KNN_Iris.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score  # for calculating accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading iris data set\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading into variable\n",
    "iris_data=load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'filename', 'target', 'target_names']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describing iris data internally\n",
    "dir(iris_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#target output\n",
    "iris_data.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# attribute of features of given data\n",
    "iris_data.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [4.9, 3.6, 1.4, 0.1],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data with attributes\n",
    "iris_features=iris_data.data\n",
    "iris_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extracting label as per features\n",
    "label=iris_data.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# separating data into training and testing\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#seperating data\n",
    "#train_data,test_data,label_train,label_test = train_test_split(iris_features,label,test_size=0.2)  # 80% training data\n",
    "\n",
    "#train_data,test_data,label_train,label_test = train_test_split(iris_features,label,test_size=0.1)   # 90% training data\n",
    "\n",
    "train_data,test_data,label_train,label_test = train_test_split(iris_features,label,test_size=0.3)  # 70% training data   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing knn classifier\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## k=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "kclf3=KNeighborsClassifier(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now training data\n",
    "ktraining3=kclf3.fit(train_data,label_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now  time for prediction\n",
    "predict_output3=ktraining3.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 0, 1, 1, 1, 1, 1, 2, 0, 2, 0, 0, 0, 1, 2, 0, 0, 2, 2,\n",
       "       1, 1, 0, 1, 1, 2, 1, 0, 2, 0, 0, 0, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2,\n",
       "       0])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_output3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 0, 1, 1, 1, 1, 1, 2, 0, 2, 0, 0, 0, 2, 2, 0, 0, 2, 1,\n",
       "       1, 1, 0, 1, 1, 2, 1, 0, 2, 0, 0, 0, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2,\n",
       "       0])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_accr3=int(accuracy_score(label_test,predict_output3)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_accr3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## k=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now calling knn\n",
    "kclf5=KNeighborsClassifier(n_neighbors=5)  # this is by default value of k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now training data\n",
    "ktraining5=kclf5.fit(train_data,label_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now  time for prediction\n",
    "predict_output5=ktraining5.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 0, 1, 1, 1, 1, 1, 2, 0, 2, 0, 0, 0, 2, 2, 0, 0, 2, 2,\n",
       "       1, 1, 0, 1, 1, 2, 1, 0, 2, 0, 0, 0, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2,\n",
       "       0])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_output5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 0, 1, 1, 1, 1, 1, 2, 0, 2, 0, 0, 0, 2, 2, 0, 0, 2, 1,\n",
       "       1, 1, 0, 1, 1, 2, 1, 0, 2, 0, 0, 0, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2,\n",
       "       0])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_accr5=int(accuracy_score(label_test,predict_output5)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_accr5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## k=7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "kclf7=KNeighborsClassifier(n_neighbors=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "ktraining7=kclf7.fit(train_data,label_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_output7=ktraining7.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 0, 1, 1, 1, 1, 1, 2, 0, 2, 0, 0, 0, 1, 2, 0, 0, 2, 2,\n",
       "       1, 1, 0, 1, 1, 2, 1, 0, 2, 0, 0, 0, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2,\n",
       "       0])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_output7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 0, 1, 1, 1, 1, 1, 2, 0, 2, 0, 0, 0, 2, 2, 0, 0, 2, 1,\n",
       "       1, 1, 0, 1, 1, 2, 1, 0, 2, 0, 0, 0, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2,\n",
       "       0])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_accr7=int(accuracy_score(label_test,predict_output7)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_accr7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## k=9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "kclf9=KNeighborsClassifier(n_neighbors=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "ktraining9=kclf9.fit(train_data,label_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_output9=ktraining9.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 0, 1, 1, 1, 1, 1, 2, 0, 1, 0, 0, 0, 1, 2, 0, 0, 2, 2,\n",
       "       1, 1, 0, 1, 1, 2, 1, 0, 2, 0, 0, 0, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2,\n",
       "       0])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_output9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 0, 1, 1, 1, 1, 1, 2, 0, 2, 0, 0, 0, 2, 2, 0, 0, 2, 1,\n",
       "       1, 1, 0, 1, 1, 2, 1, 0, 2, 0, 0, 0, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2,\n",
       "       0])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_accr9=int(accuracy_score(label_test,predict_output9)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_accr9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision tree classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calling decision tree classifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "dclf=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtraining=dclf.fit(train_data,label_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict=dtraining.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 0, 1, 1, 1, 1, 1, 2, 0, 2, 0, 0, 0, 1, 2, 0, 0, 2, 2,\n",
       "       1, 1, 0, 1, 1, 2, 1, 0, 2, 0, 0, 0, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2,\n",
       "       0])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "desc_accr=int(accuracy_score(label_test,predict)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc_accr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(desc_accr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting graph b/w accuracy scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compare decision and knn graph\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn2=[knn_accr3,knn_accr5,knn_accr7,knn_accr9]    # accuracy scores of knn classifier at values of k=3,5,7\n",
    "dtc=[desc_accr,desc_accr,desc_accr,desc_accr]          # accuracy scores of decision tree classfier  \n",
    "x=np.arange(len(knn2))\n",
    "#x=[3,5,7]   #x and y count values should match "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fecdcb66a90>"
      ]
     },
     "execution_count": 51,
     "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": [
    "bar_width=0.15\n",
    "\n",
    "rects1=plt.bar(x, knn2,width=bar_width, zorder=2 , label='KNN')\n",
    "rects2=plt.bar(x+bar_width, dtc, width=bar_width, zorder=2, label='Decision Tree Classifier' )\n",
    "\n",
    "plt.grid(axis='y')\n",
    "def autolabel(rects):\n",
    "    for rect in rects:\n",
    "        h = rect.get_height()\n",
    "        plt.text(rect.get_x()+rect.get_width()/2., 1.023*h, '%d'%int(h),ha='center', va='bottom')\n",
    "\n",
    "autolabel(rects1)\n",
    "autolabel(rects2)\n",
    "\n",
    "plt.xlabel('K value')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim(0,140)\n",
    "plt.xticks(x+bar_width*0.45, [3,5,7,9])\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}