Iris Flower Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "355fb306",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9d4d7aa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_flower_file=pd.read_csv(\"iris flower file.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "772b69f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>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",
       "    <tr>\n",
       "      <th>5</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>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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\n",
       "5            5.4          3.9           1.7          0.4  Iris-setosa\n",
       "6            4.6          3.4           1.4          0.3  Iris-setosa\n",
       "7            5.0          3.4           1.5          0.2  Iris-setosa\n",
       "8            4.4          2.9           1.4          0.2  Iris-setosa\n",
       "9            4.9          3.1           1.5          0.1  Iris-setosa\n",
       "10           5.4          3.7           1.5          0.2  Iris-setosa\n",
       "11           4.8          3.4           1.6          0.2  Iris-setosa\n",
       "12           4.8          3.0           1.4          0.1  Iris-setosa\n",
       "13           4.3          3.0           1.1          0.1  Iris-setosa\n",
       "14           5.8          4.0           1.2          0.2  Iris-setosa\n",
       "15           5.7          4.4           1.5          0.4  Iris-setosa"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_flower_file.head(16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ebce064e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_flower_file.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d5e4fb45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal_length  150 non-null    float64\n",
      " 1   sepal_width   150 non-null    float64\n",
      " 2   petal_length  150 non-null    float64\n",
      " 3   petal_width   150 non-null    float64\n",
      " 4   species       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "iris_flower_file.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e7e408b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_flower_file.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "36bf82c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    0\n",
       "sepal_width     0\n",
       "petal_length    0\n",
       "petal_width     0\n",
       "species         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_flower_file.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f5e75272",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "    <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",
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       "    <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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_flower_file.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2c7059b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_flower_file['sepal_length'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1ec063ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_flower_file['sepal_width'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7a717f29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_flower_file['petal_length'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ffde47c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_flower_file['petal_width'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f40927e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors=['red','Black','teal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2def0f3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "species=['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e2e4c69e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e26479ff70>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    x=iris_flower_file[iris_flower_file['species']==species[i]]\n",
    "    plt.scatter(x['sepal_length'],x['sepal_width'],c=colors[i],label=species[i])\n",
    "plt.xlabel(\"Sepal Length\")\n",
    "plt.ylabel(\"Sepal Width\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "708cc112",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e264813490>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    x=iris_flower_file[iris_flower_file['species']==species[i]]\n",
    "    plt.scatter(x['petal_length'],x['petal_width'],c=colors[i],label=species[i])\n",
    "plt.xlabel(\"Petal Length\")\n",
    "plt.ylabel(\"Petal Width\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "21eddd92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e264731ff0>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    x=iris_flower_file[iris_flower_file['species']==species[i]]\n",
    "    plt.scatter(x['sepal_length'],x['petal_length'],c=colors[i],label=species[i])\n",
    "plt.xlabel(\"Sepal Length\")\n",
    "plt.ylabel(\"Petal Length\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "85c0a2c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e2649e3eb0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    x=iris_flower_file[iris_flower_file['species']==species[i]]\n",
    "    plt.scatter(x['sepal_width'],x['petal_width'],c=colors[i],label=species[i])\n",
    "plt.xlabel(\"Sepal Width\")\n",
    "plt.ylabel(\"Petal Width\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "a6570818",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_columns=iris_flower_file.drop(columns='species')\n",
    "corr=numeric_columns.corr()\n",
    "fig,axis=plt.subplots(figsize=(5,5))\n",
    "sns.heatmap(corr,annot=True,ax=axis,cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "a1393b19",
   "metadata": {},
   "outputs": [],
   "source": [
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2988078e",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_flower_file['species']=le.fit_transform(iris_flower_file['species'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ccbcdc34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>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>0</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>0</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>0</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>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length  sepal_width  petal_length  petal_width  species\n",
       "0            5.1          3.5           1.4          0.2        0\n",
       "1            4.9          3.0           1.4          0.2        0\n",
       "2            4.7          3.2           1.3          0.2        0\n",
       "3            4.6          3.1           1.5          0.2        0\n",
       "4            5.0          3.6           1.4          0.2        0\n",
       "5            5.4          3.9           1.7          0.4        0\n",
       "6            4.6          3.4           1.4          0.3        0\n",
       "7            5.0          3.4           1.5          0.2        0\n",
       "8            4.4          2.9           1.4          0.2        0\n",
       "9            4.9          3.1           1.5          0.1        0\n",
       "10           5.4          3.7           1.5          0.2        0\n",
       "11           4.8          3.4           1.6          0.2        0\n",
       "12           4.8          3.0           1.4          0.1        0\n",
       "13           4.3          3.0           1.1          0.1        0\n",
       "14           5.8          4.0           1.2          0.2        0\n",
       "15           5.7          4.4           1.5          0.4        0"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_flower_file.head(16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "c1ed382f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=iris_flower_file.drop(columns='species')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f22c7a63",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=iris_flower_file['species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f1b6bb81",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b0564b1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "LR=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "3d4ee362",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "0c9cd257",
   "metadata": {},
   "outputs": [],
   "source": [
    "KNN=KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e4c3de56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" 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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" 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": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "f1aca8c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "DT=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "9c516041",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DT.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "5ef82907",
   "metadata": {},
   "outputs": [],
   "source": [
    "LR_accuracy=LR.score(x_test,y_test)*100\n",
    "KNN_accuracy=KNN.score(x_test,y_test)*100\n",
    "DT_accuracy=DT.score(x_test,y_test)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "5c591530",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy by using Logistic Regression: 95.55555555555556%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Accuracy by using Logistic Regression: {LR_accuracy}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "e68f31ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy by using K Nearest Neighbors Algorithm: 100.0%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Accuracy by using K Nearest Neighbors Algorithm: {KNN_accuracy}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "61a55162",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy by using Decision Tree Classifier: 93.33333333333333%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Accuracy by using Decision Tree Classifier: {DT_accuracy}%\")"
   ]
  }
 ],
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