Iris Species/Iris Species.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('seaborn-darkgrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Link**: [Iris Species](https://www.kaggle.com/uciml/iris)\n",
    "\n",
    "The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository.\n",
    "\n",
    "It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.\n",
    "\n",
    "The columns in this dataset are:\n",
    "- Id\n",
    "- SepalLengthCm\n",
    "- SepalWidthCm\n",
    "- PetalLengthCm\n",
    "- PetalWidthCm\n",
    "- Species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/Iris.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## **1. EXPLORE THE DATA TO GAIN INSIGHTS**\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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   Id             150 non-null    int64  \n",
      " 1   SepalLengthCm  150 non-null    float64\n",
      " 2   SepalWidthCm   150 non-null    float64\n",
      " 3   PetalLengthCm  150 non-null    float64\n",
      " 4   PetalWidthCm   150 non-null    float64\n",
      " 5   Species        150 non-null    object \n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 7.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "        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",
       "    </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  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               150\n",
       "SepalLengthCm     35\n",
       "SepalWidthCm      23\n",
       "PetalLengthCm     43\n",
       "PetalWidthCm      22\n",
       "Species            3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df[\"Species\"], palette = \"winter\")\n",
    "plt.title(\"Species\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x864 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 4, figsize = (20, 12))\n",
    "columns = [\"SepalLengthCm\", \"SepalWidthCm\", \"PetalLengthCm\", \"PetalWidthCm\"]\n",
    "for col in range(len(columns)):\n",
    "    sns.distplot(df[df[\"Species\"] == \"Iris-setosa\"][columns[col]], hist = False, kde_kws = {'shade': True, 'linewidth': 2}, label = \"Iris-setosa\", ax = ax[0, col])\n",
    "    sns.distplot(df[df[\"Species\"] == \"Iris-versicolor\"][columns[col]], hist = False, kde_kws = {'shade': True, 'linewidth': 2}, label = \"Iris-versicolor\", ax = ax[0, col])\n",
    "    sns.distplot(df[df[\"Species\"] == \"Iris-virginica\"][columns[col]], hist = False, kde_kws = {'shade': True, 'linewidth': 2}, label = \"Iris-virginica\", ax = ax[0, col])\n",
    "    sns.boxplot(x = \"Species\", y = columns[col], data = df, fliersize = 3, boxprops={'facecolor':'None'}, palette = \"winter\", ax = ax[1, col])\n",
    "    sns.swarmplot(x = \"Species\", y = columns[col], data = df, size = 4, palette = \"winter\", ax = ax[1, col])\n",
    "    sns.barplot(x = \"Species\", y = columns[col], data = df.groupby(\"Species\").std().reset_index(), palette = \"winter\", ax = ax[2, col])\n",
    "    ax[0, col].set_title(columns[col] + \" Distribution\")\n",
    "    ax[1, col].set_title(columns[col] + \" Boxplot\")\n",
    "    ax[2, col].set_title(columns[col] + \" Standard Devition\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "fig.savefig('data_distribution_species.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(\"Id\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), annot = True, fmt = \".2f\", cmap = \"Blues\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 815.875x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, palette = \"winter\", hue = \"Species\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## **2. PREPARE THE DATA**\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"Species\", axis = 1).values\n",
    "y = df[\"Species\"].values\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## **3. EXPLORE MANY DIFFERENT MODELS**\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Algorithm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "#Metrics\n",
    "from sklearn.metrics import classification_report, accuracy_score, precision_score, recall_score, plot_confusion_matrix, roc_curve, precision_recall_curve\n",
