Irish_29sep.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "682de000",
   "metadata": {},
   "source": [
    "# import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8d59be2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a321ad6",
   "metadata": {},
   "source": [
    "# import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "adf1b573",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(r\"C:\\Users\\sir\\Desktop\\Durgesh_7\\22_sep_2023\\Iris.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cf8a76ee",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
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       "      <td>Iris-setosa</td>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\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",
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       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5.9</td>\n",
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       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "0      1            5.1           3.5            1.4           0.2   \n",
       "1      2            4.9           3.0            1.4           0.2   \n",
       "2      3            4.7           3.2            1.3           0.2   \n",
       "3      4            4.6           3.1            1.5           0.2   \n",
       "4      5            5.0           3.6            1.4           0.2   \n",
       "..   ...            ...           ...            ...           ...   \n",
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       "147  148            6.5           3.0            5.2           2.0   \n",
       "148  149            6.2           3.4            5.4           2.3   \n",
       "149  150            5.9           3.0            5.1           1.8   \n",
       "\n",
       "            Species  \n",
       "0       Iris-setosa  \n",
       "1       Iris-setosa  \n",
       "2       Iris-setosa  \n",
       "3       Iris-setosa  \n",
       "4       Iris-setosa  \n",
       "..              ...  \n",
       "145  Iris-virginica  \n",
       "146  Iris-virginica  \n",
       "147  Iris-virginica  \n",
       "148  Iris-virginica  \n",
       "149  Iris-virginica  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "63f0593a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data1=data.drop(columns=\"Id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e233e62a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>4.6</td>\n",
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       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "      <td>Iris-virginica</td>\n",
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       "      <td>Iris-virginica</td>\n",
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       "      <td>Iris-virginica</td>\n",
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       "      <td>Iris-virginica</td>\n",
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       "<p>150 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm         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",
       "..             ...           ...            ...           ...             ...\n",
       "145            6.7           3.0            5.2           2.3  Iris-virginica\n",
       "146            6.3           2.5            5.0           1.9  Iris-virginica\n",
       "147            6.5           3.0            5.2           2.0  Iris-virginica\n",
       "148            6.2           3.4            5.4           2.3  Iris-virginica\n",
       "149            5.9           3.0            5.1           1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
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     "execution_count": 5,
     "metadata": {},
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   "source": [
    "data1"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4f7c1b54",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2=data.drop(columns=\"SepalLengthCm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "abfa5715",
   "metadata": {},
   "outputs": [
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       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
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       "<p>150 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "      Id  SepalWidthCm  PetalLengthCm  PetalWidthCm         Species\n",
       "0      1           3.5            1.4           0.2     Iris-setosa\n",
       "1      2           3.0            1.4           0.2     Iris-setosa\n",
       "2      3           3.2            1.3           0.2     Iris-setosa\n",
       "3      4           3.1            1.5           0.2     Iris-setosa\n",
       "4      5           3.6            1.4           0.2     Iris-setosa\n",
       "..   ...           ...            ...           ...             ...\n",
       "145  146           3.0            5.2           2.3  Iris-virginica\n",
       "146  147           2.5            5.0           1.9  Iris-virginica\n",
       "147  148           3.0            5.2           2.0  Iris-virginica\n",
       "148  149           3.4            5.4           2.3  Iris-virginica\n",
       "149  150           3.0            5.1           1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "data2"
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f2059709",
   "metadata": {},
   "outputs": [],
   "source": [
    "data3=data.drop(columns=\"SepalWidthCm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "db37ed10",
   "metadata": {},
   "outputs": [
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       "      <td>4</td>\n",
       "      <td>4.6</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</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</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>145</th>\n",
       "      <td>146</td>\n",
       "      <td>6.7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
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       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>6.5</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  PetalLengthCm  PetalWidthCm         Species\n",
       "0      1            5.1            1.4           0.2     Iris-setosa\n",
       "1      2            4.9            1.4           0.2     Iris-setosa\n",
       "2      3            4.7            1.3           0.2     Iris-setosa\n",
       "3      4            4.6            1.5           0.2     Iris-setosa\n",
       "4      5            5.0            1.4           0.2     Iris-setosa\n",
       "..   ...            ...            ...           ...             ...\n",
       "145  146            6.7            5.2           2.3  Iris-virginica\n",
       "146  147            6.3            5.0           1.9  Iris-virginica\n",
       "147  148            6.5            5.2           2.0  Iris-virginica\n",
       "148  149            6.2            5.4           2.3  Iris-virginica\n",
       "149  150            5.9            5.1           1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1239a617",
   "metadata": {},
   "outputs": [],
   "source": [
