ir.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5595ef46-c35a-4778-a17a-ff2a2f6104b1",
   "metadata": {},
   "source": [
    "# IRIS CLASSIFICATION MODEL\n",
    "\n",
    "In this notebook, we will build a classification model to predict the species of Iris flowers using the famous Iris dataset. We'll go through the steps of loading the data, performing exploratory data analysis (EDA), feature engineering, building the classification model, and evaluating its performance.\n",
    "## Local Image\n",
    "\n",
    "Here is an image uploaded to the notebook:\n",
    "\n",
    "![IRIS](Iris1.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b7ec32b-9744-4b4d-9c25-542f503c04fd",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Basic ideology on IrisClassification\n",
    "## Problem Statement:\n",
    "Create the model that can classify the different species of the Iris flower.\n",
    "\n",
    "\n",
    "We are going to create a model for classifying the the type of iris based on the variables of the dataset.\n",
    "\n",
    "In first place, we’re going to identifying the variables\n",
    "\n",
    "Sepal:\n",
    "The sepal is the part that forms the calyx of a flower, typically function as protection for the flower in bud, and often as support for the petals when in bloom.\n",
    "\n",
    ">We have two variables\n",
    "\n",
    " 1.The Sepal lenght on centimeters\n",
    " 2.The Sepal Width on centimeters\n",
    "\n",
    "Petal:\n",
    "Petals are modified leaves that surround the reproductive parts of flowers.\n",
    "\n",
    ">We have two variables\n",
    "\n",
    "The Petal lenght on centimeters\n",
    "The Petal Width on centimeters\n",
    "The Types of Flowers\n",
    "Iris is a genus of 260–300 species of flowering plants with showy flowers. It takes its name from the greek word for a rainbow,Iris.\n",
    "\n",
    "In the dataset we have three types of iris:\n",
    "   1.Iris Setosa\n",
    "   2.Iris Versicolour\n",
    "   3.Iris Virginica.\n",
    "\n",
    "\n",
    "  The data set consists of:\n",
    "\n",
    "   >150 samples\n",
    "   >3 labels: species of Iris (Iris setosa, Iris virginica and Iris versicolor).\n",
    "   >4 features: Sepal length,Sepal width,Petal length,Petal Width in cm.\n",
    "   ## Iris dataset.\n",
    "\n",
    "Here is an image uploaded to the notebook:\n",
    "\n",
    "![](Iris2.png)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a7274c2-07e3-4b48-8841-c510902d2135",
   "metadata": {},
   "source": [
    "# Goal\n",
    " The iris species have been classified into three categories that are setosa, versicolor, or virginica.\n",
    "     \n",
    "     \n",
    "The goal is to create a machine learning model that can learn from the measurements of these irises whose species are already known so that we can predict the species for the new irises that she has founded or in other words if she will find any new iris flower."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf83880f-d49b-4a85-a7ca-296d8effcf07",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Importing modules "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "59e1165d-a1f1-4829-8b9e-534be6a2ed26",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in c:\\users\\phani\\anaconda3\\lib\\site-packages (1.24.3)Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "Requirement already satisfied: pandas in c:\\users\\phani\\anaconda3\\lib\\site-packages (1.5.3)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\phani\\anaconda3\\lib\\site-packages (3.7.1)\n",
      "Requirement already satisfied: scikit-learn in c:\\users\\phani\\anaconda3\\lib\\site-packages (1.3.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\phani\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\phani\\anaconda3\\lib\\site-packages (from pandas) (2022.7)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\phani\\anaconda3\\lib\\site-packages (from matplotlib) (1.0.5)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\phani\\anaconda3\\lib\\site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\phani\\anaconda3\\lib\\site-packages (from matplotlib) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\phani\\anaconda3\\lib\\site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\phani\\anaconda3\\lib\\site-packages (from matplotlib) (23.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\phani\\anaconda3\\lib\\site-packages (from matplotlib) (9.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\phani\\anaconda3\\lib\\site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: scipy>=1.5.0 in c:\\users\\phani\\anaconda3\\lib\\site-packages (from scikit-learn) (1.10.1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\phani\\anaconda3\\lib\\site-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\phani\\anaconda3\\lib\\site-packages (from scikit-learn) (2.2.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\phani\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "pip install numpy pandas matplotlib scikit-learn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "9f4f6017-2197-463b-bc73-31798d515bfb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn in c:\\users\\phani\\anaconda3\\lib\\site-packages (1.3.0)Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\phani\\anaconda3\\lib\\site-packages (from scikit-learn) (1.24.3)\n",
      "Requirement already satisfied: scipy>=1.5.0 in c:\\users\\phani\\anaconda3\\lib\\site-packages (from scikit-learn) (1.10.1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\phani\\anaconda3\\lib\\site-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\phani\\anaconda3\\lib\\site-packages (from scikit-learn) (2.2.0)\n"
     ]
    }
   ],
   "source": [
    "pip install scikit-learn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "e6f81509-18d7-4a3f-ac67-9946aec7167e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import numpy\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "0d68fd04-c231-4146-8cb4-b537384914e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a2376c6-48e5-405e-9add-c457a7dd6c31",
   "metadata": {},
   "source": [
