Iris flower classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "748ade6b-f101-4e4b-8c5c-676fb1d5303d",
   "metadata": {},
   "source": [
    "## NAME - PRAGATHI M\n",
    "## DATA SCIENCE PROJECT TASK2 -INTERNPEDIA\n",
    "## PROJECT NAME - IRIS FLOWER CLASSIFICATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a83482e-2524-4f10-b7f0-2c2fd3e2fca8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://editor.analyticsvidhya.com/uploads/51518iris%20img1.png\" width=\"850\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from IPython.display import Image\n",
    "Image(url='https://editor.analyticsvidhya.com/uploads/51518iris%20img1.png', width=850)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5978693e-d490-4882-956c-c8508f09cf99",
   "metadata": {},
   "source": [
    "## Problem Statement\n",
    "IRIS FLOWER CLASSIFICATION:\n",
    "There are three types of iris flowers: setosa, versicolor, and virginica. These species vary in size. Assume for the moment that you have the measurements of the iris flowers according to their species. Your job is to train a machine learning model to identify and categorize iris species based on the measurements.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06c6d536-1a39-4934-87be-0e3571b15170",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d586db95-960b-4602-bf5b-cbecb873de1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26e7354d-d2df-4650-9b83-d5f514a88156",
   "metadata": {},
   "source": [
    "## Loading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ad3a50a-0582-43d4-b27e-c94bb0c598bd",
   "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>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = pd.read_csv('Iris.csv')\n",
    "iris.head()\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a6d48b6a-4f1c-497e-876a-ea6f51113eef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename the complex columns name\n",
    "iris= iris.rename(columns={'SepalLengthCm':'Sepal_Length',\n",
    "                           'SepalWidthCm':'Sepal_Width',\n",
    "                           'PetalLengthCm':'Petal_Length',\n",
    "                           'PetalWidthCm':'Petal_Width'})\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "269fb245-9a5c-49f4-af32-49548527eef9",
   "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>Id</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>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  Sepal_Length  Sepal_Width  Petal_Length  Petal_Width      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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ab404604-568b-400a-89a2-4f024d67a514",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id              0\n",
       "Sepal_Length    0\n",
       "Sepal_Width     0\n",
       "Petal_Length    0\n",
       "Petal_Width     0\n",
       "Species         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking null values\n",
    "iris.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "14383f47-5574-4c72-9790-5090ce921cee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Species\n",
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking if the data is biased or not\n",
    "iris ['Species'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8b199374-ef00-4eee-9a92-9cbb35a481e9",
   "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>Id</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",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\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>43.445368</td>\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>1.000000</td>\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>38.250000</td>\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>75.500000</td>\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>112.750000</td>\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>150.000000</td>\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": [