    "data4=data.drop(columns=\"PetalLengthCm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "57cd1e4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
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       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>6.3</td>\n",
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       "      <td>1.9</td>\n",
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       "      <td>6.5</td>\n",
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       "      <td>6.2</td>\n",
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       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
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       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalWidthCm         Species\n",
       "0      1            5.1           3.5           0.2     Iris-setosa\n",
       "1      2            4.9           3.0           0.2     Iris-setosa\n",
       "2      3            4.7           3.2           0.2     Iris-setosa\n",
       "3      4            4.6           3.1           0.2     Iris-setosa\n",
       "4      5            5.0           3.6           0.2     Iris-setosa\n",
       "..   ...            ...           ...           ...             ...\n",
       "145  146            6.7           3.0           2.3  Iris-virginica\n",
       "146  147            6.3           2.5           1.9  Iris-virginica\n",
       "147  148            6.5           3.0           2.0  Iris-virginica\n",
       "148  149            6.2           3.4           2.3  Iris-virginica\n",
       "149  150            5.9           3.0           1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c1789134",
   "metadata": {},
   "outputs": [],
   "source": [
    "data5=data.drop(columns=\"PetalWidthCm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "56267d38",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>5.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm         Species\n",
       "0      1            5.1           3.5            1.4     Iris-setosa\n",
       "1      2            4.9           3.0            1.4     Iris-setosa\n",
       "2      3            4.7           3.2            1.3     Iris-setosa\n",
       "3      4            4.6           3.1            1.5     Iris-setosa\n",
       "4      5            5.0           3.6            1.4     Iris-setosa\n",
       "..   ...            ...           ...            ...             ...\n",
       "145  146            6.7           3.0            5.2  Iris-virginica\n",
       "146  147            6.3           2.5            5.0  Iris-virginica\n",
       "147  148            6.5           3.0            5.2  Iris-virginica\n",
       "148  149            6.2           3.4            5.4  Iris-virginica\n",
       "149  150            5.9           3.0            5.1  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "53ca672d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data6=data.drop(columns=\"Species\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "86519ded",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
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       "      <td>6.2</td>\n",
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       "      <td>2.3</td>\n",
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       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "0      1            5.1           3.5            1.4           0.2\n",
       "1      2            4.9           3.0            1.4           0.2\n",
       "2      3            4.7           3.2            1.3           0.2\n",
       "3      4            4.6           3.1            1.5           0.2\n",
       "4      5            5.0           3.6            1.4           0.2\n",
       "..   ...            ...           ...            ...           ...\n",
       "145  146            6.7           3.0            5.2           2.3\n",
       "146  147            6.3           2.5            5.0           1.9\n",
       "147  148            6.5           3.0            5.2           2.0\n",
       "148  149            6.2           3.4            5.4           2.3\n",
       "149  150            5.9           3.0            5.1           1.8\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e38c5587",
   "metadata": {},
   "source": [
    "# columns visulization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b907ffb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
       "       'Species'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "702d2bbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data.iloc[: , 0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4f04bcca",
   "metadata": {},
   "outputs": [
    {
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       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm\n",
       "0      1            5.1           3.5            1.4\n",
       "1      2            4.9           3.0            1.4\n",
       "2      3            4.7           3.2            1.3\n",
       "3      4            4.6           3.1            1.5\n",
       "4      5            5.0           3.6            1.4\n",
       "..   ...            ...           ...            ...\n",
       "145  146            6.7           3.0            5.2\n",
       "146  147            6.3           2.5            5.0\n",
       "147  148            6.5           3.0            5.2\n",
       "148  149            6.2           3.4            5.4\n",
       "149  150            5.9           3.0            5.1\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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    "x"
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  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "535e9c24",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=data.iloc[: , 1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6ae9eda6",
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
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      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "0              5.1           3.5            1.4           0.2\n",
       "1              4.9           3.0            1.4           0.2\n",
       "2              4.7           3.2            1.3           0.2\n",
       "3              4.6           3.1            1.5           0.2\n",
       "4              5.0           3.6            1.4           0.2\n",
       "..             ...           ...            ...           ...\n",
       "145            6.7           3.0            5.2           2.3\n",
       "146            6.3           2.5            5.0           1.9\n",
       "147            6.5           3.0            5.2           2.0\n",
       "148            6.2           3.4            5.4           2.3\n",
       "149            5.9           3.0            5.1           1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "23c1a727",
   "metadata": {},
   "outputs": [],
   "source": [
    "z=data.iloc[: , 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9a9dec31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Iris-setosa\n",
