classification_of_IRIS_using_Scikit-Learn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification of IRIS dataset using Scikit-Learn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wodnj\\AppData\\Local\\Temp\\ipykernel_21104\\3867540888.py:2: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model, datasets, metrics\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IRIS data set\n",
    "#### https://en.wikipedia.org/wiki/Iris_flower_data_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://upload.wikimedia.org/wikipedia/commons/4/41/Iris_versicolor_3.jpg\" width=\"200\" height=\"200\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML \n",
    "Image(url = \"https://upload.wikimedia.org/wikipedia/commons/4/41/Iris_versicolor_3.jpg\", width = 200, height = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the data  (150, 4)\n",
      "Shape of the data  ['setosa' 'versicolor' 'virginica']\n",
      "Attributes  ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
      "[[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [4.7 3.2 1.3 0.2]\n",
      " [4.6 3.1 1.5 0.2]\n",
      " [5.  3.6 1.4 0.2]]\n",
      "[0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "# import Iris dataset from Scikit-Learn's datasets\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "print (\"Shape of the data \", iris.data.shape)\n",
    "print (\"Shape of the data \", iris.target_names)\n",
    "print (\"Attributes \", iris.feature_names)\n",
    "\n",
    "# view first 5 rows\n",
    "print (iris.data[range(5)])\n",
    "print (iris.target[range(5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>Class</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   Class    Name  \n",
       "0      0  setosa  \n",
       "1      0  setosa  \n",
       "2      0  setosa  \n",
       "3      0  setosa  \n",
       "4      0  setosa  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show it as a table\n",
    "df = pd.DataFrame(data = iris.data)\n",
    "df.columns = iris.feature_names\n",
    "df['Class'] = iris.target\n",
    "df['Name'] = iris.target_names[iris.target]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>Class</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
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      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     Class       Name  \n",
       "145      2  virginica  \n",
       "146      2  virginica  \n",
       "147      2  virginica  \n",
       "148      2  virginica  \n",
       "149      2  virginica  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   sepal length (cm)  150 non-null    float64\n",
      " 1   sepal width (cm)   150 non-null    float64\n",
      " 2   petal length (cm)  150 non-null    float64\n",
      " 3   petal width (cm)   150 non-null    float64\n",
      " 4   Class              150 non-null    int32  \n",
      " 5   Name               150 non-null    object \n",
      "dtypes: float64(4), int32(1), object(1)\n",
      "memory usage: 6.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data = df, x = 'sepal length (cm)', y = 'sepal width (cm)', hue = 'Class');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data = df, x = 'sepal length (cm)', y = 'sepal width (cm)', hue = 'Name');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.data[:, :2]  # take the first two features.\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5],\n",
       "       [4.9, 3. ],\n",
       "       [4.7, 3.2],\n",
       "       [4.6, 3.1],\n",
       "       [5. , 3.6],\n",
       "       [5.4, 3.9]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:6, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.loc[:, ['sepal length (cm)', 'sepal width (cm)']]  # take the first two features.\n",
