ML8.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply k-NN Classification technique to solve given problem: In  the  following  diagram  let  blue  circles\n",
    "# indicate  positive  examples  and  orange  squares indicate  negative  examples.  \n",
    "# We  want  to  use  k-NN  algorithm  for  classifying  the  points.  If k=3, find the class of the point (6,6). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 4],\n",
       "       [6, 2]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([[2,4],[4,2],[4,6],[6,4],[4,4],[6,2]])\n",
    "y = np.array([\"negative\",\"negative\",\"negative\",\"negative\",\"positive\",\"positive\"])\n",
    "\n",
    "negative = x[:4]\n",
    "positive = x[4:]\n",
    "negative\n",
    "positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(negative[:,0],negative[:,1],label='negative',c='orange')\n",
    "plt.scatter(positive[:,0],positive[:,1],label='positive',c='blue')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# k = 3\n",
    "\n",
    "clf = KNeighborsClassifier(n_neighbors=3)\n",
    "clf.fit(x,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['negative'], dtype='<U8')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test = np.array([6,6])\n",
    "y_pred = clf.predict([x_test])\n",
    "y_pred"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "e4cce46d6be9934fbd27f9ca0432556941ea5bdf741d4f4d64c6cd7f8dfa8fba"
  },
  "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}