe+5Du/8NeniaNu9HBOn17H6UfVMW/6+IOuPUhi/OgRjB89glMaa3s/oQCcSAa4044az1tmT+I7D6zi/JkNHFc/ttghWYl7bstu1m7ZxQkNYxkx1LXg3mzf08ZDKzfwzIYdSW1g0679SWNP2wuzVEjQUF3F5HFVvOqYCRx9+BjOmFHHMYePoZuZngYNJ5JB4JNvOI57nljP1bcu5db3zeuXqqqVjy279vHwqg37H7OsbEkGw1YNq2DOtHGcMX08846q4/j6sQzx7x6QPE5a0ryFm+ev5o7Fa9mRtlGMGTGUxvEjmT5hNK859rADGsIbaipLNjE7kQwCNSOHc81fHc+VNy/mhw89zeWnTyt2SDaI7d7XzqJnNvH79Nn80ubNdESSOF5+5DguntPIEbVVPPrURh5sauWzd/4JgNqRw5h3VB2nT6/jjOl1NI7P/ggmItiya98BDcXPbtpFW3v+bqxZ/0Dv2gs28kznN3FsJfOm13FKY02fv8i37t7H7Y89y02PruGJdVupGlbBX51czwWzJnHsxDFUVw0b9LWLl8IDEgeJiOCyG+ez8OmN3P3hV9JQU1XskGyQaO8Ilq/dwu+bWvlD0wbmP72RPW0dVAwRp0yuYV6aGGZOrmH40BcPLXt+6+60tpLUWp7buhuASbVVnDG9jnnT65h31HjGVg7j2c27Dmj03d82sGkn23a3HXDd2pHD8n6x50sG+/fFi5OMuszxmrs/AtZv270/Uc6dNo7Tp4/n9Ol1HDcxWw0rIli0ehM3PbKG3yxdy+59HZzQMJZL5jZy/swGxlQO6/Uag1XWAYlOJIPImo07ed1X7ueM6RP4zjtPHRB/+bS1d/B48xYebGrl902tjBpewWcvOJH6aie6/rSvvYO1OV/iuQ27T7fuZPue5Ev82IljmHdUHWfMGM/caeMZPeLgHkpEBKtad+x/DPaHlRv2JwjpwBrC8KFDmFxbdcAYh8ZxI2kcn4x1GHWQn/1Sbdm1j0c6H92t3EDT+u0AjBs1nHlHjeeM6Ukta/K4A2tYm3bs5dbHnuXmR1ezYv12Ro8YynkzG7hkTiMnTqrul9iLzYkkR6kkEoDr71/Jv/32T3zr0lm8/sT6fv/8iGDF+u38fkUrf1jZysOrNrJ9TxsSnNAwlqdadlA1vIJvvm0WrzhyfL/HV8o6/9s/+dy2F/UAWrt5Fx25X+IVQ5hUW8XkcSOZMn4kp06pZd5RdUwYM+KQxtTeESx9dgt/WNnK7n0dTMlJGIeNGTEg21Se27I7p9ttK89vTXpPNY4byenk+j+tAAAOgElEQVTpI7AHm1r57bLn2NvWwczJNVwydzJvPKmh35LfQOFEkqOUEklbewfnffNBVrVu52PnHMvl86YW/B9rd/3dp44fuf95eWd/96b127jihwt5ZsNOPvmG4/ib06cOiJrTYPXs5l08uCL5wnuwaQOtOVPm1I0eQeO4A//i7/wSP3xspTtlZBARrGxJ/jB6cOUGHl65gW172hhTOZQLTjmCi+c2lnVPSSeSHKWUSCB5Zv2JW5fyP39az+wptXzhwpM4csLoQ/oZTeu388OHnuaBplZWpb14xo8avj9x9NTffdvufXz4lse5+4/Pc/7MBj53wUkeTJnRph17eSinB9XTG3YCSdI4Y/p45k2v46RJ1f36aKictLV30NSynSnjRvl3FieSA5RaIoHkL6lbFz3LP/9qOXvaOvjI2Ufz7jOO7PNfoWs37+Jr/72Cny1cw/ChQzjtyKRh8vTpSX/3rLWfjo7gP+5r4kt3/5ljJ47l+refelC9fMrFrr3tzH96Y1rjaGX52q1EJAudveLIcWl7Rh0zDhvtmp31OyeSHKWYSDqt37qbT962jP9+4nlmTq7h3y86iemHjTno62zcsZf/uLeJHzz8DARc+opG3v/q6dSN7tsz9XufXM+VP3kMSXz9klN45dHlPWdYW3sHS5/dsr8X1MJnNrG3vYNhFWJWY+3+pH3SpGqGVXhybisuJ5IcpZxIIKmd3PH4Wj59x3J27m3nQ6+dwRVnHsnQDF9E2/e08d0HVvHdB55i59423jxrEle+dsYhneTtmQ07+NsfLuTJ57dx1dnH8HevOmpA/3UdkUyMFwENNVV5u8QezLVWtrzQy+mhVS/0cjq+fuz+rqhzp41j5HA/qrKBxYkkR6knkk4t2/Zwze3L+O2y5zhpUjVfvPBkjpmYv3aye187P35kNdfd28TGHXs594SJXHXO0S+pNpPFzr1tXP2Lpdzx+FrOOeFwvvSWmQfd9bSQWrbt4Q8rW5NG16ZW1m5JxkoMEdSn01o0plN0N45/YbruutHDX5QU12/dzYMrW/n9ihePuzhzRlLjOO3I8YzvY23PrNAGRCKRdC7wNaAC+G5EfK7L/rOArwInARdHxM9z9l0GfCrd/NeI+H5afirwPaAKuBO4Mnq5iXJJJJ1+s2Qd/3j7Mrbt3sffv2YG733VUfsfk7S1d3DrY8/ytf9ewbObd3H69PF89JxjmTm5puBxRQT/+fun+Lff/omp40dywztnc9Qh7iSQ1Y49bTz61MZkdHdTK396bhsA1VXDOD2dEmTE0CGs2bRrfzfbNTkT7XXKXReiduRwFq/ZzIp0nELtyGHMSyfjO9iR4GYDQdETiaQK4M/A64BmkrXXL4mIP+YcMxUYC1wF3NGZSCSNAxYAs4EAFgKnRsQmSY8CVwIPkySSr0fEb3uKpdwSCcCG7Xv49B3L+fWSdZzQMJYvXHgSazbu5It3PcnKlh2cPKmaj517LKdPr+v32P6wspUP3vQYe9o6+PJbTubsEyYW/DP3tXewpHnz/lpC50JBw4cOYe7Ucft7ox3fMLbHDgu79rbTvCkZqZ2M2t7Fmk0vJJkTGsbuH+DmualssBsIieQ04J8i4px0+xMAEfFveY79HvDrnERyCfCqiPjbdPt64L70dW9EHJvvuO6UYyLp9Ltl6/jUL5fRun0vANMPG81VZx/NOSdMLGo7xdrNu3jvjxaypHkL0+pGHTDJRdffyIP5He3unlq27dk/cPLEI6r3J45Tp9RSOczdPM3yyZpICvmQ+ghgTc52M/DyPpx7RPpqzlNu3Tj3ZfW8fNp4/uvBp2gcN5ILZk0aEAPVGmqquOVvT+O6e5v2j5XI1TXCLDmvp3xTXTWMeUeNz7RQkJkdnEImknz/9LP+adnduZmvKekK4AqAxsbGjB9bmmpHDecjZx9T7DBepHJYxYCMy8wOTiE7qjcDk3O2JwFr+3huc/q+12tGxA0RMTsiZk+YUN5jF8zMCqmQiWQ+MEPSNEnDgYuBOzKeexdwtqRaSbXA2cBdEbEO2CbpFUoehr8TuL0QwZuZWTYFSyQR0QZ8gCQpPAHcEhHLJV0r6TwASXMkNQMXAddLWp6euxH4F5JkNB+4Ni0DeB/wXaAJWAn02GPLzMwKywMSzcwsr6y9tjyZj5mZ9YkTiZmZ9YkTiZmZ9YkTiZmZ9UlZNLZLagGe6eWwOqC1H8IZiMr53qG879/3Xr6y3P+UiOh1IF5ZJJIsJC3I0juhFJXzvUN537/vvTzvHQ7t/fvRlpmZ9YkTiZmZ9YkTyQtuKHYARVTO9w7lff++9/J1yO7fbSRmZtYnrpGYmVmflH0ikXSupCclNUm6utjxFJqk/5K0XtKynLJxku6WtCL9WVvMGAtF0mRJ90p6QtJySVem5SV//5IqJT0q6fH03v85LZ8m6ZH03n+aztRdsiRVSHpM0q/T7bK4f0lPS1oqabGkBWnZIfu9L+tEkq4rfx3weuB44BJJxxc3qoL7HnBul7KrgXsiYgZwT7pditqAj0TEccArgPen/7/L4f73AK+JiJOBmcC5kl4BfB74Snrvm4B3FzHG/nAlyWzkncrp/l8dETNzuvwest/7sk4kwFygKSJWRcRe4Gbg/CLHVFAR8b/Axi7F5wPfT99/H3hTvwbVTyJiXUQsSt9vI/lCOYIyuP9IbE83h6WvAF4D/DwtL8l77yRpEvCXJMtQkK5pVDb3n8ch+70v90TS3drw5ebwdNEw0p+HFTmegpM0FTgFeIQyuf/0sc5iYD1wN8l6PpvTtYOg9H//vwp8DOhIt8dTPvcfwP+TtDBdhhwO4e99IddsHwz6sq68DVKSRgO/AD4UEVuTP0xLX0S0