    "\n",
    "#Validation\n",
    "from sklearn.model_selection import cross_validate, cross_val_predict\n",
    "\n",
    "#Hyper Parameter\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#Ensemble\n",
    "from sklearn.ensemble import VotingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "algorithm = {\n",
    "    'Logistic Regression': LogisticRegression(solver = \"saga\", max_iter = 2000),\n",
    "    'K Neighbors Classifier': KNeighborsClassifier(),\n",
    "    'SVC': SVC(),\n",
    "    'Gaussian NB': GaussianNB(),\n",
    "    'Decision Tree Classifier': DecisionTreeClassifier()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_scoring(model):\n",
    "    scores = {}\n",
    "    score = cross_validate(estimator = model, X = X_train, y = y_train, cv = 10,\n",
    "                  scoring = ['accuracy'])\n",
    "    for key, value in score.items():\n",
    "        scores[key] = (round(value.mean(),4))\n",
    "        scores[key + 'std'] = (round(value.std(),4))\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {}\n",
    "parameter = None\n",
    "for key, value in algorithm.items():\n",
    "    results[key] = get_scoring(value)\n",
    "results = pd.DataFrame(results)\n",
    "results = results.rename(index = {'fit_time': 'Fit Time',\n",
    "                                  'score_time': 'Score Time',\n",
    "                                  'test_accuracy': 'Accuracy',\n",
    "                                  'score_timestd': 'Score Time Std',\n",
    "                                  'test_accuracystd': 'Accuracy Std',\n",
    "                                 })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = results.T.reset_index()\n",
    "results.rename(columns = {'index': 'Model'}, inplace = True)\n",
    "results.drop(['Fit Time', 'fit_timestd', 'Score Time', 'Score Time Std'], axis = 1, inplace = True)\n",
    "\n",
    "# ALGORITHM COMPARISON\n",
    "plt.figure(figsize = (20, 4))\n",
    "sns.barplot(x = 'Accuracy', y = 'Model', data = results.sort_values('Accuracy', ascending = False), palette = 'winter')\n",
    "plt.title(\"ACCURACY\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Accuracy Std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.9583</td>\n",
       "      <td>0.0559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K Neighbors Classifier</td>\n",
       "      <td>0.9333</td>\n",
       "      <td>0.0624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.9583</td>\n",
       "      <td>0.0559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gaussian NB</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.0553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>0.9250</td>\n",
       "      <td>0.0583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model  Accuracy  Accuracy Std\n",
       "0       Logistic Regression    0.9583        0.0559\n",
       "1    K Neighbors Classifier    0.9333        0.0624\n",
       "2                       SVC    0.9583        0.0559\n",
       "3               Gaussian NB    0.9500        0.0553\n",
       "4  Decision Tree Classifier    0.9250        0.0583"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**LOGISTIC REGRESSION**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10,\n",
       "             estimator=LogisticRegression(max_iter=5000, penalty='elasticnet',\n",
       "                                          solver='saga'),\n",
       "             param_grid={'C': array([0.2, 0.4, 0.6, 0.8, 1. ]),\n",
       "                         'l1_ratio': array([0. , 0.2, 0.4, 0.6, 0.8, 1. ])},\n",
       "             return_train_score=True, scoring='accuracy')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression(penalty = \"elasticnet\", solver = \"saga\", max_iter = 5000)\n",
    "parameters = {\"C\": np.linspace(0.2, 1, 5), \"l1_ratio\": np.linspace(0, 1, 6)}\n",
    "gscv = GridSearchCV(lr, param_grid = parameters, cv = 10, return_train_score = True, scoring = \"accuracy\")\n",
    "gscv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_C</th>\n",