    "y = df.Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)\n",
       "0                5.1               3.5\n",
       "1                4.9               3.0\n",
       "2                4.7               3.2\n",
       "3                4.6               3.1\n",
       "4                5.0               3.6"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "145    2\n",
       "146    2\n",
       "147    2\n",
       "148    2\n",
       "149    2\n",
       "Name: Class, Length: 150, dtype: int32"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create train-test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size = 0.33, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 100 entries, 96 to 102\n",
      "Data columns (total 2 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   sepal length (cm)  100 non-null    float64\n",
      " 1   sepal width (cm)   100 non-null    float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 2.3 KB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 50 entries, 73 to 109\n",
      "Data columns (total 2 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   sepal length (cm)  50 non-null     float64\n",
      " 1   sepal width (cm)   50 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 1.2 KB\n"
     ]
    }
   ],
   "source": [
    "X_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50,)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Model - Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression\n",
    "# instantiation\n",
    "logreg = linear_model.LogisticRegression(C = 1e5)\n",
    "\n",
    "# fit the classifier on the data\n",
    "# model fitting\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "# output the classifiers prediction on training set\n",
    "pred_train = logreg.predict(X_train)\n",
    "\n",
    "# output the classifiers prediction on testing set\n",
    "pred_test = logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 1, 0, 2, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 2, 0, 2, 1, 0,\n",
       "       1, 1, 1, 1, 2, 1, 0, 1, 2, 0, 0, 1, 2, 0, 1, 0, 0, 1, 1, 2, 1, 1,\n",
       "       2, 1, 0, 0, 1, 2, 0, 0, 0, 1, 2, 0, 2, 2, 0, 1, 2, 1, 2, 2, 0, 2,\n",
       "       1, 2, 1, 1, 1, 0, 2, 1, 0, 1, 2, 2, 0, 1, 2, 2, 0, 2, 0, 2, 2, 2,\n",
       "       1, 2, 1, 2, 1, 1, 0, 1, 1, 0, 1, 2])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.82"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# accuracy\n",
    "(y_train == pred_train).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_test == pred_test).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# misclassification rate\n",
    "(y_train != pred_train).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>6.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  Predicted  Actual\n",
       "73                 6.1               2.8          1       1\n",
       "18                 5.7               3.8          0       0\n",
       "118                7.7               2.6          2       2\n",
       "78                 6.0               2.9          1       1\n",
       "76                 6.8               2.8          2       1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test['Predicted'] = pred_test\n",
    "X_test['Actual'] = y_test\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot decision boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.006</td>\n",
       "      <td>3.428</td>\n",
       "      <td>1.462</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td>5.936</td>\n",
       "      <td>2.770</td>\n",
       "      <td>4.260</td>\n",
       "      <td>1.326</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.588</td>\n",
       "      <td>2.974</td>\n",
       "      <td>5.552</td>\n",
