IRIS flower classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d852920c",
   "metadata": {},
   "source": [
    "# Iris flower classification using KNN "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8906b6ca",
   "metadata": {},
   "source": [
    "## 1. Loading The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c9984a03",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5.1</th>\n",
       "      <th>3.5</th>\n",
       "      <th>1.4</th>\n",
       "      <th>0.2</th>\n",
       "      <th>Iris-setosa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-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>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     5.1  3.5  1.4  0.2     Iris-setosa\n",
       "0    4.9  3.0  1.4  0.2     Iris-setosa\n",
       "1    4.7  3.2  1.3  0.2     Iris-setosa\n",
       "2    4.6  3.1  1.5  0.2     Iris-setosa\n",
       "3    5.0  3.6  1.4  0.2     Iris-setosa\n",
       "4    5.4  3.9  1.7  0.4     Iris-setosa\n",
       "..   ...  ...  ...  ...             ...\n",
       "144  6.7  3.0  5.2  2.3  Iris-virginica\n",
       "145  6.3  2.5  5.0  1.9  Iris-virginica\n",
       "146  6.5  3.0  5.2  2.0  Iris-virginica\n",
       "147  6.2  3.4  5.4  2.3  Iris-virginica\n",
       "148  5.9  3.0  5.1  1.8  Iris-virginica\n",
       "\n",
       "[149 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "iris= pd.read_csv('iris.Data')\n",
    "iris"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5c3ae1b",
   "metadata": {},
   "source": [
    "##  2. Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f3089569",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>species</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>Iris-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>Iris-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>Iris-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>Iris-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>Iris-setosa</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols=[\"sepal_length\",\"sepal_width\",\"petal_length\",\"petal_width\",\"species\"]\n",
    "df=pd.read_csv(\"iris.Data\", names=cols)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "038bcc9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "75137638",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species\n",
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['species'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddee54c9",
   "metadata": {},
   "source": [
    "## 3. visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a779e78",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "76792130",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03d72368",
   "metadata": {},
   "source": [
    "## 4. splitting the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ba89232b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[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 0.1]\n",
      " [5.  3.2 1.2 0.2]\n",
      " [5.5 3.5 1.3 0.2]\n",
      " [4.9 3.1 1.5 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]]\n",
      "['Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica']\n"
     ]
    }
   ],
   "source": [
    "x= df.iloc[:,[0,1,2,3]].values\n",
    "y=df.iloc[:,4].values\n",
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1b05ccce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(105, 4)\n",
      "(45, 4)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3,random_state=0,stratify=y)\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dbd05f35",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "s=MinMaxScaler()\n",
    "x_train=s.fit_transform(x_train)\n",
    "x_test=s.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f4bcc45",
   "metadata": {},
   "source": [
    "## 5. using KNN algorithm to train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2e6c4895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn_model=KNeighborsClassifier(n_neighbors=5)\n",
    "knn_model.fit(x_train,y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a4001549",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-virginica', 'Iris-virginica', 'Iris-setosa', 'Iris-setosa',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor', 'Iris-setosa',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
       "       'Iris-versicolor', 'Iris-virginica', 'Iris-virginica',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-setosa',\n",
       "       'Iris-versicolor', 'Iris-virginica', 'Iris-setosa',\n",
       "       'Iris-versicolor', 'Iris-virginica', 'Iris-virginica',\n",
       "       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',\n",
       "       'Iris-virginica', 'Iris-versicolor', 'Iris-setosa', 'Iris-setosa',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-virginica',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-setosa', 'Iris-versicolor',\n",
       "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-setosa',\n",
       "       'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=knn_model.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ef0ed25",
   "metadata": {},
   "source": [
    "## 6. checking Accuracy via classification report and confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dd7abb75",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score , confusion_matrix , classification_report\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fac25a39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9333333333333333\n"
     ]
    }
   ],
   "source": [
    "acc=accuracy_score(y_pred,y_test)\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "efc18b5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15,  0,  0],\n",
       "       [ 0, 12,  0],\n",
       "       [ 0,  3, 15]], dtype=int64)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2e4a5bbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        15\n",
      "Iris-versicolor       0.80      1.00      0.89        12\n",
      " Iris-virginica       1.00      0.83      0.91        18\n",
      "\n",
      "       accuracy                           0.93        45\n",
      "      macro avg       0.93      0.94      0.93        45\n",
      "   weighted avg       0.95      0.93      0.93        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cla=classification_report(y_pred,y_test)\n",
    "print(cla)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8bdd920",
   "metadata": {},
   "source": [
    "## 7.  Testing on new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2efc3255",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Iris-setosa']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "x_new=np.array([[2.1,3.1,0.4,0.4]])\n",
    "prediction=knn_model.predict(x_new)\n",
    "print(prediction)"
   ]
  }
 ],
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