       "               Id  Sepal_Length  Sepal_Width  Petal_Length  Petal_Width\n",
       "count  150.000000    150.000000   150.000000    150.000000   150.000000\n",
       "mean    75.500000      5.843333     3.054000      3.758667     1.198667\n",
       "std     43.445368      0.828066     0.433594      1.764420     0.763161\n",
       "min      1.000000      4.300000     2.000000      1.000000     0.100000\n",
       "25%     38.250000      5.100000     2.800000      1.600000     0.300000\n",
       "50%     75.500000      5.800000     3.000000      4.350000     1.300000\n",
       "75%    112.750000      6.400000     3.300000      5.100000     1.800000\n",
       "max    150.000000      7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking statistical features\n",
    "iris.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c89de789-2fe5-418c-ba27-4c101341c0b0",
   "metadata": {},
   "source": [
    "## Visualization\n",
    "## Scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "38bc4253-9348-4fa9-a378-18738feaad31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x22d4e55d4f0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 743x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(iris, hue=\"Species\",height=6).map(plt.scatter,\"Petal_Length\",\"Sepal_Width\").add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a8c255-88b9-4ef3-bf07-c48f9954ff7c",
   "metadata": {},
   "source": [
    "## Pairplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "35690ad4-e3be-4eb3-8629-d0308e15d7f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x22d7f6c2120>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1143x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the whole dataset\n",
    "sns.pairplot(iris[['Sepal_Length','Sepal_Width','Petal_Length','Petal_Width','Species']], hue=\"Species\",diag_kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f77403f7-7920-42d0-9258-2c8bd9414ed8",
   "metadata": {},
   "source": [
    "## SEPARATING INPUT COLUMNS AND THE OUTPUT COLUMNS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8271ea0e-a027-429a-994e-33e927970e30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate features and target\n",
    "data=iris.values\n",
    "\n",
    "# slicing the matrices\n",
    "X=data[:,0:4]\n",
    "Y=data[:,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d6239ab9-8bb8-4c99-bd5a-39d9067ed5ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n",
      "[[1 5.1 3.5 1.4]\n",
      " [2 4.9 3.0 1.4]\n",
      " [3 4.7 3.2 1.3]\n",
      " [4 4.6 3.1 1.5]\n",
      " [5 5.0 3.6 1.4]\n",
      " [6 5.4 3.9 1.7]\n",
      " [7 4.6 3.4 1.4]\n",
      " [8 5.0 3.4 1.5]\n",
      " [9 4.4 2.9 1.4]\n",
      " [10 4.9 3.1 1.5]\n",
      " [11 5.4 3.7 1.5]\n",
      " [12 4.8 3.4 1.6]\n",
      " [13 4.8 3.0 1.4]\n",
      " [14 4.3 3.0 1.1]\n",
      " [15 5.8 4.0 1.2]\n",
      " [16 5.7 4.4 1.5]\n",
      " [17 5.4 3.9 1.3]\n",
      " [18 5.1 3.5 1.4]\n",
      " [19 5.7 3.8 1.7]\n",
      " [20 5.1 3.8 1.5]\n",
      " [21 5.4 3.4 1.7]\n",
      " [22 5.1 3.7 1.5]\n",
      " [23 4.6 3.6 1.0]\n",
      " [24 5.1 3.3 1.7]\n",
      " [25 4.8 3.4 1.9]\n",
      " [26 5.0 3.0 1.6]\n",
      " [27 5.0 3.4 1.6]\n",
      " [28 5.2 3.5 1.5]\n",
      " [29 5.2 3.4 1.4]\n",
      " [30 4.7 3.2 1.6]\n",
      " [31 4.8 3.1 1.6]\n",
      " [32 5.4 3.4 1.5]\n",
      " [33 5.2 4.1 1.5]\n",
      " [34 5.5 4.2 1.4]\n",
      " [35 4.9 3.1 1.5]\n",
      " [36 5.0 3.2 1.2]\n",