       "      <td>2.026</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "Name                                                                 \n",
       "setosa                  5.006             3.428              1.462   \n",
       "versicolor              5.936             2.770              4.260   \n",
       "virginica               6.588             2.974              5.552   \n",
       "\n",
       "            petal width (cm)  Class  \n",
       "Name                                 \n",
       "setosa                 0.246    0.0  \n",
       "versicolor             1.326    1.0  \n",
       "virginica              2.026    2.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Name\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = [\"setosa\", \"versicolor\", \"virginica\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepal length (cm)', 'sepal width (cm)', 'Predicted', 'Actual'], dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot the decision boundary\n",
    "# point in the mesh [x_min, m_max]x[y_min, y_max].\n",
    "\n",
    "def plot_clf_boundary(X, y, legend_labels):\n",
    "    \n",
    "    xlabel = X.columns.tolist()[0]\n",
    "    ylabel = X.columns.tolist()[1]\n",
    "    \n",
    "    X = X.values.copy()\n",
    "    \n",
    "    h = .02  # step size in the mesh\n",
    "    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "    Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "    # put the result into a color plot\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.figure(1, )\n",
    "\n",
    "    fig_kw = {\"figsize\":(7, 5)}\n",
    "    fig, ax = plt.subplots(**fig_kw)\n",
    "\n",
    "    ax.pcolormesh(xx, yy, Z, cmap = plt.cm.rainbow, shading = 'auto')\n",
    "\n",
    "    # plot also the training points\n",
    "    kwargs = {'edgecolor':\"k\",\n",
    "                 # 'facecolor':\"k\",\n",
    "                 'linewidth':1,\n",
    "                 'linestyle':'--',\n",
    "                }\n",
    "\n",
    "    # sns.scatterplot(data = X_test, x = \"sepal length (cm)\", y = \"sepal width (cm)\", hue = y_test, palette = plt.cm.rainbow, **kwargs)\n",
    "    scatter = ax.scatter(X[:, 0], X[:, 1], c = y, edgecolors = 'k', cmap = plt.cm.rainbow)\n",
    "    plt.xlabel(xlabel, fontdict = {'size': 16})\n",
    "    plt.ylabel(ylabel, fontdict = {'size': 16})\n",
    "\n",
    "    # produce a legend with the unique colors from the scatter\n",
    "    # get the handles and labels\n",
    "    handles, _ = scatter.legend_elements()\n",
    "    legend1 = ax.legend(# *scatter.legend_elements(),\n",
    "                        handles, legend_labels,\n",
    "                        loc=\"lower left\", title = \"Classes\")\n",
    "    ax.add_artist(legend1)\n",
    "\n",
    "    plt.xlim(xx.min(), xx.max())\n",
    "    plt.ylim(yy.min(), yy.max())\n",
    "    plt.xticks(())\n",
    "    plt.yticks(())\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\wodnj\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_clf_boundary(X = X_test, y = y_test, legend_labels = legend_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dhxu6c8wyql+/fjpy5Ij69eunZs2aKX/+/Jo8ebKSkpLUuXPnVP+eAHcL5QVIQ9WqVfXLL78of/78euuttxQcHKymTZuqU6dOevLJJ1WgQAG1bNlSx44du2U5qFmzph5++GFFRkaqdOnSat26tVq3bq3ixYvrxIkTatmyZapt5s2bp0qVKqlkyZJq3bq1OnbsqNDQULVp00YeHh4uD0ubNGmSHnzwQVWoUEFPP/20OnTooKpVq2rgwIHy9fXVqFGjMv34SFLevHk1d+5c+fv7a+DAgSpRooTat2+vFi1aqFSpUgoNDXVOng0ODlaPHj0UFxenKlWq6Mknn1Tbtm1VrFgxLV++PN1vU752R0yTJk30zDPPqGfPnho+fPhNczkcDn399dfKly+f3nzzTVWoUEEdO3ZU/fr1ncdvypQp9/XXCkh/TfIdOnSokpKS1KhRIz3yyCPq0KGDSpYsqUmTJqlv375pbnftmHXs2FFt27ZVz5491bNnzzvOM3fuXH311VcqX768xo8f7/J+zz33nA4ePKgXXnjhjt8HyBA7Hi4DmOLChQvWu+++azVq1MgKCgqyPD09rdy5c1t16tSxRo8ebR07dsxlfHoPqbt06ZI1cuRIq1SpUpaPj49VuHBhq1evXlZMTEyaD1pbtWqV1bdvX6tq1apWvnz5LF9fX+vBBx+0OnToYG3evNll3wsXLrSeffZZq0KFClbu3LmtHDlyWKVLl7Z69uxp7d2712VsZj6k7prDhw9bvXr1sooVK2Z5e3tbefPmtWrUqGG99tprVlxcnHNcUlKS9d5771nly5e3fH19rfz581udOnWyjhw5ku5xS0xMtF5++WWrRIkSlpeXV6qMaR27a44ePWo999xzVuHChS0vLy8rMDDQatWqlbVx48ZUY292XCwr/d9rem58SN2tpPeQOsuyrOnTp1vVqlWzcuTIYeXLl89q2bKlFRERYU2dOtWSZI0ePTrVNh988IFVvnx5y8fHx/lAumtudsyuufH3HRUVZeXJk8fy9va2tm3blmr8xYsXrVKlSlmSrPnz52fozwzcCYdl3YPp/QAAAJmEy0YAAMAolBcAAGAUygsAADAK5QUAABiF8gIAAIxCeQEAAEahvAAAAKN42h3gbkiMsecLy4Cbyf7Avf8uHQAwSdLV4xkax5kXAABgFMoLAAAwCuUFAAAYhfICAACMQnkBAABGobwAAACjUF4AAIBRKC8AAMAolBcAAGAUygsAADAK5QUAABiF8gIAAIxCeQEAAEahvAAAAKNQXgAAgFEoLwAAwCiUFwAAYBTKCwAAMArlBQAAGIXyAgAAjEJ5AQAARqG8AAAAo1BeAACAUSgvAADAKJQXAABgFMoLAAAwCuUFAAAYhfICAACMQnkBAABGobwAAACjUF4AAIBRKC8AAMAolBcAAGAUygsAADAK5QUAABiF8gIAAIxCeQEAAEahvAAAAKNQXgAAgFEoLwAAwCiUFwAAYBTKCwAAMArlBQAAGIXyAgAAjEJ5AQAARqG8AAAAo1BeAACAUSgvAADAKJQXAABgFMoLAAAwCuUFAAAYhfICAACMQnkBAABGobwAAACjUF4AAIBRKC/Qb9t3qu/Q0Qpr0UkV6z2mZb+uc1kfE3tWI8e+p7AWnVTzoVZ6ftDLOnrsuE1pkZX17hWug/s36GLcIa1b871q1axqdyRkcXwm7UF5gS5fvqIyJR/UyMF9Uq2zLEsvDH9NUSf+1IS3R2nu1E/0QEiwer7wL126fMWGtMiq2rZtoXffGa3Xx76vWnWaKWLHbv34w9cKCspndzRkUXwm7UN5gRqE1tKAf4araaN6qdYdPXZcEb/v1StD+qlSuTIqXrSQXhnSTwkJCfpx6cp7HxZZ1sAXntPkKTM1/cs52rPngPr0Ha5Lly6re7cOdkdDFsVn0j62lpeYmBiNHz9erVu3VmhoqEJDQ9W6dWu98847io6OtjMa/utqYqIkydvby7nMw8NDXt5e2rbjd7tiIYvx8vJS9eqVtWz5aucyy7K0bPka1a1bw8ZkyKr4TNrLtvKyefNmlS5dWhMmTFBAQIAaNmyohg0bKiAgQBMmTFDZsmX122+/2RUP/1W8aGEVyB+sjz6dpvNxF5SYmKgpM+bo1OkYRZ+JtTsesojAwLzy9PTU6VMxLstPn45WSP4gm1IhK+MzaS9Pu964f//+atu2rSZNmiSHw+GyzrIs9erVS/3799f69etvup+EhAQlJCS4LPNISJCPj0+mZ86KvDw99eGbL2vUuA9V77F2ypbNQ3VrVlODujVl2R0OAJAl2XbmJSIiQgMHDkxVXCTJ4XBo4MCB2r59+y33M27cOAUEBLj8vP3RpLuQOOuqULaU5k3/t9b//I1WfPe1Pn1/rM7FXVChB0LsjoYsIiYmVklJSQrOH+iyPDg4SH+e4hIz7j0+k/ayrbyEhIRo06ZN6a7ftGmT8ufPf8v9jBgxQufPn3f5GfZCr8yMiv/yz+mnvHly6+ix4/p97wGF1a9rdyRkEYmJidq6dYceCqvvXOZwOPRQWH1t2LDFxmTIqvhM2su2y0ZDhgzRP//5T23ZskVNmjRxFpVTp05p2bJl+vzzz/Xuu+/ecj8+Pj6pLhElXo1JZzTScunSZUVGnXC+Pn7ilPbuP6SAXP4qEBKsn5evVp7cASqQP0gHDh/RWx9O0kMNQlWvDpPScO988NHnmjrlA23ZukObN2/TgP7Pyc8vu6ZNn213NGRRfCbtY1t56du3rwIDA/XBBx/oP//5j5KTkyVJ2bJlU40aNTRt2jS1a9fOrnhZyq69B/Rs/2HO1+M//kyS1PKxpnrj5cGKPhOr8R9/pjOx5xSUL69aNGuiXt2fsSsusqi5cxcqKDCvxowaopCQIEVE/K4nnuys06f5nxXYg8+kfRyWZdk+7zIxMVExMX/9sgMDA+Xl5XWLLW6xv5jDmRELyFTZH2hgdwQAuK8lXc3Y09ttO/NyPS8vLxUoUMDuGAAAwAA8YRcAABiF8gIAAIxCeQEAAEahvAAAAKNQXgAAgFEoLwAAwCiUFwAAYBTKCwAAMArlBQAAGIXyAgAAjEJ5AQAARqG8AAAAo1BeAACAUSgvAADAKJQXAABgFMoLAAAwCuUFAAAYhfICAACMQnkBAABGobwAAACjUF4AAIBRKC8AAMAolBcAAGAUygsAADAK5QUAABiF8gIAAIxCeQEAAEahvAAAAKNQXgAAgFEoLwAAwCiUFwAAYBTKCwAAMArlBQAAGIXyAgAAjEJ5AQAARqG8AAAAo1BeAACAUSgvAADAKJQXAABgFMoLAAAwCuUFAAAYhfICAACMQnkBAABGobwAAACjUF4AAIBRKC8AAMAolBcAAGAUygsAADAK5QUAABiF8gIAAIxCeQEAAEahvAAAAKNQXgAAgFEoLwAAwCiUFwAAYBRPuwPcDdkfaGB3BCCVWfka2x0BcHHK02F3BMAtnHkBAABGobwAAACjUF4AAIBRKC8AAMAolBcAAGAUygsAADAK5QUAABiF8gIAAIxCeQEAAEahvAAAAKNQXgAAgFEoLwAAwCiUFwAAYBTKCwAAMArlBQAAGIXyAgAAjEJ5AQAARqG8AAAAo1BeAACAUSgvAADAKJQXAABgFMoLAAAwCuUFAAAYhfICAACMQnkBAABGobwAAACjUF4AAIBRKC8AAMAolBcAAGAUygsAADAK5QUAABiF8gIAAIxCeQEAAEbxzOjAQYMGZXin77//vlthAAAAbiXD5WXbtm0ur7du3aqkpCSVKVNGkrR//35ly5ZNNWrUyNyEAAAA18lweVmxYoXzn99//335+/tr+vTpypMnjyTp7Nmz6t69uxo0aJD5KQEAAP7LYVmWdbsbFSxYUEuWLFGFChVclu/atUuPPPKITpw4kWkB3eHpXdDW9wfSMitfY7sjAC5OeTrsjgC46HdsRobGuTVhNy4uTtHR0amWR0dH68KFC+7sEgAAIEPcKi+tW7dW9+7dNX/+fEVFRSkqKkrz5s1Tjx491KZNm8zOCAAA4JThOS/XmzRpkoYMGaKOHTsqMTHxrx15eqpHjx565513MjUgAADA9dya83JNfHy8Dh06JEkqUaKE/Pz8Mi3YnWDOC+5HzHnB/YY5L7jf3NU5L9ecPHlSJ0+eVKlSpeTn56c76EEAAAAZ4lZ5OXPmjJo0aaLSpUvr8ccf18mTJyVJPXr00ODBgzM1IAAAwPXcKi8DBw6Ul5eXIiMjlSNHDufy9u3ba/HixZkWDgAA4EZuTdhdsmSJfv75ZxUqVMhlealSpXT06NFMCQYAAJAWt868xMfHu5xxuSY2NlY+Pj53HAoAACA9bpWXBg0a6Msvv3S+djgcSklJ0fjx4xUWFpZp4QAAAG7k1mWj8ePHq0mTJvrtt9909epVDR06VL///rtiY2O1du3azM4IAADg5NaZl4oVK2r//v2qX7++WrZsqfj4eLVp00bbtm1TiRIlMjsjAACAk1tnXiIjI1W4cGGNHDkyzXVFihS542AAAABpcevMS/HixdP8YsYzZ86oePHidxwKAAAgPW6VF8uy5HCkfqz0xYsX5evre8ehAAAA0nNbl40GDRok6a+7i1555RWX26WTk5O1ceNGVa1aNVMDAgAAXO+2ysu2bdsk/XXmZefOnfL29nau8/b2VpUqVTRkyJDMTQgAAHCd2yovK1askCR1795dH330kXLlynVXQgEAAKTHrbuNPvzwQyUlJaVaHhsbK09PT0rN30DvXuEaPKi3QkKCtGPHbr3w4iva/Nt2u2Mhq/JwqMKQp1T0qXryDcqty6fO6sicX7Xng2/tToYsquu6D5SrcFCq5TumL9WvL0+3IVHW4lZ56dChg5o3b64+ffq4LJ8zZ44WLlyoH3/8MVPCwR5t27bQu++MVp++w7Vp8zYN6N9TP/7wtcpXbKjo6DN2x0MWVLZfc5UIb6pNAyYpbl+U8lR5ULU+/KcS4y7r4JSf7Y6HLGjOk6Pkke1/97zkLVNIrWaN0KFFm2xMlXW4dbfRxo0b0/wagMaNG2vjxo13HAr2GvjCc5o8ZaamfzlHe/YcUJ++w3Xp0mV179bB7mjIovLVLK0Ti7foz2XbdSkqRsd/2KRTq3Yqb7UH7Y6GLOpK7AVdij7v/CnWpJrOHTml4xv22B0tS3CrvCQkJKR52SgxMVGXL1++41Cwj5eXl6pXr6xly1c7l1mWpWXL16hu3Ro2JkNWdua3/QpuUEE5HwyRJAWUL6LA2mX05/IIm5MBkodXNpVpU097Zq+yO0qW4dZlo9q1a+uzzz7Txx9/7LJ80qRJqlGD/8CZLDAwrzw9PXX6VIzL8tOno1W2DF/9AHvs/fh7eeXMrmar35GVnCJHNg/temuuIuevszsaoAcfrSmfXDm0d+6vdkfJMtwqL2PHjlXTpk0VERGhJk2aSJKWLVumzZs3a8mSJZkW7tixYxo9erS++OKLdMckJCQoISHBZVl6D9EDYKbCLeqoSJt62tjn3zq/77hyVyyqqq921uU/z+ro3NW33gFwF5Xv0EhHV0Qo/tQ5u6NkGW5dNqpXr57Wr1+vQoUKac6cOfr+++9VsmRJ7dixQw0aNMi0cLGxsZo+/eaztseNG6eAgACXHyvlQqZlyGpiYmKVlJSk4PyBLsuDg4P056nUXwkB3AuVX+movZ98r2PfbVDc3mOK/GaNDny+WGUHtLA7GrI4/4L5VKh+Re3+v5V2R8lS3DrzIklVq1bVzJkz7+jNFy5ceNP1hw8fvuU+RowY4Xzy7zV58pW9o1xZWWJiorZu3aGHwupr4cK/7uJwOBx6KKy+/jNxqs3pkFVly+4tKyXFZZmVnMIZVtiuXLtGuhwTpyPLttsdJUtxu7wcOnRIU6dO1eHDh/Xhhx8qODhYP/30k4oUKaIKFSpkaB+tWrWSw+GQZVnpjrnVX04+Pj7y8fG5rW1wcx989LmmTvlAW7bu0ObN2zSg/3Py88uuadNn2x0NWdTJpdtU7oVWunT8jOL2RSl3pWIq/fxj+mMWEyRhI4dDZds11N5vVstKTrn1eGQaty4brVq1SpUqVdLGjRs1b948Xbx4UZIUERGh0aNHZ3g/BQoU0Pz585WSkpLmz9atW92Jhzs0d+5CDR32usaMGqItm5eoapXyeuLJzjp9OubWGwN3wbaR0xX1w0ZVf6u7mv36jqqM6qhDXy3XrvFz7Y6GLKxwgwrKVSiQu4xs4LBudtojHaGhoWrbtq0GDRokf39/RURE6MEHH9SmTZvUpk0bRUVFZWg/LVq0UNWqVfXaa6+luT4iIkLVqlVTSsrtNVpP74K3NR64F2bla2x3BMDFKU/OUuP+0u/YjAyNc+uy0c6dO9Oc7xIcHKyYmIz/3/lLL72k+Pj4dNeXLFnS+X1KAAAAkpvlJXfu3Dp58qSKFy/usnzbtm0qWDDjZz1udWeSn5+fGjVq5E5EAADwN+XWnJcOHTpo2LBh+vPPP+VwOJSSkqK1a9dqyJAh6tq1a2ZnBAAAcHKrvLz55psqW7asChcurIsXL6p8+fJq2LCh/vGPf+jll1/O7IwAAABObk3YvSYyMlK7du3SxYsXVa1aNZUqVSozs7mNCbu4HzFhF/cbJuzifnNXJ+xeU6RIERUuXFgSz1YBAAD3hluXjSRpypQpqlixonx9feXr66uKFStq8uTJmZkNAAAgFbfOvIwaNUrvv/+++vfvr9DQUEnS+vXrNXDgQEVGRqb73BYAAIA75dacl6CgIE2YMEHPPPOMy/JZs2apf//+t/Wsl7uBOS+4HzHnBfcb5rzgfpPROS9uXTZKTExUzZo1Uy2vUaOGkpKS3NklAABAhrhVXrp06aKJEyemWv7ZZ5+pU6dOdxwKAAAgPW7fbTRlyhQtWbJEdevWlSRt3LhRkZGR6tq1qwYNGuQc9/777995SgAAgP9yq7zs2rVL1atXlyQdOnRIkhQYGKjAwEDt2rXLOY7bpwEAQGZzq7zwZYkAAMAubs15iY6OTnfdzp073Q4DAABwK26Vl0qVKumHH35Itfzdd99V7dq17zgUAABAetwqL4MGDdJTTz2l3r176/Llyzp+/LiaNGmi8ePHa+bMmZmdEQAAwMmt8jJ06FCtX79eq1evVuXKlVW5cmX5+Phox44dat26dWZnBAAAcHL7u41KliypihUr6siRI4qLi1P79u0VEhKSmdkAAABScau8rF27VpUrV9aBAwe0Y8cOTZw4Uf3791f79u119uzZzM4IAADg5FZ5eeihh9S+fXtt2LBB5cqVU8+ePbVt2zZFRkaqUqVKmZ0RAADAya3nvCxZskSNGjVyWVaiRAmtXbtWb7zxRqYEAwAASMttnXl5/PHHdf78eWdxeeutt3Tu3Dnn+rNnz2rWrFmZGhAAAOB6t1Vefv75ZyUkJDhfv/nmm4qNjXW+TkpK0r59+zIvHQAAwA1uq7xYlnXT1wAAAHeb27dKAwAA2OG2yovD4Uj1TdF8czQAALiXbutuI8uy1K1bN/n4+EiSrly5ol69esnPz0+SXObDAAAA3A23VV7Cw8NdXnfu3DnVmK5du95ZIgAAgJu4rfIyderUu5UDAAAgQ5iwCwAAjEJ5AQAARqG8AAAAo1BeAACAUSgvAADAKJQXAABgFMoLAAAwCuUFAAAYhfICAACMQnkBAABGobwAAACjUF4AAIBRKC8AAMAolBcAAGAUygsAADAK5QUAABiF8gIAAIxCeQEAAEahvAAAAKNQXgAAgFEoLwAAwCiUFwAAYBTKCwAAMArlBQAAGIXyAgAAjEJ5AQAARqG8AAAAo3jaHQDIKiZ4/Gl3BMDFj8297Y4AuIUzLwAAwCiUFwAAYBTKCwAAMArlBQAAGIXyAgAAjEJ5AQAARqG8AAAAo1BeAACAUSgvAADAKJQXAABgFMoLAAAwCuUFAAAYhfICAACMQnkBAABGobwAAACjUF4AAIBRKC8AAMAolBcAAGAUygsAADAK5QUAABiF8gIAAIxCeQEAAEahvAAAAKNQXgAAgFEoLwAAwCiUFwAAYBTKCwAAMArlBQAAGIXyAgAAjEJ5AQAARqG8AAAAo1BeAACAUSgvAADAKJQXAABgFMoLAAAwCuUFAAAYhfICAACMQnkBAABGobwAAACjUF4AAIBRKC