Simi_ML_KNN_iris,Classnotes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5fbc4544",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "36e2b501",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal.length</th>\n",
       "      <th>sepal.width</th>\n",
       "      <th>petal.length</th>\n",
       "      <th>petal.width</th>\n",
       "      <th>variety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal.length  sepal.width  petal.length  petal.width    variety\n",
       "0             5.1          3.5           1.4          0.2     Setosa\n",
       "1             4.9          3.0           1.4          0.2     Setosa\n",
       "2             4.7          3.2           1.3          0.2     Setosa\n",
       "3             4.6          3.1           1.5          0.2     Setosa\n",
       "4             5.0          3.6           1.4          0.2     Setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  Virginica\n",
       "146           6.3          2.5           5.0          1.9  Virginica\n",
       "147           6.5          3.0           5.2          2.0  Virginica\n",
       "148           6.2          3.4           5.4          2.3  Virginica\n",
       "149           5.9          3.0           5.1          1.8  Virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = pd.read_csv('iris.csv')\n",
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4435e51",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = iris.iloc[: , 0:4].values\n",
    "y = iris.iloc[: ,  4:5].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7d006fae",
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a88082ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica']], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "65d39ff1",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain, xtest, ytrain, ytest = train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "da63aa9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4e12b985",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ytest = pd.DataFrame(ytest)\n",
    "# ytest[0] = ytest[0].map({'Setosa':1, 'Versicolor':2, 'Virginica':3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "64029442",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ytest[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b73be382",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Versicolor'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Versicolor'],\n",
       "       ['Virginica'],\n",
       "       ['Versicolor'],\n",
       "       ['Setosa'],\n",
       "       ['Versicolor'],\n",
       "       ['Virginica'],\n",
       "       ['Versicolor'],\n",
       "       ['Setosa'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Setosa'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Versicolor'],\n",
       "       ['Virginica'],\n",
       "       ['Setosa'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Setosa'],\n",
       "       ['Virginica'],\n",
       "       ['Versicolor'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Versicolor'],\n",
       "       ['Virginica'],\n",
       "       ['Setosa'],\n",
       "       ['Virginica'],\n",
       "       ['Setosa'],\n",
       "       ['Versicolor'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Virginica'],\n",
       "       ['Versicolor'],\n",
       "       ['Virginica'],\n",
       "       ['Setosa'],\n",
       "       ['Versicolor'],\n",
       "       ['Setosa'],\n",
       "       ['Virginica'],\n",
       "       ['Versicolor'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Setosa'],\n",
       "       ['Versicolor'],\n",
       "       ['Setosa'],\n",
       "       ['Virginica'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Virginica'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Setosa'],\n",
       "       ['Versicolor'],\n",
       "       ['Setosa'],\n",
       "       ['Versicolor'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Virginica'],\n",
       "       ['Setosa'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Setosa'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Virginica'],\n",
       "       ['Versicolor'],\n",
       "       ['Setosa'],\n",
       "       ['Virginica'],\n",
       "       ['Versicolor'],\n",
       "       ['Setosa'],\n",
       "       ['Virginica'],\n",
       "       ['Virginica'],\n",
       "       ['Versicolor'],\n",
       "       ['Virginica'],\n",
       "       ['Versicolor'],\n",
       "       ['Setosa'],\n",
       "       ['Virginica'],\n",
       "       ['Versicolor'],\n",
       "       ['Virginica'],\n",
       "       ['Setosa'],\n",
       "       ['Setosa'],\n",
       "       ['Versicolor'],\n",
       "       ['Setosa'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Versicolor'],\n",
       "       ['Setosa']], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a0c3ae1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(xtrain,ytrain)\n",
    "p=knn.predict(xtest)\n",
    "acc=accuracy_score(ytest,p)\n",
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4595a885",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 1 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 3 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 5 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 7 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 9 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 11 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 13 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 15 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 17 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 19 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 21 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 23 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 25 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 27 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 29 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 31 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 33 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 35 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 37 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 39 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 41 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Versicolor' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 43 is  1.0\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Virginica' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 45 is  0.9666666666666667\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Virginica' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Virginica' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 47 is  0.9666666666666667\n",
      "*********predicted data*********\n",
      "['Virginica' 'Setosa' 'Setosa' 'Virginica' 'Virginica' 'Setosa' 'Setosa'\n",
      " 'Versicolor' 'Virginica' 'Virginica' 'Setosa' 'Setosa' 'Virginica'\n",
      " 'Setosa' 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Virginica' 'Setosa'\n",
      " 'Versicolor' 'Setosa' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
      " 'Setosa' 'Versicolor' 'Versicolor' 'Virginica']\n",
      "*********real data*********\n",
      "[['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Versicolor']\n",
      " ['Setosa']\n",
      " ['Setosa']\n",
      " ['Versicolor']\n",
      " ['Virginica']\n",
      " ['Virginica']]\n",
      "Accuracy score for 49 is  0.9333333333333333\n",
      "BestK  :  1\n",
      "BestScore  :  1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "bestK = -1\n",
    "bestScore = -1\n",
    "for x in range(1,50,2):\n",
    "    knn = KNeighborsClassifier(n_neighbors=x)\n",
    "    knn.fit(xtrain,ytrain)\n",
    "    p=knn.predict(xtest)\n",
    "    \n",
    "#     p=pd.DataFrame(p)\n",
    "#     p[0] = p[0].map({'Iris-setosa':1, 'Iris-versicolor':2, 'Iris-virginica':3})\n",
    "    print('*********predicted data*********')\n",
    "    print(p)\n",
    "    print('*********real data*********')\n",
    "    print(ytest)\n",
    "    accuracy = accuracy_score(p, ytest)\n",
    "    print(f'Accuracy score for {x} is ',accuracy)\n",
    "    if(accuracy > bestScore):\n",
    "        bestScore = accuracy\n",
    "        bestK = x\n",
    "        \n",
    "print('BestK  : ', bestK)\n",
    "print('BestScore  : ', bestScore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c97177e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64c93e05",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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