Iris-flowers/Iris_flowers_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iris plant classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following are the steps which we're going to cover:\n",
    "1. Import libraries\n",
    "2. Load dataset\n",
    "3. Summarize dataset\n",
    "4. Visualize dataset\n",
    "5. Evaluate algorithms\n",
    "6. Make predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import model_selection\n",
    "\n",
    "# Metrics\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Models\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('iris.csv', header=None, names=['sepal-length','sepal-width','petal-length','petal-width','class'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Summarize dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Dimensions of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Peek at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal-length</th>\n",
       "      <th>sepal-width</th>\n",
       "      <th>petal-length</th>\n",
       "      <th>petal-width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal-length  sepal-width  petal-length  petal-width        class\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Statistical summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal-length</th>\n",
       "      <th>sepal-width</th>\n",
       "      <th>petal-length</th>\n",
       "      <th>petal-width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal-length  sepal-width  petal-length  petal-width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Class-wise summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.groupby('class').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Data visualization\n",
    "We now have a basic idea about the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.plot(kind='box', subplots=True,layout=(2,2),sharex=False, sharey=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1abf7c4f7b8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Split into train and validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n",
      "(150,)\n"
     ]
    }
   ],
   "source": [
    "array = dataset.values\n",
    "\n",
    "X = array[:,:-1]\n",
    "y = array[:,-1]\n",
    "\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(105, 4)\n",
      "(45, 4)\n",
      "(105,)\n",
      "(45,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Test harness\n",
    "We will use 10-fold cross validation to estimate accuracy. \n",
    "In this, we train on 9 parts and test on 1 part and repeat for all combinations of train-test splits. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test options and metric=accuracy\n",
    "seed = 7\n",
    "scoring=\"accuracy\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are using the metric of `accuracy` to evaluate models. This is a **ratio** of the **number of correctly predicted instances** in **divided** by the **total number of instances** in the dataset multiplied by 100 to give a percentage (e.g. 95% accurate). We will be using the scoring variable when we run build and evaluate each model next."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Build models\n",
    "Let’s evaluate 6 different algorithms:\n",
    "\n",
    "1. Logistic Regression (LR)\n",
    "2. Linear Discriminant Analysis (LDA)\n",
    "3. K-Nearest Neighbors (KNN).\n",
    "4. Classification and Regression Trees (CART).\n",
    "5. Gaussian Naive Bayes (NB).\n",
    "6. Support Vector Machines (SVM).\n",
    "\n",
    "This is a good mixture of simple linear (LR and LDA), nonlinear (KNN, CART, NB and SVM) algorithms. We reset the random number seed before each run to ensure that the evaluation of each algorithm is performed using exactly the same data splits. It ensures the results are directly comparable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.932727 (0.074877)\n",
      "LDA: 0.971818 (0.059259)\n",
      "KNN: 0.951818 (0.065813)\n",
      "CART: 0.942727 (0.062065)\n",
      "NB: 0.943636 (0.061496)\n",
      "SVM: 0.970000 (0.064031)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agarw\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:442: UserWarning: The priors do not sum to 1. Renormalizing\n",
      "  UserWarning)\n"
     ]
    }
   ],
   "source": [
    "# Add algorithms\n",
    "models = []\n",
    "models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('SVM', SVC(gamma='auto')))\n",
    "\n",
    "# Evaluate each model in turn\n",
    "results = []\n",
    "names = []\n",
    "\n",
    "for name, model in models:\n",
    "    kfold = model_selection.KFold(n_splits=10, random_state=seed)\n",
    "    cv_results = model_selection.cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    print(\"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 Plot results of each model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.suptitle('Algorithm comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Prediction using best model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1 KNeighbors Classification alorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9777777777777777\n",
      "[[16  0  0]\n",
      " [ 0 17  1]\n",
      " [ 0  0 11]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        16\n",