8AAMAolBcAAGAUygsAADAK5QUAABiF8gIAAIxCeQEAAEahvAAAAKNQXgAAgFEoLwAAwCiUFwAAYBTKCwAAMArlBQAAGIXyAgAAjEJ5AQAARvG0OwDuT717hWvwoN4KCQnSjh279cKLr2jzb9vtjoUsqlXX5mrVpYVCCueXJP2x/6imffCVNq7YZHMyZBXZSlaU98NPy6NwSXnkzqfLn76mpIj1zvXeT3SSZ41G8sgTJCUnKjnyoBIWTlfKkX02pv774swLUmnbtoXefWe0Xh/7vmrVaaaIHbv14w9fKygon93RkEWdPhmjSeM+V8/Heuu5x/to69ptGvfFaypWuqjd0ZBVePsqOeqwEmb/J83VKaeOK2H2fxQ/trcuvTdEKWdOKUf/N+TIGXCPg2YNlBekMvCF5zR5ykxN/3KO9uw5oD59h+vSpcvq3q2D3dGQRa1bul4blm9S1B/HdexwlD5/+wtdjr+sCtXL2x0NWUTy7t909fsvlRSxLs31Sb+tVPK+7bLO/KmUk5FKmPe5HNn95FGw+D1OmjVQXuDCy8tL1atX1rLlq53LLMvSsuVrVLduDRuTAX/x8PBQkxZh8s3hq9+37LY7DpBaNk951X9M1qWLSok6bHeavyXmvMBFYGBeeXp66vSpGJflp09Hq2yZEjalAqQHyxbXxIUfy9vHW5fjL2tkz9E6cuCo3bEAp2wVayv7s8Mlbx9ZcbG69PFIWfFxdsf6W7L9zMvly5e1Zs0a7d6d+v+grly5oi+//PKm2yckJCguLs7lx7KsuxUXgE0iDx3Ts4/8U88/2VfffblQIz8cpmKlmPOC+0fy/gjFj+urS+8OVtLuLcreYwRzXu4SW8vL/v37Va5cOTVs2FCVKlVSo0aNdPLkSef68+fPq3v37jfdx7hx4xQQEODyY6VcuNvR/7ZiYmKVlJSk4PyBLsuDg4P056lom1IBUlJiko4fOaH9Ow/o07em6ODuQ3q6Zxu7YwH/czVBVvRJpRzZq4QZH0opyfKq96jdqf6WbC0vw4YNU8WKFXX69Gnt27dP/v7+qlevniIjIzO8jxEjRuj8+fMuPw4P/7uY+u8tMTFRW7fu0ENh9Z3LHA6HHgqrrw0bttiYDHDl8PCQt7eX3TGA9Dk8JE8+o3eDrXNe1q1bp19++UWBgYEKDAzU999/rz59+qhBgwZasWKF/Pz8brkPHx8f+fj4uCxzOBx3K3KW8MFHn2vqlA+0ZesObd68TQP6Pyc/v+yaNn223dGQRT0/vIc2rNikU8dPK0fOHHq41UOqFlpFgzsOtzsasgofX3kEPeB86ciXXx6FHpQVf0FWfJy8m3VQ0o6NsuJi5fDLJe9GzeXInU9JW1ffZKdwl63l5fLly/L0/F8Eh8OhiRMnql+/fmrUqJFmzpxpY7qsa+7chQoKzKsxo4YoJCRIERG/64knO+v06ZhbbwzcBbkD82jkR8OVLziv4i/E69Cewxrccbh+W83ZQNwb2YqUUo6B452vfZ9+XpKUuH6prsz6WB4hhZW9blM5/AJkxccp+eh+XXr/JaWczPiVBGScw7Jxdmvt2rXVv39/denSJdW6fv366euvv1ZcXJySk5Nva7+e3gUzKyKQaUKDytodAXDxY0tvuyMALvz/81OGxtk656V169aaNWtWmus++eQTPfPMM9w5BAAAXNh65uVu4cwL7kececH9hjMvuN8YceYFAADgdlFeAACAUSgvAADAKJQXAABgFMoLAAAwCuUFAAAYhfICAACMQnkBAABGobwAAACjUF4AAIBRKC8AAMAolBcAAGAUygsAADAK5QUAABiF8gIAAIxCeQEAAEahvAAAAKNQXgAAgFEoLwAAwCiUFwAAYBTKCwAAMArlBQAAGIXyAgAAjEJ5AQAARqG8AAAAo1BeAACAUSgvAADAKJQXAABgFMoLAAAwCuUFAAAYhfICAACMQnkBAABGobwAAACjUF4AAIBRKC8AAMAolBcAAGAUygsAADAK5QUAABiF8gIAAIxCeQEAAEahvAAAAKNQXgAAgFEoLwAAwCiUFwAAYBTKCwAAMArlBQAAGIXyAgAAjEJ5AQAARqG8AAAAo1BeAACAUSgvAADAKJQXAABgFMoLAAAwCuUFAAAYhfICAACM4rAsy7I7BO5PCQkJGjdunEaMGCEfHx+74wB8JnFf4nN571FekK64uDgFBATo/PnzypUrl91xAD6TuC/xubz3uGwEAACMQnkBAABGobwAAACjUF6QLh8fH40ePZoJaLhv8JnE/YjP5b3HhF0AAGAUzrwAAACjUF4AAIBRKC8AAMAolBcAAGAUygvS9O9//1vFihWTr6+v6tSpo02bNtkdCVnYr7/+qubNm+uBBx6Qw+HQt99+a3ckZHHjxo1TrVq15O/vr+DgYLVq1Ur79u2zO1aWQXlBKrNnz9agQYM0evRobd26VVWqVNGjjz6q06dP2x0NWVR8fLyqVKmif//733ZHASRJq1atUt++fbVhwwYtXbpUiYmJeuSRRxQfH293tCyBW6WRSp06dVSrVi198sknkqSUlBQVLlxY/fv31/Dhw21Oh6zO4XBowYIFatWqld1RAKfo6GgFBwdr1apVatiwod1x/vY48wIXV69e1ZYtW9S0aVPnMg8PDzVt2lTr16+3MRkA3L/Onz8vScqbN6/NSbIGygtcxMTEKDk5Wfnz53dZnj9/fv355582pQKA+1dKSopefPFF1atXTxUrVrQ7TpbgaXcAAABM1rdvX+3atUtr1qyxO0qWQXmBi8DAQGXLlk2nTp1yWX7q1CmFhITYlAoA7k/9+vXTokWL9Ouvv6pQoUJ2x8kyuGwEF97e3qpRo4aWLVvmXJaSkqJly5YpNDTUxmQAcP+wLEv9+vXTggULtHz5chUvXtzuSFkKZ16QyqBBgxQeHq6aNWuqdu3a+vDDDxUfH6/u3bvbHQ1Z1MWLF3Xw4EHn6z/++EPbt29X3rx5VaRIERuTIavq27evZs6cqe+++07+/v7OOYEBAQHKnj27zen+/rhVGmn65JNP9M477+jPP/9U1apVNWHCBNWpU8fuWMiiVq5cqbCwsFTLw8PDNW3atHsfCFmew+FIc/nUqVPVrVu3exsmC6K8AAAAozDnBQAAGIXyAgAAjEJ5AQAARqG8AAAAo1BeAACAUSgvAADAKJQXAABgFMoLgPtWt27d1KpVK+frxo0b68UXX7znOVauXCmHw6Fz587d8/cGkBrlBcBt69atmxwOhxwOh7y9vVWyZEm99tprSkpKuqvvO3/+fL3++usZGkvhAP6++G4jAG5p1qyZpk6dqoSEBP3444/q27evvLy8NGLECJdxV69elbe3d6a8Z968eTNlPwDMxpkXAG7x8fFRSEiIihYtqt69e6tp06ZauHCh81LPG2+8oQceeEBlypSRJB07dkzt2rVT7ty5lTdvXrVs2VJHjhxx7i85OVmDBg1S7ty5lS9fPg0dOlQ3fnvJjZeNEhISNGzYMBUuXFg+Pj4qWbKkpkyZoiNHjji/CylPnjxyOBzO75tJSUnRuHHjVLx4cWXPnl1VqlTRN9984/I+P/74o0qXLq3s2bMrLCzMJScA+1FeAGSK7Nmz6+rVq5KkZcuWad++fVq6dKkWLVqkxMREPfroo/L399fq1au1du1a5cyZU82aNXNu895772natGn64osvtGbNGsXGxmrBggU3fc+uXbtq1qxZmjBhgvbs2aNPP/1UOXPmVOHChTVv3jxJ0r59+3Ty5El99NFHkqRx48bpyy+/1KRJk/T7779r4MCB6ty5s1atWiXpr5LVpk0bNW/eXNu3b1fPnj01fPjwu3XYALjDAoDbFB4ebrVs2dKyLMtKSUmxli5davn4+FhDhgyxwsPDrfz581sJCQnO8V999ZVVpkwZKyUlxbksISHByp49u/Xzzz9blmVZBQoUsMaPH+9cn5iYaBUqVMj5PpZlWY0aNbJeeOEFy7Isa9++fZYka+nSpWlmXLFihSXJOnv2rHPZlStXrBw5cljr1q1zGdujRw/rmWeesSzLskaMGGGVL1/eZf2wYcNS7QuAfZjzAsAtixYtUs6cOZWYmKiUlBR17NhRY8aMUd++fVWpUiWXeS4RERE6ePCg/P39XfZx5coVHTp0SOfPn9fJkydVp04d5zpPT0/VrFkz1aWja7Zv365s2bKpUaNGGc588OBBXbp0SQ8//LDL8qtXr6patWqSpD179rjkkKTQ0NAMvweAu4/yAsAtYWFhmjhxory9vfXAAw/I0/N/f534+fm5jL148aJq1Kihr7/+OtV+goKC3Hr/7Nmz3/Y2Fy9elCT98MMPKliwoMs6Hx8ft3IAuPcoLwDc4ufnp5IlS2ZobPXq1TV79mwFBwcrV65caY4pUKCANm7cqIYNG0qSkpKStGXLFlWvXj3N8ZUqVVJKSopWrVqlpk2bplp/7cxPcnKyc1n58uXl4+OjyMjIdM/YlCtXTgsXLnRZtmHDhlv/IQHcM0zYBXDXderUSYGBgWrZsqVWr16tP/74QytXrtSAAQMUFRUlSXrhhRf01ltv6dtvv9XevXvVp0+fmz6jpVixYgoPD9ezzz6rb7/91rnPOXPmSJKKFi0qh8OhRYsWKTo6WhcvXpS/v7+GDBmigQMHavr06Tp06JC2bt2qjz/+WNOnT5ck9erVSwcOHNBLL72kffv2aebMmZo2bdrdPkQAbgPlBcBdlyNHDv36668qUqSI2rRpo3LlyqlHjx66cuWK80zM4MGD1aVLF4WHhys0NFT+/v5q3br1Tfc7ceJEPf300+rTp4/Kli2r5557TvHx8ZKkggUL6tVXX9Xw4cOVP39+9evXT5L0+uuv65VXXtG4ceNUrlw5NWvWTD/88IOKFy8uSSpSpIjmzZunb7/9VlWqVNGkSZP05ptv3sWjA+B2Oaz0ZsMBAADchzjzAgAAjEJ5AQAARqG8AAAAo1BeAACAUSgvAADAKJQXAABgFMoLAAAwCuUFAAAYhfICAACMQnkBAABGobwAAACjUF4AAIBR/h/N/6Q7drqdewAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix =  pd.crosstab(index = y_test, columns = pred_test.ravel(), rownames = ['Expected'], colnames = ['Predicted'])\n",
    "sns.heatmap(confusion_matrix, annot = True, square = False, fmt = '', cbar = False)\n",
    "plt.title(\"Classification Matrix\", fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2744b7b9100>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics.ConfusionMatrixDisplay.from_predictions(y_true = y_test, y_pred = pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19,  0,  0],\n",
       "       [ 0,  8,  7],\n",
       "       [ 0,  3, 13]], dtype=int64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.confusion_matrix(y_true = y_test, y_pred = pred_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        19\n",
      "           1       0.73      0.53      0.62        15\n",
      "           2       0.65      0.81      0.72        16\n",
      "\n",
      "    accuracy                           0.80        50\n",
      "   macro avg       0.79      0.78      0.78        50\n",
      "weighted avg       0.81      0.80      0.80        50\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (metrics.classification_report(y_test, pred_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Model - Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train_set1, X_test_set1, y_train_set1, y_test_set1 = train_test_split(\n",
    "    X, y, test_size = 0.33, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decision Tree\n",
    "# instantiation\n",
    "from sklearn import tree\n",
    "\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "\n",
    "# fit the classifier on the data\n",
    "# model fitting\n",
    "clf = clf.fit(X_train_set1, y_train_set1)\n",
    "\n",
    "# output the classifiers prediction on training set\n",
    "pred_train1 = clf.predict(X_train_set1)\n",
    "\n",
    "# output the classifiers prediction on testing set\n",
    "pred_test1 = clf.predict(X_test_set1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>6.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  Predicted  Actual\n",
       "73                 6.1               2.8          1       1\n",
       "18                 5.7               3.8          0       0\n",
       "118                7.7               2.6          1       2\n",
       "78                 6.0               2.9          2       1\n",