       "      <th>param_l1_ratio</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>split5_train_score</th>\n",
       "      <th>split6_train_score</th>\n",
       "      <th>split7_train_score</th>\n",
       "      <th>split8_train_score</th>\n",
       "      <th>split9_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.183675</td>\n",
       "      <td>0.052891</td>\n",
       "      <td>0.001002</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>{'C': 1.0, 'l1_ratio': 0.6000000000000001}</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.972222</td>\n",
       "      <td>0.972222</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.006142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
       "27       0.183675      0.052891         0.001002         0.00001       1   \n",
       "\n",
       "   param_l1_ratio                                      params  \\\n",
       "27            0.6  {'C': 1.0, 'l1_ratio': 0.6000000000000001}   \n",
       "\n",
       "    split0_test_score  split1_test_score  split2_test_score  ...  \\\n",
       "27           0.916667                1.0           0.916667  ...   \n",
       "\n",
       "    split2_train_score  split3_train_score  split4_train_score  \\\n",
       "27            0.962963            0.962963            0.962963   \n",
       "\n",
       "    split5_train_score  split6_train_score  split7_train_score  \\\n",
       "27            0.962963            0.962963            0.962963   \n",
       "\n",
       "    split8_train_score  split9_train_score  mean_train_score  std_train_score  \n",
       "27            0.972222            0.972222          0.966667         0.006142  \n",
       "\n",
       "[1 rows x 32 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv_res = pd.DataFrame(gscv.cv_results_).sort_values(\"rank_test_score\")\n",
    "gscv_res.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1, l1_ratio=0.6, max_iter=5000, penalty='elasticnet',\n",
       "                   solver='saga')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression(penalty = \"elasticnet\", solver = \"saga\", max_iter = 5000, C = 1, l1_ratio = 0.6)\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        39\n",
      "Iris-versicolor       0.97      0.92      0.94        37\n",
      " Iris-virginica       0.93      0.98      0.96        44\n",
      "\n",
      "       accuracy                           0.97       120\n",
      "      macro avg       0.97      0.97      0.97       120\n",
      "   weighted avg       0.97      0.97      0.97       120\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = lr.predict(X_train)\n",
    "print(classification_report(y_train, y_pred))\n",
    "plot_confusion_matrix(lr, X_train, y_train, cmap = plt.cm.Blues, values_format = '.2f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**KNN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'leaf_size': array([20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32.,\n",
       "       33., 34., 35., 36., 37., 38., 39., 40.]),\n",
       "                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
       "                                         13, 14, 15, 16, 17, 18, 19, 20],\n",
       "                         'p': [1, 2]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "parameters = {\"p\": [1, 2], \"n_neighbors\": list(range(1, 21)), \"leaf_size\": np.linspace(20, 40, 21)}\n",
    "gscv = GridSearchCV(knn, param_grid = parameters, cv = 10, scoring = \"accuracy\")\n",
    "gscv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_leaf_size</th>\n",
       "      <th>param_n_neighbors</th>\n",
       "      <th>param_p</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>split5_test_score</th>\n",
       "      <th>split6_test_score</th>\n",
       "      <th>split7_test_score</th>\n",
       "      <th>split8_test_score</th>\n",
       "      <th>split9_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>0.001787</td>\n",
       "      <td>0.000404</td>\n",
       "      <td>0.004296</td>\n",