      " [37 5.5 3.5 1.3]\n",
      " [38 4.9 3.1 1.5]\n",
      " [39 4.4 3.0 1.3]\n",
      " [40 5.1 3.4 1.5]\n",
      " [41 5.0 3.5 1.3]\n",
      " [42 4.5 2.3 1.3]\n",
      " [43 4.4 3.2 1.3]\n",
      " [44 5.0 3.5 1.6]\n",
      " [45 5.1 3.8 1.9]\n",
      " [46 4.8 3.0 1.4]\n",
      " [47 5.1 3.8 1.6]\n",
      " [48 4.6 3.2 1.4]\n",
      " [49 5.3 3.7 1.5]\n",
      " [50 5.0 3.3 1.4]\n",
      " [51 7.0 3.2 4.7]\n",
      " [52 6.4 3.2 4.5]\n",
      " [53 6.9 3.1 4.9]\n",
      " [54 5.5 2.3 4.0]\n",
      " [55 6.5 2.8 4.6]\n",
      " [56 5.7 2.8 4.5]\n",
      " [57 6.3 3.3 4.7]\n",
      " [58 4.9 2.4 3.3]\n",
      " [59 6.6 2.9 4.6]\n",
      " [60 5.2 2.7 3.9]\n",
      " [61 5.0 2.0 3.5]\n",
      " [62 5.9 3.0 4.2]\n",
      " [63 6.0 2.2 4.0]\n",
      " [64 6.1 2.9 4.7]\n",
      " [65 5.6 2.9 3.6]\n",
      " [66 6.7 3.1 4.4]\n",
      " [67 5.6 3.0 4.5]\n",
      " [68 5.8 2.7 4.1]\n",
      " [69 6.2 2.2 4.5]\n",
      " [70 5.6 2.5 3.9]\n",
      " [71 5.9 3.2 4.8]\n",
      " [72 6.1 2.8 4.0]\n",
      " [73 6.3 2.5 4.9]\n",
      " [74 6.1 2.8 4.7]\n",
      " [75 6.4 2.9 4.3]\n",
      " [76 6.6 3.0 4.4]\n",
      " [77 6.8 2.8 4.8]\n",
      " [78 6.7 3.0 5.0]\n",
      " [79 6.0 2.9 4.5]\n",
      " [80 5.7 2.6 3.5]\n",
      " [81 5.5 2.4 3.8]\n",
      " [82 5.5 2.4 3.7]\n",
      " [83 5.8 2.7 3.9]\n",
      " [84 6.0 2.7 5.1]\n",
      " [85 5.4 3.0 4.5]\n",
      " [86 6.0 3.4 4.5]\n",
      " [87 6.7 3.1 4.7]\n",
      " [88 6.3 2.3 4.4]\n",
      " [89 5.6 3.0 4.1]\n",
      " [90 5.5 2.5 4.0]\n",
      " [91 5.5 2.6 4.4]\n",
      " [92 6.1 3.0 4.6]\n",
      " [93 5.8 2.6 4.0]\n",
      " [94 5.0 2.3 3.3]\n",
      " [95 5.6 2.7 4.2]\n",
      " [96 5.7 3.0 4.2]\n",
      " [97 5.7 2.9 4.2]\n",
      " [98 6.2 2.9 4.3]\n",
      " [99 5.1 2.5 3.0]\n",
      " [100 5.7 2.8 4.1]\n",
      " [101 6.3 3.3 6.0]\n",
      " [102 5.8 2.7 5.1]\n",
      " [103 7.1 3.0 5.9]\n",
      " [104 6.3 2.9 5.6]\n",
      " [105 6.5 3.0 5.8]\n",
      " [106 7.6 3.0 6.6]\n",
      " [107 4.9 2.5 4.5]\n",
      " [108 7.3 2.9 6.3]\n",
      " [109 6.7 2.5 5.8]\n",
      " [110 7.2 3.6 6.1]\n",
      " [111 6.5 3.2 5.1]\n",
      " [112 6.4 2.7 5.3]\n",
      " [113 6.8 3.0 5.5]\n",
      " [114 5.7 2.5 5.0]\n",
      " [115 5.8 2.8 5.1]\n",
      " [116 6.4 3.2 5.3]\n",
      " [117 6.5 3.0 5.5]\n",
      " [118 7.7 3.8 6.7]\n",
      " [119 7.7 2.6 6.9]\n",
      " [120 6.0 2.2 5.0]\n",
      " [121 6.9 3.2 5.7]\n",
      " [122 5.6 2.8 4.9]\n",
      " [123 7.7 2.8 6.7]\n",
      " [124 6.3 2.7 4.9]\n",
      " [125 6.7 3.3 5.7]\n",
      " [126 7.2 3.2 6.0]\n",
      " [127 6.2 2.8 4.8]\n",
      " [128 6.1 3.0 4.9]\n",
      " [129 6.4 2.8 5.6]\n",
      " [130 7.2 3.0 5.8]\n",
      " [131 7.4 2.8 6.1]\n",
      " [132 7.9 3.8 6.4]\n",
      " [133 6.4 2.8 5.6]\n",
      " [134 6.3 2.8 5.1]\n",
      " [135 6.1 2.6 5.6]\n",
      " [136 7.7 3.0 6.1]\n",
      " [137 6.3 3.4 5.6]\n",
      " [138 6.4 3.1 5.5]\n",
      " [139 6.0 3.0 4.8]\n",
      " [140 6.9 3.1 5.4]\n",
      " [141 6.7 3.1 5.6]\n",
      " [142 6.9 3.1 5.1]\n",
      " [143 5.8 2.7 5.1]\n",
      " [144 6.8 3.2 5.9]\n",
      " [145 6.7 3.3 5.7]\n",
      " [146 6.7 3.0 5.2]\n",
      " [147 6.3 2.5 5.0]\n",
      " [148 6.5 3.0 5.2]\n",
      " [149 6.2 3.4 5.4]\n",
      " [150 5.9 3.0 5.1]]\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6767aa39-bc70-4fb9-aff4-9d25d4eb0ee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150,)\n",
      "['Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica']\n"
     ]
    }
   ],
   "source": [
    "print(Y.shape)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4c56751-37cd-42cc-aa0c-3c553a560a19",
   "metadata": {},