       "76                 6.8               2.8          1       1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_set1['Predicted'] = pred_test1\n",
    "X_test_set1['Actual'] = y_test_set1\n",
    "X_test_set1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2745fa283a0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics.ConfusionMatrixDisplay.from_predictions(y_true = y_test_set1, y_pred = pred_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.95      0.97        19\n",
      "           1       0.44      0.53      0.48        15\n",
      "           2       0.50      0.44      0.47        16\n",
      "\n",
      "    accuracy                           0.66        50\n",
      "   macro avg       0.65      0.64      0.64        50\n",
      "weighted avg       0.67      0.66      0.66        50\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (metrics.classification_report(y_test_set1, pred_test1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Model - Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train_set2, X_test_set2, y_train_set2, y_test_set2 = train_test_split(\n",
    "    X, y, test_size = 0.33, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Support Vector Machine\n",
    "# instantiation\n",
    "from sklearn import svm\n",
    "\n",
    "clf2 = svm.SVC()\n",
    "\n",
    "# fit the classifier on the data\n",
    "# model fitting\n",
    "clf2.fit(X_train_set2, y_train_set2)\n",
    "\n",
    "# output the classifiers prediction on training set\n",
    "pred_train2 = clf2.predict(X_train_set2)\n",
    "\n",
    "# output the classifiers prediction on testing set\n",
    "pred_test2 = clf2.predict(X_test_set2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>6.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  Predicted  Actual\n",
       "73                 6.1               2.8          1       1\n",
       "18                 5.7               3.8          0       0\n",
       "118                7.7               2.6          2       2\n",
       "78                 6.0               2.9          1       1\n",
       "76                 6.8               2.8          2       1"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_set2['Predicted'] = pred_test2\n",
    "X_test_set2['Actual'] = y_test_set2\n",
    "X_test_set2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x27460fb7b50>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics.ConfusionMatrixDisplay.from_predictions(y_true = y_test_set2, y_pred = pred_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        19\n",
      "           1       0.67      0.53      0.59        15\n",
      "           2       0.63      0.75      0.69        16\n",
      "\n",
      "    accuracy                           0.78        50\n",
      "   macro avg       0.77      0.76      0.76        50\n",
      "weighted avg       0.78      0.78      0.78        50\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (metrics.classification_report(y_test_set2, pred_test2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare the Performances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression's Accuracy: 0.8\n",
      "Decision Tree's Acuracy: 0.66\n",
      "Suppor Vector Machine's Accuracy: 0.78\n"
     ]
    }
   ],
   "source": [
    "accuracy_test = metrics.accuracy_score(y_test, pred_test)\n",
    "accuracy_test_set1 = metrics.accuracy_score(y_test_set1, pred_test1)\n",
    "accuracy_test_set2 = metrics.accuracy_score(y_test_set2, pred_test2)\n",
    "\n",
    "print(\"Logistic Regression's Accuracy:\", accuracy_test)\n",
    "print(\"Decision Tree's Acuracy:\", accuracy_test_set1)\n",
    "print(\"Suppor Vector Machine's Accuracy:\", accuracy_test_set2)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.9.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}