       "      <td>0.000453</td>\n",
       "      <td>37</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>{'leaf_size': 37.0, 'n_neighbors': 12, 'p': 2}</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.055277</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>0.001258</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>0.002575</td>\n",
       "      <td>0.000818</td>\n",
       "      <td>40</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>{'leaf_size': 40.0, 'n_neighbors': 12, 'p': 2}</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.055277</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>0.001355</td>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.003775</td>\n",
       "      <td>0.001306</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>{'leaf_size': 30.0, 'n_neighbors': 12, 'p': 2}</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.055277</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>0.001497</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.003403</td>\n",
       "      <td>0.001285</td>\n",
       "      <td>35</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>{'leaf_size': 35.0, 'n_neighbors': 14, 'p': 2}</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.055277</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>0.001628</td>\n",
       "      <td>0.000497</td>\n",
       "      <td>0.003741</td>\n",
       "      <td>0.000671</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>{'leaf_size': 29.0, 'n_neighbors': 14, 'p': 2}</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.055277</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "703       0.001787      0.000404         0.004296        0.000453   \n",
       "823       0.001258      0.000405         0.002575        0.000818   \n",
       "423       0.001355      0.000441         0.003775        0.001306   \n",
       "627       0.001497      0.000501         0.003403        0.001285   \n",
       "387       0.001628      0.000497         0.003741        0.000671   \n",
       "\n",
       "    param_leaf_size param_n_neighbors param_p  \\\n",
       "703              37                12       2   \n",
       "823              40                12       2   \n",
       "423              30                12       2   \n",
       "627              35                14       2   \n",
       "387              29                14       2   \n",
       "\n",
       "                                             params  split0_test_score  \\\n",
       "703  {'leaf_size': 37.0, 'n_neighbors': 12, 'p': 2}           0.916667   \n",
       "823  {'leaf_size': 40.0, 'n_neighbors': 12, 'p': 2}           0.916667   \n",
       "423  {'leaf_size': 30.0, 'n_neighbors': 12, 'p': 2}           0.916667   \n",
       "627  {'leaf_size': 35.0, 'n_neighbors': 14, 'p': 2}           0.916667   \n",
       "387  {'leaf_size': 29.0, 'n_neighbors': 14, 'p': 2}           0.916667   \n",
       "\n",
       "     split1_test_score  ...  split3_test_score  split4_test_score  \\\n",
       "703                1.0  ...                1.0                1.0   \n",
       "823                1.0  ...                1.0                1.0   \n",
       "423                1.0  ...                1.0                1.0   \n",
       "627                1.0  ...                1.0                1.0   \n",
       "387                1.0  ...                1.0                1.0   \n",
       "\n",
       "     split5_test_score  split6_test_score  split7_test_score  \\\n",
       "703                1.0                1.0                1.0   \n",
       "823                1.0                1.0                1.0   \n",
       "423                1.0                1.0                1.0   \n",
       "627                1.0                1.0                1.0   \n",
       "387                1.0                1.0                1.0   \n",
       "\n",
       "     split8_test_score  split9_test_score  mean_test_score  std_test_score  \\\n",
       "703           0.833333                1.0         0.966667        0.055277   \n",