       "1         Iris-setosa\n",
       "2         Iris-setosa\n",
       "3         Iris-setosa\n",
       "4         Iris-setosa\n",
       "            ...      \n",
       "145    Iris-virginica\n",
       "146    Iris-virginica\n",
       "147    Iris-virginica\n",
       "148    Iris-virginica\n",
       "149    Iris-virginica\n",
       "Name: Species, Length: 150, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "86d067d3",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'seaborn' has no attribute 'count'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[23], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m sns\u001b[38;5;241m.\u001b[39mcount(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSpecies\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'seaborn' has no attribute 'count'"
     ]
    }
   ],
   "source": [
    "sns.count(data['Species'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74583f0f",
   "metadata": {},
   "source": [
    "# Model_selection dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a586921",
   "metadata": {},
   "source": [
    "# To train and test model split datase to train and test sets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3e37b402",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "20fdaee7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "05883ce4",
   "metadata": {},
   "outputs": [
    {
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       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm\n",
       "0      1            5.1           3.5            1.4\n",
       "1      2            4.9           3.0            1.4\n",
       "2      3            4.7           3.2            1.3\n",
       "3      4            4.6           3.1            1.5\n",
       "4      5            5.0           3.6            1.4\n",
       "..   ...            ...           ...            ...\n",
       "145  146            6.7           3.0            5.2\n",
       "146  147            6.3           2.5            5.0\n",
       "147  148            6.5           3.0            5.2\n",
       "148  149            6.2           3.4            5.4\n",
       "149  150            5.9           3.0            5.1\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "x"
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  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "84ef2c17",
   "metadata": {},
   "outputs": [
    {
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       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
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       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "0              5.1           3.5            1.4           0.2\n",
       "1              4.9           3.0            1.4           0.2\n",
       "2              4.7           3.2            1.3           0.2\n",
       "3              4.6           3.1            1.5           0.2\n",
       "4              5.0           3.6            1.4           0.2\n",
       "..             ...           ...            ...           ...\n",
       "145            6.7           3.0            5.2           2.3\n",
       "146            6.3           2.5            5.0           1.9\n",
       "147            6.5           3.0            5.2           2.0\n",
       "148            6.2           3.4            5.4           2.3\n",
       "149            5.9           3.0            5.1           1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
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     "execution_count": 27,
     "metadata": {},
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   "id": "fdcaa9ee",
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   "outputs": [
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       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm\n",
       "60    61            5.0           2.0            3.5\n",
       "116  117            6.5           3.0            5.5\n",
       "144  145            6.7           3.3            5.7\n",
       "119  120            6.0           2.2            5.0\n",
       "108  109            6.7           2.5            5.8\n",
       "..   ...            ...           ...            ...\n",
       "9     10            4.9           3.1            1.5\n",
       "103  104            6.3           2.9            5.6\n",
       "67    68            5.8           2.7            4.1\n",
       "117  118            7.7           3.8            6.7\n",
       "47    48            4.6           3.2            1.4\n",
       "\n",
       "[105 rows x 4 columns]"
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   "execution_count": 30,
   "id": "01bd8b71",
   "metadata": {},
   "outputs": [
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       "      <td>5.0</td>\n",
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       "      <th>137</th>\n",
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       "      <td>6.0</td>\n",
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       "      <th>43</th>\n",
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      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm\n",
       "114  115            5.8           2.8            5.1\n",
       "62    63            6.0           2.2            4.0\n",
       "33    34            5.5           4.2            1.4\n",
       "107  108            7.3           2.9            6.3\n",
       "7      8            5.0           3.4            1.5\n",
       "100  101            6.3           3.3            6.0\n",
       "40    41            5.0           3.5            1.3\n",
       "86    87            6.7           3.1            4.7\n",
       "76    77            6.8           2.8            4.8\n",
       "71    72            6.1           2.8            4.0\n",
       "134  135            6.1           2.6            5.6\n",
       "51    52            6.4           3.2            4.5\n",
       "73    74            6.1           2.8            4.7\n",
       "54    55            6.5           2.8            4.6\n",
       "63    64            6.1           2.9            4.7\n",
       "37    38            4.9           3.1            1.5\n",
       "78    79            6.0           2.9            4.5\n",
       "90    91            5.5           2.6            4.4\n",
       "45    46            4.8           3.0            1.4\n",
       "16    17            5.4           3.9            1.3\n",
       "121  122            5.6           2.8            4.9\n",
       "66    67            5.6           3.0            4.5\n",
       "24    25            4.8           3.4            1.9\n",
       "8      9            4.4           2.9            1.4\n",
       "126  127            6.2           2.8            4.8\n",
       "22    23            4.6           3.6            1.0\n",
       "44    45            5.1           3.8            1.9\n",
       "97    98            6.2           2.9            4.3\n",
       "93    94            5.0           2.3            3.3\n",
       "26    27            5.0           3.4            1.6\n",