   "source": [
    "## SPLITTING DATA INTO TRAINING AND TESTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2173f217-6e21-4b8a-89c4-4b6e9bd73861",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the data to train and test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e63a329e-2809-4228-a358-a5ce7159f64b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test= train_test_split(X,Y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "952230ca-a258-4eaa-9c9f-c70094b41559",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(120, 4)\n",
      "[[85 5.4 3.0 4.5]\n",
      " [124 6.3 2.7 4.9]\n",
      " [60 5.2 2.7 3.9]\n",
      " [128 6.1 3.0 4.9]\n",
      " [11 5.4 3.7 1.5]\n",
      " [75 6.4 2.9 4.3]\n",
      " [139 6.0 3.0 4.8]\n",
      " [51 7.0 3.2 4.7]\n",
      " [53 6.9 3.1 4.9]\n",
      " [99 5.1 2.5 3.0]\n",
      " [144 6.8 3.2 5.9]\n",
      " [143 5.8 2.7 5.1]\n",
      " [8 5.0 3.4 1.5]\n",
      " [137 6.3 3.4 5.6]\n",
      " [17 5.4 3.9 1.3]\n",
      " [72 6.1 2.8 4.0]\n",
      " [44 5.0 3.5 1.6]\n",
      " [29 5.2 3.4 1.4]\n",
      " [47 5.1 3.8 1.6]\n",
      " [134 6.3 2.8 5.1]\n",
      " [73 6.3 2.5 4.9]\n",
      " [140 6.9 3.1 5.4]\n",
      " [6 5.4 3.9 1.7]\n",
      " [103 7.1 3.0 5.9]\n",
      " [130 7.2 3.0 5.8]\n",
      " [45 5.1 3.8 1.9]\n",
      " [76 6.6 3.0 4.4]\n",
      " [100 5.7 2.8 4.1]\n",
      " [39 4.4 3.0 1.3]\n",
      " [7 4.6 3.4 1.4]\n",
      " [20 5.1 3.8 1.5]\n",
      " [136 7.7 3.0 6.1]\n",
      " [22 5.1 3.7 1.5]\n",
      " [43 4.4 3.2 1.3]\n",
      " [127 6.2 2.8 4.8]\n",
      " [79 6.0 2.9 4.5]\n",
      " [5 5.0 3.6 1.4]\n",
      " [34 5.5 4.2 1.4]\n",
      " [114 5.7 2.5 5.0]\n",
      " [58 4.9 2.4 3.3]\n",
      " [95 5.6 2.7 4.2]\n",
      " [113 6.8 3.0 5.5]\n",
      " [69 6.2 2.2 4.5]\n",
      " [16 5.7 4.4 1.5]\n",
      " [26 5.0 3.0 1.6]\n",
      " [106 7.6 3.0 6.6]\n",
      " [40 5.1 3.4 1.5]\n",
      " [37 5.5 3.5 1.3]\n",
      " [104 6.3 2.9 5.6]\n",
      " [89 5.6 3.0 4.1]\n",
      " [65 5.6 2.9 3.6]\n",
      " [96 5.7 3.0 4.2]\n",
      " [133 6.4 2.8 5.6]\n",
      " [86 6.0 3.4 4.5]\n",
      " [56 5.7 2.8 4.5]\n",
      " [33 5.2 4.1 1.5]\n",
      " [135 6.1 2.6 5.6]\n",
      " [87 6.7 3.1 4.7]\n",
      " [46 4.8 3.0 1.4]\n",
      " [9 4.4 2.9 1.4]\n",
      " [78 6.7 3.0 5.0]\n",
      " [19 5.7 3.8 1.7]\n",
      " [64 6.1 2.9 4.7]\n",
      " [77 6.8 2.8 4.8]\n",
      " [52 6.4 3.2 4.5]\n",
      " [71 5.9 3.2 4.8]\n",
      " [50 5.0 3.3 1.4]\n",
      " [42 4.5 2.3 1.3]\n",
      " [80 5.7 2.6 3.5]\n",
      " [59 6.6 2.9 4.6]\n",
      " [119 7.7 2.6 6.9]\n",
      " [23 4.6 3.6 1.0]\n",
      " [84 6.0 2.7 5.1]\n",
      " [74 6.1 2.8 4.7]\n",
      " [108 7.3 2.9 6.3]\n",
      " [1 5.1 3.5 1.4]\n",
      " [83 5.8 2.7 3.9]\n",
      " [15 5.8 4.0 1.2]\n",
      " [36 5.0 3.2 1.2]\n",
      " [117 6.5 3.0 5.5]\n",
      " [41 5.0 3.5 1.3]\n",
      " [30 4.7 3.2 1.6]\n",
      " [48 4.6 3.2 1.4]\n",
      " [120 6.0 2.2 5.0]\n",
      " [55 6.5 2.8 4.6]\n",
      " [63 6.0 2.2 4.0]\n",
      " [27 5.0 3.4 1.6]\n",
      " [98 6.2 2.9 4.3]\n",
      " [3 4.7 3.2 1.3]\n",
      " [57 6.3 3.3 4.7]\n",
      " [126 7.2 3.2 6.0]\n",
      " [31 4.8 3.1 1.6]\n",
      " [150 5.9 3.0 5.1]\n",
      " [148 6.5 3.0 5.2]\n",
      " [138 6.4 3.1 5.5]\n",
      " [14 4.3 3.0 1.1]\n",
      " [125 6.7 3.3 5.7]\n",
      " [70 5.6 2.5 3.9]\n",
      " [146 6.7 3.0 5.2]\n",
      " [10 4.9 3.1 1.5]\n",
      " [145 6.7 3.3 5.7]\n",
      " [129 6.4 2.8 5.6]\n",
      " [92 6.1 3.0 4.6]\n",
      " [24 5.1 3.3 1.7]\n",
      " [102 5.8 2.7 5.1]\n",
      " [118 7.7 3.8 6.7]\n",
      " [132 7.9 3.8 6.4]\n",
      " [54 5.5 2.3 4.0]\n",
      " [116 6.4 3.2 5.3]\n",
