KNN Final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing basic libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn.datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading in the iris dataset\n",
    "dat=sklearn.datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
      "['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "#Checking the feature and target names\n",
    "print(dat.feature_names)\n",
    "print(dat.target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the data into training and testing datas\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#20% of the data is split into testing\n",
    "# a = feature_data for training\n",
    "# c = target_data for training\n",
    "# b = feature_data for testing\n",
    "# d = target_data for testing\n",
    "a,c,b,d=train_test_split(dat.data,dat.target,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the classifier with a K value of 17\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model=KNeighborsClassifier(n_neighbors=17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=17, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#making predictions\n",
    "pred=model.predict(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9333333333333333\n"
     ]
    }
   ],
   "source": [
    "#evaluating the accuracy of the model\n",
    "print(accuracy_score(d,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here we want to see how the algorithm behaves with different K values and choose the optimal value accordingly\n",
    "score=[]\n",
    "for k in range(1,100,2):\n",
    "    model=KNeighborsClassifier(n_neighbors=k)\n",
    "    model.fit(a,b)\n",
    "    pred=model.predict(c)\n",
    "    score.append(accuracy_score(d,pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9333333333333333, 0.9333333333333333, 0.9, 0.9, 0.9333333333333333, 0.9333333333333333, 0.9666666666666667, 0.9666666666666667, 0.9333333333333333, 0.9333333333333333, 0.9666666666666667, 0.9333333333333333, 0.9333333333333333, 0.9666666666666667, 0.9666666666666667, 0.9333333333333333, 0.9333333333333333, 0.9666666666666667, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9666666666666667, 1.0, 0.9666666666666667, 0.9666666666666667, 0.9666666666666667, 0.9333333333333333, 0.9333333333333333, 0.9666666666666667, 0.9, 0.9, 0.9, 0.8, 0.6666666666666666, 0.6, 0.6, 0.6333333333333333, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.5666666666666667, 0.5333333333333333]\n"
     ]
    }
   ],
   "source": [
    "#Score is a list with the accuracy scores for different values of K\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'accuracy')"
      ]
     },
     "execution_count": 26,
     "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": [
    "#Visualising the trend of accuracy with varying values of K\n",
    "plt.plot(range(1,100,2),score)\n",
    "plt.xlabel('K range')\n",
    "plt.ylabel('accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Values of K in the range of 39 - 50 provide a good accuracy."
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}