03- KNN Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dba0ab5a-d7e4-457f-b110-cb655b56943e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88399def-3b91-4aef-b533-8e5eb634e98b",
   "metadata": {},
   "source": [
    "### data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d39fa772-3e4c-4700-9e4f-c736ed026d16",
   "metadata": {},
   "outputs": [],
   "source": [
    "#banana width = 4\n",
    "#banana length = 8\n",
    "#apple width =6\n",
    "#apple length =6\n",
    "\n",
    "N = 400\n",
    "std = 0.5\n",
    "bananas_width = np.random.normal(4, std, N)\n",
    "bananas_length = np.random.normal(8, std, N)\n",
    "\n",
    "apples_width = np.random.normal(6, std, N)\n",
    "apples_length = np.random.normal(6, std, N)\n",
    "\n",
    "width = np.concatenate((bananas_width, apples_width))\n",
    "length = np.concatenate((bananas_length, apples_length))\n",
    "\n",
    "X = np.array([width, length]).T\n",
    "\n",
    "bananas_labels = np.zeros(N, dtype = 'int')\n",
    "apples_labels = np.ones(N, dtype = 'int')\n",
    "\n",
    "Y = np.concatenate((bananas_labels, apples_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "178967c5-7689-4978-be53-b6cdf664761b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Length')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[0 : N, 0], X[0 : N , 1],  label='bananas', c='orange')\n",
    "plt.scatter(X[N :, 0], X[N: , 1],  label='apples', c='red')\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('Width')\n",
    "plt.ylabel('Length')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c11405f-7d4c-449b-b285-6ae9cd879892",
   "metadata": {},
   "source": [
    "### KNearestNeighbore classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fa26da5b-dea7-4228-8bc6-e1f0d951fb6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class KNearestNeighbore():\n",
    "    def __init__(self, k):\n",
    "        self.k = k\n",
    "    \n",
    "    \n",
    "    def fit(self, X_train, y_train):\n",
    "        self.X_train = X_train\n",
    "        self.y_train = y_train\n",
    "        self.number_classes = len(np.unique(y_train))\n",
    "    \n",
    "    def nearNeighbors(self, x_test):\n",
    "        distance = np.sqrt(np.sum((x_test- self.X_train)**2, axis = 1))\n",
    "        near_neighbors = np.argsort(distance)[:self.k]\n",
    "        \n",
    "        return near_neighbors\n",
    "    \n",
    "    def predict(self, x_test):\n",
    "        near_neighbors = self.nearNeighbors(x_test)\n",
    "        predict_label = np.argmax(np.bincount(self.y_train[near_neighbors]))\n",
    "            \n",
    "        return predict_label\n",
    "    \n",
    "    def evaluate(self, X_test, y_test):\n",
    "        self.X_test = X_test\n",
    "        self.Y_test = y_test\n",
    "        y_pred = []     \n",
    "        \n",
    "        for i in range(len(X_test)):\n",
    "            y_pred.append(self.predict(X_test[i]))\n",
    "            \n",
    "        true_label = np.count_nonzero(y_pred == y_test)\n",
    "        accuracy = (true_label / len(y_pred))*100\n",
    "        \n",
    "        return accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01dc7bc8-6bb9-4b63-908b-702208f522da",
   "metadata": {},
   "source": [
    "## test and train split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0a57ef37-0644-46ec-8db2-610c04f80cc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8e8bc55-814a-4f08-b75e-afc61bf34290",
   "metadata": {},
   "source": [
    "### fit, predict and evaluating model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "daddbd0d-522e-4df1-bd11-f621046fce92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.375\n"
     ]
    }
   ],
   "source": [
    "knn = KNearestNeighbore(3)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "accuracy = knn.evaluate(X_test, y_test)\n",
    "\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2de32c48-b9b6-4e30-a88c-9e0e92dbed24",
   "metadata": {},
   "source": [
    "### fit, predict and evaluating model using sklearn KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "030eca7c-1588-45f7-950a-6c7f35c2fd46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.375\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "classifier = KNeighborsClassifier(n_neighbors = 3)\n",
    "classifier.fit(X, Y)\n",
    "\n",
    "y_pred = classifier.predict(X_test)\n",
    "accuracy = np.count_nonzero(y_pred == y_test) / len(y_test) * 100\n",
    "print(accuracy)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea3d5540-b33d-4212-b66a-88f17722219a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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