      " [111 6.5 3.2 5.1]\n",
      " [115 5.8 2.8 5.1]\n",
      " [149 6.2 3.4 5.4]\n",
      " [49 5.3 3.7 1.5]\n",
      " [21 5.4 3.4 1.7]\n",
      " [62 5.9 3.0 4.2]\n",
      " [105 6.5 3.0 5.8]\n",
      " [68 5.8 2.7 4.1]\n",
      " [122 5.6 2.8 4.9]\n",
      " [142 6.9 3.1 5.1]\n",
      " [110 7.2 3.6 6.1]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2e8fdb17-caa9-432b-aff7-70dd7117fb56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30,)\n",
      "['Iris-virginica' 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor']\n"
     ]
    }
   ],
   "source": [
    "print(y_test.shape)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9f6d1568-fdec-4127-b875-212b474d612c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30, 4)\n",
      "[[121 6.9 3.2 5.7]\n",
      " [18 5.1 3.5 1.4]\n",
      " [28 5.2 3.5 1.5]\n",
      " [81 5.5 2.4 3.8]\n",
      " [38 4.9 3.1 1.5]\n",
      " [4 4.6 3.1 1.5]\n",
      " [82 5.5 2.4 3.7]\n",
      " [93 5.8 2.6 4.0]\n",
      " [131 7.4 2.8 6.1]\n",
      " [90 5.5 2.5 4.0]\n",
      " [35 4.9 3.1 1.5]\n",
      " [109 6.7 2.5 5.8]\n",
      " [141 6.7 3.1 5.6]\n",
      " [112 6.4 2.7 5.3]\n",
      " [12 4.8 3.4 1.6]\n",
      " [66 6.7 3.1 4.4]\n",
      " [25 4.8 3.4 1.9]\n",
      " [2 4.9 3.0 1.4]\n",
      " [91 5.5 2.6 4.4]\n",
      " [101 6.3 3.3 6.0]\n",
      " [107 4.9 2.5 4.5]\n",
      " [32 5.4 3.4 1.5]\n",
      " [97 5.7 2.9 4.2]\n",
      " [61 5.0 2.0 3.5]\n",
      " [123 7.7 2.8 6.7]\n",
      " [147 6.3 2.5 5.0]\n",
      " [13 4.8 3.0 1.4]\n",
      " [67 5.6 3.0 4.5]\n",
      " [94 5.0 2.3 3.3]\n",
      " [88 6.3 2.3 4.4]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape)\n",
    "print(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d098c4f6-98cf-49a8-aae9-19fb441a937d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(120,)\n",
      "['Iris-versicolor' 'Iris-virginica' 'Iris-versicolor' 'Iris-virginica'\n",
      " 'Iris-setosa' 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-setosa' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-virginica'\n",
      " 'Iris-versicolor' 'Iris-virginica' 'Iris-setosa' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-virginica' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-virginica' 'Iris-versicolor' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-virginica' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-virginica' 'Iris-setosa' 'Iris-setosa' 'Iris-virginica'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa' 'Iris-virginica'\n",
      " 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-setosa' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-virginica' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-virginica' 'Iris-versicolor' 'Iris-versicolor'\n",
      " 'Iris-setosa' 'Iris-versicolor' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-virginica' 'Iris-setosa' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-setosa' 'Iris-virginica' 'Iris-versicolor'\n",
      " 'Iris-virginica' 'Iris-setosa' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-versicolor' 'Iris-setosa' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica']\n"
     ]
    }
   ],
   "source": [
    "print(y_train.shape)\n",
    "print(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7539f39d-92c4-47a1-9c6e-8662b5ef6567",
   "metadata": {},
   "source": [
    "## MODEL 1: SUPPORT VECTOR MACHINE ALGORITHM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d5cf8dda-2041-4f4f-8be7-5f5cd9aed03c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "model_svc=SVC()\n",
    "model_svc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ac91ac8d-dd9c-49aa-b696-6fe796c5863d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "prediction1 = model_svc.predict(X_test)\n",