       "137  138            6.4           3.1            5.5\n",
       "84    85            5.4           3.0            4.5\n",
       "27    28            5.2           3.5            1.5\n",
       "127  128            6.1           3.0            4.9\n",
       "132  133            6.4           2.8            5.6\n",
       "59    60            5.2           2.7            3.9\n",
       "18    19            5.7           3.8            1.7\n",
       "83    84            6.0           2.7            5.1\n",
       "61    62            5.9           3.0            4.2\n",
       "92    93            5.8           2.6            4.0\n",
       "112  113            6.8           3.0            5.5\n",
       "2      3            4.7           3.2            1.3\n",
       "141  142            6.9           3.1            5.1\n",
       "43    44            5.0           3.5            1.6\n",
       "10    11            5.4           3.7            1.5"
      ]
     },
     "execution_count": 30,
     "metadata": {},
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       "      <th>60</th>\n",
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       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "60             5.0           2.0            3.5           1.0\n",
       "116            6.5           3.0            5.5           1.8\n",
       "144            6.7           3.3            5.7           2.5\n",
       "119            6.0           2.2            5.0           1.5\n",
       "108            6.7           2.5            5.8           1.8\n",
       "..             ...           ...            ...           ...\n",
       "9              4.9           3.1            1.5           0.1\n",
       "103            6.3           2.9            5.6           1.8\n",
       "67             5.8           2.7            4.1           1.0\n",
       "117            7.7           3.8            6.7           2.2\n",
       "47             4.6           3.2            1.4           0.2\n",
       "\n",
       "[105 rows x 4 columns]"
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  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9e2b6a19",
   "metadata": {},
   "outputs": [
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       "      <th>18</th>\n",
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       "      <th>112</th>\n",
       "      <td>6.8</td>\n",
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       "      <th>141</th>\n",
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       "      <td>5.0</td>\n",
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       "      <th>10</th>\n",
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      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "114            5.8           2.8            5.1           2.4\n",
       "62             6.0           2.2            4.0           1.0\n",
       "33             5.5           4.2            1.4           0.2\n",
       "107            7.3           2.9            6.3           1.8\n",
       "7              5.0           3.4            1.5           0.2\n",
       "100            6.3           3.3            6.0           2.5\n",
       "40             5.0           3.5            1.3           0.3\n",
       "86             6.7           3.1            4.7           1.5\n",
       "76             6.8           2.8            4.8           1.4\n",
       "71             6.1           2.8            4.0           1.3\n",
       "134            6.1           2.6            5.6           1.4\n",
       "51             6.4           3.2            4.5           1.5\n",
       "73             6.1           2.8            4.7           1.2\n",
       "54             6.5           2.8            4.6           1.5\n",
       "63             6.1           2.9            4.7           1.4\n",
       "37             4.9           3.1            1.5           0.1\n",
       "78             6.0           2.9            4.5           1.5\n",
       "90             5.5           2.6            4.4           1.2\n",
       "45             4.8           3.0            1.4           0.3\n",
       "16             5.4           3.9            1.3           0.4\n",
       "121            5.6           2.8            4.9           2.0\n",
       "66             5.6           3.0            4.5           1.5\n",
       "24             4.8           3.4            1.9           0.2\n",
       "8              4.4           2.9            1.4           0.2\n",
       "126            6.2           2.8            4.8           1.8\n",
       "22             4.6           3.6            1.0           0.2\n",
       "44             5.1           3.8            1.9           0.4\n",
       "97             6.2           2.9            4.3           1.3\n",
       "93             5.0           2.3            3.3           1.0\n",
       "26             5.0           3.4            1.6           0.4\n",
       "137            6.4           3.1            5.5           1.8\n",
       "84             5.4           3.0            4.5           1.5\n",
       "27             5.2           3.5            1.5           0.2\n",
       "127            6.1           3.0            4.9           1.8\n",
       "132            6.4           2.8            5.6           2.2\n",
       "59             5.2           2.7            3.9           1.4\n",
       "18             5.7           3.8            1.7           0.3\n",
       "83             6.0           2.7            5.1           1.6\n",
       "61             5.9           3.0            4.2           1.5\n",
       "92             5.8           2.6            4.0           1.2\n",
       "112            6.8           3.0            5.5           2.1\n",
       "2              4.7           3.2            1.3           0.2\n",
       "141            6.9           3.1            5.1           2.3\n",
       "43             5.0           3.5            1.6           0.6\n",
       "10             5.4           3.7            1.5           0.2"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "796b72b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105, 4)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2bab8236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45, 4)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2cfd9c62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105, 4)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cdad6892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45, 4)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e9ccad33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='PetalLengthCm', y='PetalWidthCm',\n",
    "               hue='Species',data=data1,)\n",
    "plt.legend(bbox_to_anchor=(1,1),loc=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d6b5dfcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='SepalLengthCm', y='SepalWidthCm',\n",
    "               hue='Species',data=data1,)\n",
    "plt.legend(bbox_to_anchor=(1,1),loc=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e5eda79",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}