K-Neighbors Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (800, 11) (800,)\n",
      "Test set: (200, 11) (200,)\n",
      "Train set Accuracy:  0.5475\n",
      "Test set Accuracy:  0.32\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best accuracy was with 0.34 with k= 9\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import NullFormatter as nf\n",
    "import matplotlib.ticker as tck\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing as pr\n",
    "%matplotlib inline\n",
    "\n",
    "#to read the csv file\n",
    "df = pd.read_csv('teleCust1000t.csv')\n",
    "df.head()\n",
    "\n",
    "#to check the counts of classes\n",
    "df['custcat'].value_counts()\n",
    "\n",
    "#exploring data\n",
    "df.hist(column='income', bins=50)\n",
    "\n",
    "#defining feature sets\n",
    "df.columns\n",
    "\n",
    "#creating an array using attr\n",
    "X = df[['region', 'tenure','age', 'marital', 'address', 'income', 'ed', 'employ','retire', 'gender', 'reside']] .values\n",
    "X[0:5]\n",
    "\n",
    "#our labels\n",
    "y = df['custcat'].values\n",
    "y[0:5]\n",
    "\n",
    "#normalizing the data\n",
    "X = pr.StandardScaler().fit(X).transform(X.astype(float))\n",
    "X[0:5]\n",
    "\n",
    "#train test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)\n",
    "\n",
    "#importing neighbors classifier\n",
    "from sklearn.neighbors import KNeighborsClassifier as knc\n",
    "\n",
    "#starting the algortihm with k=4 for now\n",
    "k = 4\n",
    "#Train Model and Predict \n",
    "neigh = knc(n_neighbors = k).fit(X_train,y_train)\n",
    "neigh\n",
    "\n",
    "#using model to predict test set\n",
    "yhat = neigh.predict(X_test)\n",
    "yhat[0:5]\n",
    "\n",
    "#finding acciracy for train and test set\n",
    "from sklearn import metrics\n",
    "print(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh.predict(X_train)))\n",
    "print(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat))\n",
    "\n",
    "Ks = 10\n",
    "mean_acc = np.zeros((Ks-1))\n",
    "std_acc = np.zeros((Ks-1))\n",
    "ConfustionMx = [];\n",
    "for n in range(1,Ks):\n",
    "    \n",
    "    #Train Model and Predict  \n",
    "    neigh = knc(n_neighbors = n).fit(X_train,y_train)\n",
    "    yhat=neigh.predict(X_test)\n",
    "    mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)\n",
    "\n",
    "    \n",
    "    std_acc[n-1]=np.std(yhat == y_test)/np.sqrt(yhat.shape[0])\n",
    "\n",
    "mean_acc\n",
    "\n",
    "#plotting graph\n",
    "plt.plot(range(1,Ks),mean_acc,'g')\n",
    "plt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\n",
    "plt.legend(('Accuracy ', '+/- 3xstd'))\n",
    "plt.ylabel('Accuracy ')\n",
    "plt.xlabel('Number of Neighbors (K)')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print( \"The best accuracy was with\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1) "
   ]
  },
  {
   "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": "3.7.6"
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 },
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}