      "Iris-versicolor       1.00      0.94      0.97        18\n",
      " Iris-virginica       0.92      1.00      0.96        11\n",
      "\n",
      "    avg / total       0.98      0.98      0.98        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Take KNN algorithm\n",
    "\n",
    "model = KNeighborsClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "y_predict = model.predict(X_test)\n",
    "print(accuracy_score(y_test, y_predict))\n",
    "print(confusion_matrix(y_test, y_predict))\n",
    "print(classification_report(y_test, y_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see accuracy of KNN algorithm on validation dataset is ~97.78% compared to Training dataset(~95.18%).  \n",
    "Confusion matrix shows it made only 1 error. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2 Linear Discriminant Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9777777777777777\n",
      "[[16  0  0]\n",
      " [ 0 17  1]\n",
      " [ 0  0 11]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        16\n",
      "Iris-versicolor       1.00      0.94      0.97        18\n",
      " Iris-virginica       0.92      1.00      0.96        11\n",
      "\n",
      "    avg / total       0.98      0.98      0.98        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Take LDA algorithm\n",
    "\n",
    "lda = LinearDiscriminantAnalysis()\n",
    "lda.fit(X_train, y_train)\n",
    "y_predict_lda = lda.predict(X_test)\n",
    "\n",
    "print(accuracy_score(y_test, y_predict_lda))\n",
    "print(confusion_matrix(y_test, y_predict_lda))\n",
    "print(classification_report(y_test, y_predict_lda))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3 Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8888888888888888\n",
      "[[16  0  0]\n",
      " [ 0 13  5]\n",
      " [ 0  0 11]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        16\n",
      "Iris-versicolor       1.00      0.72      0.84        18\n",
      " Iris-virginica       0.69      1.00      0.81        11\n",
      "\n",
      "    avg / total       0.92      0.89      0.89        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Take Logistics regression\n",
    "\n",
    "lr = LogisticRegression(solver='liblinear', multi_class='ovr')\n",
    "lr.fit(X_train, y_train)\n",
    "y_predict_lr = lr.predict(X_test)\n",
    "\n",
    "print(accuracy_score(y_test, y_predict_lr))\n",
    "print(confusion_matrix(y_test, y_predict_lr))\n",
    "print(classification_report(y_test, y_predict_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4 Decision tree classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9777777777777777\n",
      "[[16  0  0]\n",
      " [ 0 17  1]\n",
      " [ 0  0 11]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        16\n",
      "Iris-versicolor       1.00      0.94      0.97        18\n",
      " Iris-virginica       0.92      1.00      0.96        11\n",
      "\n",
      "    avg / total       0.98      0.98      0.98        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cart = DecisionTreeClassifier()\n",
    "cart.fit(X_train, y_train)\n",
    "y_predict_cart = cart.predict(X_test)\n",
    "\n",
    "print(accuracy_score(y_test, y_predict_cart))\n",
    "print(confusion_matrix(y_test, y_predict_cart))\n",
    "print(classification_report(y_test, y_predict_cart))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.5 Gaussion Naive Baye's algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "[[16  0  0]\n",
      " [ 0 18  0]\n",
      " [ 0  0 11]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        16\n",
      "Iris-versicolor       1.00      1.00      1.00        18\n",
      " Iris-virginica       1.00      1.00      1.00        11\n",
      "\n",
      "    avg / total       1.00      1.00      1.00        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "nb = GaussianNB()\n",
    "nb.fit(X_train, y_train)\n",
    "y_predict_nb = nb.predict(X_test)\n",
    "\n",
    "print(accuracy_score(y_test, y_predict_nb))\n",
    "print(confusion_matrix(y_test, y_predict_nb))\n",
    "print(classification_report(y_test, y_predict_nb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.6 Support Vector machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9777777777777777\n",
      "[[16  0  0]\n",
      " [ 0 17  1]\n",
      " [ 0  0 11]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        16\n",
      "Iris-versicolor       1.00      0.94      0.97        18\n",
      " Iris-virginica       0.92      1.00      0.96        11\n",
      "\n",
      "    avg / total       0.98      0.98      0.98        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "svc = SVC(gamma='auto')\n",
    "svc.fit(X_train, y_train)\n",
    "y_predict_svc = svc.predict(X_test)\n",
    "\n",
    "print(accuracy_score(y_test, y_predict_svc))\n",
    "print(confusion_matrix(y_test, y_predict_svc))\n",
    "print(classification_report(y_test, y_predict_svc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results\n",
    "\n",
    "Out of all algorithms, Gaussian Naive Baye's algorithm stands out as it has accuracy score of 100%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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