    "\n",
    "#calculate the accuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test, prediction1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c0c25a7-5762-4d7f-a30e-315cae0e0e05",
   "metadata": {},
   "source": [
    "## MODEL 2: LOGISTIC REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "023fffd7-1c02-4c92-a1f1-03045b6005ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting categorical variables into numbers\n",
    "flower_mapping = {'Iris-setosa':0,'Iris-versicolor':1,'Iris-virginica':2}\n",
    "iris['Species']=iris['Species'].map(flower_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6c8d1226-c522-46c0-8bf5-b3dd969e76b3",
   "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>Id</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>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</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>NaN</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>NaN</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>NaN</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>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Sepal_Length  Sepal_Width  Petal_Length  Petal_Width  Species\n",
       "0   1           5.1          3.5           1.4          0.2      NaN\n",
       "1   2           4.9          3.0           1.4          0.2      NaN\n",
       "2   3           4.7          3.2           1.3          0.2      NaN\n",
       "3   4           4.6          3.1           1.5          0.2      NaN\n",
       "4   5           5.0          3.6           1.4          0.2      NaN"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6781ee5a-12d7-472a-8dba-aa5963fb0ed2",
   "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>Id</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>145</th>\n",
       "      <td>146</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>NaN</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",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  Sepal_Length  Sepal_Width  Petal_Length  Petal_Width  Species\n",
       "145  146           6.7          3.0           5.2          2.3      NaN\n",
       "146  147           6.3          2.5           5.0          1.9      NaN\n",
       "147  148           6.5          3.0           5.2          2.0      NaN\n",
       "148  149           6.2          3.4           5.4          2.3      NaN\n",
       "149  150           5.9          3.0           5.1          1.8      NaN"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.tail()\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5b22e3f9-e06f-44b4-99f9-e9388014b31f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model training completed.\n",
      "Model coefficients: [[-0.42442638  0.9664242  -2.51564942 -1.08210974]\n",
      " [ 0.53528316 -0.32058973 -0.20730191 -0.94264964]\n",
      " [-0.11085677 -0.64583447  2.72295133  2.02475937]]\n",
      "Model intercept: [  9.85515307   2.2309809  -12.08613397]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "# Load the iris dataset\n",
    "iris = load_iris(as_frame=True).frame\n",
    "\n",
    "# Prepare inputs and outputs\n",
    "X = iris[['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']].values\n",
    "y = iris[['target']].values.ravel()  # Ensure y is 1D\n",
    "\n",
    "# Initialize and fit the model\n",
    "model = LogisticRegression(max_iter=200)  # Increased max_iter for convergence\n",
    "model.fit(X, y)\n",
    "\n",
    "# Checking the model's performance (optional)\n",
    "print(\"Model training completed.\")\n",
    "print(\"Model coefficients:\", model.coef_)\n",