AzMl1QC3AcflO6x/o+ofkt4IrI+IhZJe1Vmc59CSvH/g9IhYK+kw4G5JfzqUFy/3Gklf1pUvJc9LqgdIf64vcjwFI2kYSRL5cUTcmhaXzf0DRMRm4D6SdqIaSZ1/UJby7//pwHmSniZ5hP0akhpKWdx/RKxNf64n+SNiLofw977cE0lf1pUvJXcAl6XvLwNuL2IsBZM+E/9P4ImI+HLOrpK/f0kT0poIkqqA15K0Ed0LXJgeVpL3DhARn4iISRExleTf+f9ExKWUwf1LGiVpTOd74GxgGYfw977sByRKegPJXyYVwH9FxGeKHFJBSfoJ8CqSmT+fBz4N/BK4BWgEVgMXRUTXBvlBT9IZwAPAUl54Tv5JknaSkr5/SSeRNKhWkPwBeUtEXCvpSJK/0McBjwFvj4g9xYu08NJHW1dFxBvL4f7Te7wt3RwK3BQRn5E0nkP0e1/2icTMzPqm3B9tmZlZHzmRmJlZnziRmJlZnziRmJlZnziRmJlZnziR2IAlKSR9KWf7Kkn/dIiu/T1JF/Z+ZJ8/56J0tuF7u5RPTe/vgzll35R0eS/Xe6+kd/ZyzOWSvtnNvu35yg+V9L5yZ5Z+j6RFpTijsr3AicQGsj3ABZLqih1IrnTW6KzeDfxdRLw6z771wJUHM3V5RHw7In5wEJ9/yOSMAM96/DuADwJnR8SmwkRlA4ETiQ1kbSTLgf7frju61ig6/9KW9CpJ90u6RdKfJX1O0qXpWhxLJR2Vc5nXSnogPe6N6fkVkr4oab6kJZL+Nue690q6iWRAY9d4Lkmvv0zS59Oya4AzgG9L+mKe+2shmb77sq47JB0l6XfpJHsPSDo2Lf8nSVel7+ekMT6Uxrws5xIN6fkrJH2hy7W/lNYS7pE0IS2bKenh9Hq3ddYgJN0n6bOS7idJehel9/i4pP/Nc0+dn/EWkmnJz46I1u6Os9LgRGID3XXApZKqD+Kck0nWnTgReAdwdETMJZk+/IM5x00FXkkytfi3JVWS1CC2RMQcYA7wHknT0uPnAv8QEQesWSOpgWRdi9eQrPUxR9KbIuJaYAFwaUR8tJtYPwd8JE8t5wbggxFxKnAV8B95zr0ReG9EnAa0d9k3E3hr+t/grZI655QbBSyKiFnA/SQzGwD8APh4RJxEkig/nXOtmoh4ZUR8CbgGOCdd1+S8bu5pCvBNkiTyXDfHWAlxIrEBLSK2knzJ/f1BnDY/XXtkD8lU6f8vLV9Kkjw63RIRHRGxAlgFHEsyD9E7lUy3/gjJVOMz0uMfjYin8nzeHOC+iGhJpyT/MXBWxvt7CngUeFtnmZLZiecBP0vjuB6ozz0vnTdrTET8IS26qcul74mILRGxG/gjyZc7JFPD/DR9/yPgjDRJ10TE/Wn597vE/9Oc9w8C35P0HpLpVvJpIZly4y3d3riVlHKfRt4Gh68Ci0j+Au/URvqHUDoZY247Q+5cSR052x0c+DvfdX6gIJla/IMRcVfujnR+ph3dxNfXeeg/S7K4UuejoiEk62TM7OGc3j4z979BO93/W88yR9L++46I90p6OUktbrGkmRGxocvxO0lWHf29pPUR8eMMn2GDmGskNuClE8ndwoHLoD4NnJq+P59kxb+DdZGkIWm7yZHAk8BdwPuUTDePpKPTGVN78gjwSkl16SOqS0geG2USEX8iqTW8Md3eCjwl6aI0Bkk6ucs5m4BtSpbLhWRG2yyG8MJst28Dfh8RW4BNks5My9/RXfySjoqIRyLiGqCVA5dhyI2vhWRJ589KOidjbDZIuUZig8WXgA/kbH8HuF3SoyQN1t3VFnryJMkX5uEkbQ27JX2X5PHXorSm00IvS5BGxDpJnyCZklzAnRFxsFNyf4Zk9tlOlwLfkvQpkiR5M/B4l3PeDXxH0g6S9UW2ZPicHcAJkhamx781Lb+MpJ1oJMljvnd1c/4XJc0guc978sS0X0Q8Jek84E5JF0TEIxnis0HIs/+aDVKSRneuwy7paqA+Iq4sclhWhlwjMRu8/jKtCQ0FngEuL244Vq5cIzEzsz5xY7uZmfWJE4mZmfWJE4mZmfWJE4mZmfWJE4mZmfWJE4mZmfXJ/wdyeAPD60wrDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# changing to misclassification error\n",