    "## Loding the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "386f3a54-6a1f-4b99-922e-1f3d809c2444",
   "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",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\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>2</td>\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>3</td>\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</td>\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</td>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load the csv data\n",
    "iris =  pd.read_csv('Desktop/DEMO/Iris.csv')\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "64d55d7b-fbef-4ecf-a195-41c5b86988ce",
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</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",
       "  </tbody>\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"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = iris.drop(columns = ['Id'])\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "46241e13-f055-4bbd-bbf5-61fe2f4974f4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</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": [
       "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\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": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe()\n",
    "#basic stats of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "49a390d1-b969-43ac-a673-d561c9570fa8",
   "metadata": {
    "tags": []
   },
   "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   SepalLengthCm  150 non-null    float64\n",
      " 1   SepalWidthCm   150 non-null    float64\n",
      " 2   PetalLengthCm  150 non-null    float64\n",
      " 3   PetalWidthCm   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.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac71ede2-8549-45be-824f-7b20cfa9f3aa",
   "metadata": {
    "tags": []
   },
   "source": [
    "#display no of samples in each class\n",
    "iris['Species'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "289b433f-611a-4a88-9e6a-f3e5a0b5245c",
   "metadata": {},
   "source": [
    "## Preprocessing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "75e5b66b-0ad4-4940-886d-2b321056e295",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLengthCm    0\n",
       "SepalWidthCm     0\n",
       "PetalLengthCm    0\n",
       "PetalWidthCm     0\n",
       "Species          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to check any null values\n",
    "iris.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3535dee-e245-47f9-94c2-b2d8c7138ce8",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "4b548e49-f4a3-46a3-897a-d249c781ab59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data analysis\n",
    "iris['SepalLengthCm'].hist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "256339f5-d189-4cdd-a666-e4826e427c16",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris['PetalLengthCm'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "4326e98d-65c2-409d-b619-4bb78f8f089d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris['PetalWidthCm'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "fa20c8e4-1ab2-4fb5-9c54-3d59a89ddbcc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris['SepalWidthCm'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "7bf2f83e-9c61-4cb9-80be-0c69d5353528",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# scatter plotting, to create list of colors and lables\n",
    "colors = ['red','green','blue']\n",
    "Species = ['Iris-setosa','Iris-versicolor','Iris-virginica']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "7db5fcba-4389-40d5-9786-b1e01c30bb2b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3):\n",
    "  x=iris[iris['Species'] == Species[i]]\n",
    "  plt.scatter(x['SepalLengthCm'],x['SepalWidthCm'], c = colors[i], label=Species[i])\n",
    "plt.xlabel(\"SepalLengthCm\")\n",
    "plt.ylabel(\"SepalWidthCm\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "6764c7ba-2241-4cd4-b347-bc455587e18c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3):\n",
    "  x=iris[iris['Species'] == Species[i]]\n",
    "  plt.scatter(x['PetalLengthCm'],x['PetalWidthCm'], c = colors[i], label=Species[i])\n",
    "plt.xlabel(\"PetalLengthCm\")\n",
    "plt.ylabel(\"PetalWidthCm\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "bc8f6327-39a3-42e3-8b59-7b2c955f96a8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Species\n",
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.groupby(\"Species\").size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "305e5172-7527-4c12-bf84-7362657ca0de",
   "metadata": {},
   "source": [
    "## Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "ad6a6e5c-6397-4520-ae60-941abc1869d6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = iris.corr(numeric_only = True)\n",
    "plt.subplots(figsize=(10,6))\n",
    "sns.heatmap(corr, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff40d3e1-afb2-4c61-8349-feccf08af6c6",
   "metadata": {},
   "source": [
    "## Model Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "32cba1db-0360-4533-99b7-57ea7cbf54db",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import pickle\n",
    "\n",
    "#input data\n",
    "X = iris.drop(columns=['Species'])\n",
    "#output data\n",
    "Y = iris['Species']\n",
    "#split data for train and test\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "46d49e63-1e84-4e38-a670-825020e3a150",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy:  97.14285714285714\n"
     ]
    }
   ],
   "source": [
    "#logistic regression \n",
    "model = LogisticRegression()\n",
    "model.fit(x_train, y_train)\n",