    "print(\"Model intercept:\", model.intercept_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8c89e9d6-3346-4df5-91f2-8ecc4890c0fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1f272c51-46eb-4f2b-9111-cebaaf405852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9733333333333334"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "01253fd7-3f21-4c4b-997c-eb8b7a9dff3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make prediction for all 150 species in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c42ea782-ce98-4005-87e0-e394be8798de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expected = y\n",
    "predicted = model.predict(X)\n",
    "predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bed409b0-9f3d-418c-b36a-a2e8302c772c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# summarize the fit of the model\n",
    "\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8937b466-f02c-444a-8bb3-6a3859e40c5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        50\n",
      "           1       0.98      0.94      0.96        50\n",
      "           2       0.94      0.98      0.96        50\n",
      "\n",
      "    accuracy                           0.97       150\n",
      "   macro avg       0.97      0.97      0.97       150\n",
      "weighted avg       0.97      0.97      0.97       150\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(metrics.classification_report(expected, predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1884062-42ad-4eb7-8142-d0da1294eed3",
   "metadata": {},
   "source": [
    "## setosa -> 0\n",
    "## versicolor -> 1\n",
    "## virginica ->2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "051dc9c5-ed6b-4ece-b85e-84651a3103ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[50  0  0]\n",
      " [ 0 47  3]\n",
      " [ 0  1 49]]\n"
     ]
    }
   ],
   "source": [
    "# confusion metrics\n",
    "print(metrics.confusion_matrix(expected, predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f20e1c7a-7075-4430-9e50-fb651d87d3bd",
   "metadata": {},
   "source": [
    "## MODEL3: DECISION TREE CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "982fec69-8441-41d6-9a6a-e194419efa16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" 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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model_DTC = DecisionTreeClassifier()\n",
    "model_DTC.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c2b79e4e-4314-40ef-b2ce-e6609a569afe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "prediction3= model_svc.predict(X_test)\n",
    "\n",
    "#calculate the accuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test, prediction3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d09aed84-c376-4954-bebb-55b6c42afa49",
   "metadata": {},
   "source": [
    "## New data for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "563cdec7-44e0-4d76-90cf-01d2ec609a86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2]\n"
     ]
    }
   ],
   "source": [
    "# New data for prediction\n",
    "X_new = np.array([[3, 2, 1, 0.2], [4.9, 2.2, 3.8, 1.1], [5.3, 2.5, 4.6, 1.9]])\n",
    "\n",
    "# Predicting the sizes of the iris flowers\n",
    "predicted_sizes = model.predict(X_new)\n",
    "\n",
    "# Output the predicted sizes\n",
    "print(predicted_sizes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cb6512a-f528-46cb-8165-a6fdbee1a140",
   "metadata": {},
   "source": [
    "setosa -> 0\n",
    "versicolor -> 1\n",
    "virginica ->2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd0936d8-54fe-4b86-aadb-3a590c4e0d1d",
   "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": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}