    "MSE = [1 - x for x in cv_scores]\n",
    "\n",
    "# determining best k\n",
    "optimal_k = neighbors[MSE.index(min(MSE))]\n",
    "print(f\"The optimal number of neighbors is {optimal_k}\")\n",
    "\n",
    "# plot misclassification error vs k\n",
    "plt.plot(neighbors, MSE)\n",
    "plt.xlabel('Number of Neighbors K')\n",
    "plt.ylabel('Misclassification Error')\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Classification Report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "       -1.0       0.92      0.97      0.94      1190\n",
      "        1.0       0.63      0.38      0.47       167\n",
      "\n",
      "avg / total       0.88      0.90      0.88      1357\n",
      "\n",
      "Confusion Metrics: \n",
      " [[1153   37]\n",
      " [ 104   63]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy of random forest 89.61%\n"
     ]
    }
   ],
   "source": [
    "# Running knn with n = 9\n",
    "\n",
    "# from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors = 9)\n",
    "knn.fit(x_train, y_train)\n",
    "predicted_label = knn.predict(x_test)\n",
    "\n",
    "# from sklearn.metrics import classification_report\n",
    "\n",
    "report = classification_report(y_test, predicted_label)\n",
    "print(f' Classification Report\\n{report}')\n",
    "\n",
    "# from sklearn import metrics\n",
    "\n",
    "labels=[-1,1]\n",
    "cm = metrics.confusion_matrix(y_test, predicted_label, labels)\n",
    "print(f'Confusion Metrics: \\n {cm}')\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(cm)\n",
    "plt.title('Confusion matrix of the classifier')\n",
    "fig.colorbar(cax)\n",
    "ax.set_xticklabels([''] + labels)\n",
    "ax.set_yticklabels([''] + labels)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()\n",
    "\n",
    "score=knn.score(x_test,y_test)*100\n",
    "print(f'Test Accuracy of random forest {round(score,2)}%')"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}