       "823           0.833333                1.0         0.966667        0.055277   \n",
       "423           0.833333                1.0         0.966667        0.055277   \n",
       "627           0.833333                1.0         0.966667        0.055277   \n",
       "387           0.833333                1.0         0.966667        0.055277   \n",
       "\n",
       "     rank_test_score  \n",
       "703                1  \n",
       "823                1  \n",
       "423                1  \n",
       "627                1  \n",
       "387                1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv_res = pd.DataFrame(gscv.cv_results_).sort_values(\"rank_test_score\")\n",
    "gscv_res.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=12)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 12, leaf_size = 30, p = 2)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        39\n",
      "Iris-versicolor       0.97      0.95      0.96        37\n",
      " Iris-virginica       0.96      0.98      0.97        44\n",
      "\n",
      "       accuracy                           0.97       120\n",
      "      macro avg       0.98      0.97      0.98       120\n",
      "   weighted avg       0.98      0.97      0.97       120\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = knn.predict(X_train)\n",
    "print(classification_report(y_train, y_pred))\n",
    "plot_confusion_matrix(knn, X_train, y_train, cmap = plt.cm.Blues, values_format = '.2f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SVC**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=SVC(),\n",
       "             param_grid={'C': [1, 2, 3],\n",
       "                         'kernel': ('linear', 'poly', 'rbf', 'sigmoid')},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = SVC()\n",
    "parameters = {\"C\": [1, 2, 3], \"kernel\": (\"linear\", \"poly\", \"rbf\", \"sigmoid\")}\n",
    "gscv = GridSearchCV(svc, param_grid = parameters, cv = 10, scoring = \"accuracy\")\n",
    "gscv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_C</th>\n",
       "      <th>param_kernel</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>split5_test_score</th>\n",
       "      <th>split6_test_score</th>\n",
       "      <th>split7_test_score</th>\n",
       "      <th>split8_test_score</th>\n",
       "      <th>split9_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002753</td>\n",
       "      <td>0.001243</td>\n",
       "      <td>0.001569</td>\n",
       "      <td>0.000805</td>\n",
       "      <td>1</td>\n",
       "      <td>linear</td>\n",
       "      <td>{'C': 1, 'kernel': 'linear'}</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>0.055902</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002151</td>\n",
       "      <td>0.000620</td>\n",
       "      <td>0.001235</td>\n",
       "      <td>0.000621</td>\n",
       "      <td>1</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1, 'kernel': 'rbf'}</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>0.055902</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.001310</td>\n",
       "      <td>0.000642</td>\n",
       "      <td>0.000980</td>\n",
       "      <td>0.000446</td>\n",
       "      <td>2</td>\n",
       "      <td>linear</td>\n",
       "      <td>{'C': 2, 'kernel': 'linear'}</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>0.055902</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.002231</td>\n",
       "      <td>0.000423</td>\n",
       "      <td>0.001291</td>\n",
       "      <td>0.000462</td>\n",
       "      <td>2</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 2, 'kernel': 'rbf'}</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>0.055902</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.001194</td>\n",
       "      <td>0.000401</td>\n",
       "      <td>0.000898</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>3</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 3, 'kernel': 'rbf'}</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>0.055902</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