    "print(\"Logistic Regression Accuracy: \", model.score(x_train, y_train)*100) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "7b8b58c7-a520-4f62-b178-bb0646e88334",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 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-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 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-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 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-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 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-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train.values, y_train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "db19d7dc-6377-4cdf-bd92-6f37a65ac1ec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  97.77777777777777\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: \",model.score(x_test, y_test)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "4954d7da-bb5f-4064-beb5-4cb3869426fc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-nearest neighbours Accuracy:  93.33333333333333\n"
     ]
    }
   ],
   "source": [
    "#k-nearest neighbours \n",
    "model = KNeighborsClassifier()\n",
    "model.fit(x_train.values, y_train.values)\n",
    "print(\"K-nearest neighbours Accuracy: \", model.score(x_test,y_test)*100)\n",
    "                                                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "027c4b14-d18b-404b-a2f3-3137e4ebb354",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 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-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 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-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 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-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 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-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" 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-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" 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": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train.values, y_train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "dcb9683d-db1c-43a2-95a5-1ca9641502cf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  93.33333333333333\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: \",model.score(x_test, y_test)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "23f9a392-26e1-4a48-bd7f-98ffb9f4bec2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Accuracy:  95.55555555555556\n"
     ]
    }
   ],
   "source": [
    "#decision tree\n",
    "model= DecisionTreeClassifier()\n",
    "model.fit(x_train.values, y_train.values)\n",
    "print(\"Decision Tree Accuracy: \", model.score(x_test,y_test)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "54e59556-27f7-4ce4-a6c2-be5dd8a3de49",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 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-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 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-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 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-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 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-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</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-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train.values, y_train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "77fd475c-fdd8-43a6-a653-3e2128d16b48",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  95.55555555555556\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: \",model.score(x_test, y_test)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "e96ac510-cac3-435a-9200-3076a2a1ef06",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename= 'saved_model.sav'\n",
    "pickle.dump(model,open(filename,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "d81c9608-6c05-47fa-ba24-db0490855cbb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "bcd52412-3624-42d1-8a58-6a7a0ba4b745",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved successfully.\n"
     ]
    }
   ],
   "source": [
    "#saving the updated model\n",
    "filename= 'saved_model.sav'\n",
    "try:\n",
    "    with open(filename,'wb')as file:\n",
    "        pickle.dump(model,file)\n",
    "    print(\"Model saved successfully.\")\n",
    "except Exception as e:\n",
    "    print(f\"Error saving the model:{e}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "9a24f00e-d55e-4ef1-8ff0-a79dfbd9fe2f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "load_model= pickle.load(open(filename,'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "b303909e-5fa8-47f4-ada7-1268ff68dddf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa'], dtype=object)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_model.predict([[6,5,2,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "a7fac23b-413a-43d8-a64d-0f7e10ed3d7e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3.0\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "print(sklearn.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "0f6941eb-7d59-4d94-955b-4224d4124543",
   "metadata": {
    "tags": []
   },
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>6.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "37             4.9           3.1            1.5           0.1\n",
       "60             5.0           2.0            3.5           1.0\n",
       "132            6.4           2.8            5.6           2.2\n",
       "53             5.5           2.3            4.0           1.3\n",
       "58             6.6           2.9            4.6           1.3"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "afc9e9d2-f11c-44b0-8331-f5037a7a733e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_model.predict([[1,2,3,4]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a49a97b9-0bee-4a75-9d22-f4322dd0fa14",
   "metadata": {},
   "source": [
    " ## Conclusion\n",
    "\n",
    "In this notebook, we successfully built a classification model for predicting the species of Iris flowers. We followed these key steps:\n",
    "\n",
    "### Data Loading and Preprocessing\n",
    "We loaded the Iris dataset and performed basic preprocessing, ensuring the data was ready for analysis.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25ecc53e-61b5-4ff7-9c7a-d3ca4d6c6df4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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