       "0        0.002753      0.001243         0.001569        0.000805       1   \n",
       "2        0.002151      0.000620         0.001235        0.000621       1   \n",
       "4        0.001310      0.000642         0.000980        0.000446       2   \n",
       "6        0.002231      0.000423         0.001291        0.000462       2   \n",
       "10       0.001194      0.000401         0.000898        0.000299       3   \n",
       "\n",
       "   param_kernel                        params  split0_test_score  \\\n",
       "0        linear  {'C': 1, 'kernel': 'linear'}           0.916667   \n",
       "2           rbf     {'C': 1, 'kernel': 'rbf'}           0.916667   \n",
       "4        linear  {'C': 2, 'kernel': 'linear'}           0.916667   \n",
       "6           rbf     {'C': 2, 'kernel': 'rbf'}           0.916667   \n",
       "10          rbf     {'C': 3, 'kernel': 'rbf'}           0.916667   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0                 1.0           0.916667                1.0   \n",
       "2                 1.0           0.916667                1.0   \n",
       "4                 1.0           0.833333                1.0   \n",
       "6                 1.0           0.916667                1.0   \n",
       "10                1.0           0.916667                1.0   \n",
       "\n",
       "    split4_test_score  split5_test_score  split6_test_score  \\\n",
       "0                 1.0                1.0                1.0   \n",
       "2                 1.0                1.0                1.0   \n",
       "4                 1.0                1.0                1.0   \n",
       "6                 1.0                1.0                1.0   \n",
       "10                1.0                1.0                1.0   \n",
       "\n",
       "    split7_test_score  split8_test_score  split9_test_score  mean_test_score  \\\n",
       "0                 1.0           0.833333           0.916667         0.958333   \n",
       "2                 1.0           0.833333           0.916667         0.958333   \n",
       "4                 1.0           0.916667           0.916667         0.958333   \n",
       "6                 1.0           0.833333           0.916667         0.958333   \n",
       "10                1.0           0.833333           0.916667         0.958333   \n",
       "\n",
       "    std_test_score  rank_test_score  \n",
       "0         0.055902                1  \n",
       "2         0.055902                1  \n",
       "4         0.055902                1  \n",
       "6         0.055902                1  \n",
       "10        0.055902                1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv_res = pd.DataFrame(gscv.cv_results_).sort_values(\"rank_test_score\")\n",
    "gscv_res.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, kernel='linear')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = SVC(C = 1, kernel = \"linear\")\n",
    "svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        39\n",
      "Iris-versicolor       1.00      0.92      0.96        37\n",
      " Iris-virginica       0.94      1.00      0.97        44\n",
      "\n",
      "       accuracy                           0.97       120\n",
      "      macro avg       0.98      0.97      0.97       120\n",
      "   weighted avg       0.98      0.97      0.97       120\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = svc.predict(X_train)\n",
    "print(classification_report(y_train, y_pred))\n",
    "plot_confusion_matrix(svc, X_train, y_train, cmap = plt.cm.Blues, values_format = '.2f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**GAUSSIAN NB**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        39\n",
      "Iris-versicolor       0.92      0.92      0.92        37\n",
      " Iris-virginica       0.93      0.93      0.93        44\n",
      "\n",
      "       accuracy                           0.95       120\n",
      "      macro avg       0.95      0.95      0.95       120\n",
      "   weighted avg       0.95      0.95      0.95       120\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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/n4YNG+Zb/kPHeNetW/fQRH369CnIdxNCCKuk4snGeD/77DNSU1NZuHAhCxcuBGDMmDFMmTIFnU6Hm5sbkyZNyjefhwbe69evP1HFhBDC2j3pg2vh4eGEh4ff9/natWsfK5+HBt7hw4db/rxv3z4uX75MkyZNqFWr1mMVIIQQ1sbqHxmeNWsWf/31F+fPn0en07FkyRJmzZqlRN2EEKJYPOk83iIrP78Tjhw5wvTp07G3t6d79+5cvnxZiXoJIUSxedKba0Ul3x5vTk4OmZmZqFQqcnJyUKtlIoQQ4ulm9Ws1DBgwgB49enDz5k169erFwIEDFaiWEEIUH6tfney1116jTZs2JCQkUK1aNVxdXZWolxBCFBuNtfd4jx8/zoQJE0hOTqZKlSpERUVRr149JeomhBDFwuqHGj7++GOmT5/Oc889x5kzZ4iKimL16tVK1E0IIYpFCc8myz/w2tjY8NxzzwFQr149dDpdsVdKCCGKk9X2eO88MqzVaomMjOSll14iNjY23+XOhBDC2lntzbU7jwy/8MILAMTHx+Pk5ET9+vWVqZkQQhQTq+3x3v3IcFJSEkajEbPZTFJSkiIVE0KI4qKx9keGw8LCOHbsGLdv3yYjI4Pq1auzfv16JeomhBDFooRHGvJ/ZDguLo4tW7bQtm1btmzZgo2NjRL1EkKIYqNWqQq8FYd8e7wODg6oVCpu3bpF+fLlyc7OLpaKiMdjMpn4YNo6Tp67gl6n5d/hAXhUr2g5vi3mODOWbUOnVePv25oB3V/ON01poFapmDmoJbUrO2MymQlevp+L1w0AdG/lzuAOdfH9eEeeNCoVfBLYggbVXcgymvjgiwNcSDLg/owjc4e0xgycvvw/xq48hNlcAl+qmOTkmBgzYx1xl5LQqNXMGONHzapuluO7fjnBv7/cgY1eQ4/OLfDzbY3JZCJ89tec+vMqer2WaaP74F7t6fsNlfTNtXx7vA0bNmT58uU888wzjBo1CqPRmG+mly9fpnfv3nk+i4mJeeTi6sVlyZIlltXhCyowMJDz588XU42Kxpb/xpKZaWTH5x8yYfibhM+JthzLNuYwbvZGoucPZ/uyYL785hcSk1Mfmaa06ORZFYA3p+xg+je/E+nXDICG1V3xa1f7gTdVXmtWHRudGt+Pd/DxhqNM6JubJrLvi3wS/Tvdpu5EpVLR5YVqyn0RBezadxKAjQtGMmrwa0xasNlyLNuYw6QFm/nq0/fYtmQEa77bT9KNVHb8fILMLCPfLAom9J3Xmbzw25KqfqEU5tU/RSHfHm9ISAjp6enY2NgQExND06ZNn6ggLy+vJ0pXWO+8806JlFvcDvx+ng5tcmeYvNS4FsdOJViOnYn/C49qFXFxtkevgVaetdl/7E8OHY9/aJrS4oejl9n5+xUAqrk5kPx3Bq4Oesb18mT8miPMHNjyvjQt6lTkx+PXAPgt7gZN3SsA0MS9PPvP5N5M3nP8Ku0bPsu230rP6nyd2zWmQ+sGAFxJvElF13+miv55MZGaVd0o52SPXqeheZNaHIqN47eTF2jf4nkAmjV05/iZSyVS98Iq6R7vQwPvp59++sBof+zYMUJCQgqUeWBgIK6urqSmptK1a1cuXrxIUFAQI0eOxGAwkJGRwejRo2nZ8p+/DNnZ2fj4+LB582bs7e1ZtmwZWq2Wzp07ExERQWZmJjY2NkyaNImcnByGDh2Ki4sLXl5e2Nvbs2nTJtRqNc2aNSM0NJQxY8bg4+NDixYtGDt2LFevXiU7O5uIiAgaNWpEWFgYly5dIicnh0GDBuHj42OpS2pqKqNHj8ZgMJCTk8PIkSNp3bo1r7/+Ou7u7uj1+hJbmzgtPQNnBzvLvlqtxmjMQavV5B5z/OeYo70NqYaMR6YpTXJMZub+v9a81qw67yzcy6zBrZiw5gi3s3IeeL6jnY602/8MoZlMZjTqvC+HMWRk42xf+h4e0mo1hExZxY69x1k4caDlc0N6Bk4OtpZ9Rztb0tIzMNzKwOmu35BGrXoqf0NWO6vBw8OjSAq48xbO6Ojcy9qEhASSk5NZsWIFN27c4MKFC3nO1+l0dOrUiR07dtCtWze2bt3K8uXLiYqKIjAwkPbt27N//35mzpzJqFGjuH79Ohs3bkSv1/PWW28RERGBp6cnq1evzjMssnbtWqpWrcrs2bM5e/Ys+/bt4+TJk7i6ujJjxgwMBgM9evSgVatWljSLFi2iTZs2DBgwgMTERPz8/Ni1axe3bt3i/fffp0GDBkXSRk/CycEWw61My77ZbLb8+J0cbEm7lWE5ZriVSTknu0emKW1GLtvPZOejHJzxJtf/zuCT/i2w0WmoW6UcE/1eZPyaI5ZzDbezcbD956+CSqUix2TGdNd4rqOtjr9vZSn5FRQzKyyApBupdB86h51fhmJvZ4Ojgy3pd/1WDLczcHa0xdHelvS7flump/Q39KRDCNnZ2YSFhXHlyhWysrIYOnQozz33HGPGjEGlUlGnTh0mTJiQ7/K5Dw283bt3f6KK3eveVwXVqVOHgIAAQkJCMBqNBAYGcvjwYebOnQvkvrGzV69eREZG4uHhgbu7O66urpw9e5bFixezbNkyzGaz5dHlatWqodfrAZg6dSqff/45M2fOxNPTE/Ndd0Li4uIswx1169albt26REVF0aZNGwAcHR2pXbs2ly79c+l0/vx5fH19AahUqRKOjo7cvHnzgd/rDq1amefAX37Bg60xJ+jTpRm/xsbT8Lkq6P/v99+4dmXiL13HYEjHyd6G/Uf/JGRAB3QaHppGKXuiuhRr/s72enRaNTdSM1CrVGjUKm7dzsTVXotOo0arhrbPV7TUo17VcjjZ6enzci1Gvd4AW70WZzste6K64GSn5cAnr3Mr00hlVwfSM7NpXad46w9Q3kGZ/ylrtvzK1aT/8cGgTmjNtmg1KtycdNjaaGjRoAoJV5PBmEFOji2/HY/jo0EdcbLVsG3vCfr7NufX4/E0qlNFsfrecTP9wVcuj+NJVxX/9ttvcXFxYcaMGaSkpNC9e3eef/55goODadmyJePHj2f37t14e3s/Mp98x3gL695/Wc6cOUN6ejpLliwhKSmJvn37smfPHr766qs855nNZpYtW4afnx+Q2wMfPHgwzZo14/z58xw6dAggz78s69evJyoqChsbG4YMGcLRo0ctx2rXrs3x48fp2LEjly5dYs6cObzwwgscPnwYb29vDAYDZ8+epVq1annSHD58mAYNGpCYmEhqaiouLi73lXs3o6kQjfUYung1Zef+07wy4FPAzPzx/Vi15RDptzIZ2KMtk4J78Mb7CzCbzfj7tsKtgssD0zzk6rvY/GvCD8Wav51ew5whrXmmnB1ajYp3Fv7M9qO547LVKjjw2dC2vD55OwD//n+t0WrUtI/YyieBLahf3YX0rCz6z43hz79S8ajkxMyBLdFp1fxw7AoffnEQkwLTGk4v6J3/SUWgXctGjP5kDR0Hz8m9ITusG2u2HSX9dib+b7QhbOgbvPH+QtQqMz26tMTWwYmXWzRk2y+neGXALMxmMzPG+BVJIFTak/Z4u3TpQufOnS37Go2GkydP0qJFCyD3XtYvv/xS8oH3Xu7u7ixYsIBNmzah0+kYMWLEA8/r2bMnc+fOtVz6h4aGEhkZSWZmJhkZGYwbN+6+NPXq1aNnz564urpSqVIlmjZtahni6Nu3L2FhYfTr14+cnBzCwsKoV68eERER+Pn5kZmZyfDhw6lQoYIlv3fffZewsDC2b99ORkYGEydORKtVvMkeSK1WM3usX57P6rpXtvz5Na/GvObVGL0GS3B9UJrS5nZWDu8u+vmBxy7fSLcEXYARy/azJ6oLZjOE/ufX+86PS0yjx7RdxVbXkmZvZ8OCqIEPPd7x5UZ0fLkR5R00luCqVquZ8oEy/zAUpye9KnVwcADAYDAwYsQIgoODmTZtmiWQOzg4kJaWlm8+KrP50f+EJyYmWrrVnTt3pl69ek88s6EsyMh/tp2i7g681uDZQatKugp57InqUuy98MelVI+3oO4OvNaiknPhbnR+8N2ZAp/7qW/e9cevXbvGsGHD8Pf3p2fPnnh5eRETEwPArl272LdvH+PHj39knvkOdURERPDWW2+RlZVF8+bN+fjjjwtcYSGEsEZqVcG3uyUnJzN48GBGjx5Nz549AWjQoAEHDx4Ecp9XaN68ef7l53dCZmYmrVu3RqVS4eHhIY8MCyGeek/6luHPPvuM1NRUFi5cSGBgIIGBgQQHBzNv3jz69OlDdnZ2njHgh8l3wFKv17N3715MJhPHjh2zzCAQQoin1ZOuwRAeHk54ePh9n69cufLxys/vhEmTJhEdHU1KSgqff/45kZGRj1WAEEJYG/VjbMUh3x5v5cqVmT17djEVL4QQyrPaR4bvaNu2reXP//vf/6hevTrbtm0r1koJIURxstpHhu/4+ed/5kReuXKF+fPnF2uFhBCiuFn9W4bvVrVqVeLi4oqrLkIIoYjiWuC8oAq0LOSdpzKSkpLyPNklhBBPI6sf4/Xx8cHZ2RkAGxsbGjVqVOyVEkKI4mT1Qw3Lly9nzZo1StRFCCEUoSrh113mG3jLlSvHl19+Sa1atSwrct0900EIIZ422uKaoFvQ8vM7wdXVldOnT3P69GnLZxJ4hRBPs+J6l1pBPTTwBgcHM2fOHKZOnapkfYQQothZ7RjvnTctCCFEaWO1sxouXbr00Bc5FvRll0IIYY2sdh6vra3tQ98rJoQQTzONtd5cc3NzK7IXXgohhDVRW+t0MnlQQghRWlntGG9oaKiS9RBCCMVY7awGIYQoraz25poQQpRWJT3UUML39oQQQnkatarA24P8/vvvBAYGAnDy5EnatWtnefnl1q1b8y1ferxCiDKnMD3OpUuX8u2332JnZwfAH3/8waBBgxg8eLAi5QshxFNJpVIVeLtXjRo1mDdvnmX/xIkT/Pe//yUgIICwsDAMBkO+5UvgFUKUOarH2O7VuXNntNp/BguaNGnCRx99xKpVq6hevToLFizIt3wJvEKIMketUhV4y4+3t7fluQdvb2/++OOP/Msv9DcQQoinTGF6vPcaMmQIsbGxAOzfv5+GDRvmm0Zurgkhyhx1ET5BERkZyaRJk9DpdLi5uTFp0qR800jgFUKUOYW91K9WrRrr168HoGHDhqxdu/ax0kvgFUKUOSX9BgqV2Ww2l2gNSpkMY0nXIC+9BrJySroW//j7VnZJVyGP8g4abqZbUQMB7u1HlXQV8vh51Ue0DZhe0tXI4/bR+YVKv+HY1QKf28uzSqHKehDp8QohypyS7vFK4BVClDkaCbxCCKGsEl4jRwKvEKLsKenVySTwCiHKHKt99Y8QQpRW0uMVQgiFqaTHK4QQypJZDUIIoTAZahBCCIVJ4BVCCIXJGK8QQiisCFeFfCISeIUQZU5B3ixRnCTwCiHKHBlqEEIIhclQgxBCKKyke7zyskshRJmjUhV8e5Dff/+dwMBAAC5evIifnx/+/v5MmDABk8mUb/kSeIUQZU5h3jK8dOlSwsPDyczMBGDq1KkEBwezevVqzGYzu3fvzrd8CbxCiDJHo1IVeLtXjRo1mDdvnmX/5MmTtGjRAgAvLy/27duXb/kSeIUQZU8hurydO3dGq/3n9pjZbLa8SsjBwYG0tLR8i5eba0KIMqcob66p1f/0X9PT03F2ds4/TZGVLoQQT4nC3ly7W4MGDTh48CAAMTExNG/ePN80EniFEGVOYW6u3Ss0NJR58+bRp08fsrOz6dy5c75pZKhBCFH2FHKkoVq1aqxfvx6AWrVqsXLlysdKL4FXCFHmyFoNQgihMHm9uxBCKE3WahBCCGWV9FoNEniFEGWOvPpHCCEUJoFXCCEUJkMNQgihMOnxiidiMpn4YNo6Tp67gl6n5d/hAXhUr2g5vi3mODOWbUOnVePv25oB3V/ON01pk5NjYsyMdcRdSkKjVjNjjB81q7pZju/65QQLVu4EVPT2aYmfb2tMJhPhs7/m1J9X0eu1TBvdB/dqpa+N3Fwd+e9XoXQfNp9zFxMB+HhUD/68mMQX0T/fd/6sMX1pWKcqWdlGRkxeRfzlZGpVc2PhhEDMZjOnzl/jw+nrMXuRn3sAABWNSURBVJvNSn+VJ1LS08kUfWT48uXL9O7dO89nMTExrFu37onzXLJkCbGxsQU6N7+yoqOjC7SWpjXY8t9YMjON7Pj8QyYMf5PwOdGWY9nGHMbN3kj0/OFsXxbMl9/8QmJy6iPTlEa79p0EYOOCkYwa/BqTFmy2HMs25jBpwWY2zX+fdf8ezprv9pN0I5UdP58gM8vIN4uCCX3ndSYv/Lakql9stBo1s8f6cTsjG4AKLo5smDuU17waP/B8J0c7bGy0dB7yKVHzNzM5uAcAH496i8mLvsfnnTmoVCp82j84vVUqymeGn0CJ93i9vLwKlf6dd94psrJ69OhRqLoo6cDv5+nQpj4ALzWuxbFTCZZjZ+L/wqNaRVyc7dFroJVnbfYf+5NDx+MfmqY06tyuMR1aNwDgSuJNKro6Wo79eTGRmlXdcHW252Z6Ds2b1OJQbBy/nbxA+xbPA9CsoTvHz1wqkboXp0nB3fki+mdGDewEgIO9DZ8s2UrHNg0feL69nQ27950C4PCJC3jWrwFA0+er88tv5wDYue8k/2pVny3/LVgnqKSVyTHewMBAXF1dSU1NpWvXrly8eJGgoCBGjhyJwWAgIyOD0aNH07JlS0ua7OxsfHx82Lx5M/b29ixbtgytVsvp06fx8fEhOTmZjRs3YjKZGDFiBJcvX2bVqlWUK1cOnU6Hj48PAHFxcfTt25cPPviAypUrc+nSJRo3bkxUVBTz5s3Dzc2NPn36MHnyZGJjY8nOziYoKIhXX32V8ePH89dff5GSkoKXlxfBwcEl0XwApKVn4OxgZ9lXq9UYjTlotZrcY47/HHO0tyHVkPHINKWVVqshZMoqduw9zsKJAy2fG9IzcHKwtew72tmSlp6B4VYGTne1kUatKlVt5Pd6S5JTDOw5cMoSeBOu3iDh6o2HBl6NWk1q+m3LvslkQqNRW9agBTDcysTZ0fZBya1SmX3Zpa+vL97e3kRH517uJiQkkJyczIoVK7hx4wYXLlzIc75Op6NTp07s2LGDbt26sXXrVpYvX87p06ct5zg7O7No0SJu3rxJZGQkmzZtQq/X079///vKv3DhAsuXL8fOzo6OHTty/fp1y7Hdu3eTkpLC119/zfXr11m5ciXPP/88np6e9OrVi8zMzIcGXq1amf+p5RxtycjIRP9/8cBsNmNvk7tT3smW9NsZ6DW5V0q3bmdSoZzdI9MopbyD8gFsxcf9SUxO5V8DP+XXDWE42NlQ1c2erMwsNGoV5R00GLOzqFLBnkvl7FCZsu6qp5lnyukVre/Pqz4qtrzdqz0DwKgB3tja6Pnvyo+4dOU6xhwTFSuUw2jMYchbbfKkKedoy6zQ3qQabgFQqYIzP/3nQ9xcHCx1dXKww8HBtljrfkfbgOmFz6SsBt5atWrl2a9Tpw4BAQGEhIRgNBoJDAzk8OHDzJ07F4AhQ4bQq1cvIiMj8fDwwN3dHVdX1wfmmZCQQO3atbGzy+25vPDCC/eVX6NGDRwdcy89K1asaHl/EkB8fDyenp6WY6NGjcJgMHD8+HEOHDiAo6MjWVlZD/xexvzfc1ckXmriwbaYE/h2aMah4/HUr12FrJzcYx41K/NnwnUSb6bj6mTDz7/9yfsBHcgx89A0Svn7lnIFRm8/xLXrfzOsX0cyTBrMqPj7tplMUw4Vn6nIuYtJXE8xkGXWEnPkT/q/1Z70LDPf/XSSV9o05beTF6jj/iw305VtpCIJLAXw3WcjCZm61nJzLfRtH5JupN53c+33zRP4+bfzDItaSfNG7oS+/Rq9Ri5izax3mb9yD7/8do5ZY/qy98hZvtn5myJ1L6wyOdQA5LlMAThz5gzp6eksWbKEpKQk+vbty549e/jqq6/ynGc2m1m2bBl+fn735XlnJfgaNWoQFxdHRkYGer2e2NhYPDw8Hln+3Tw8PPjhhx8ASEtLIzg4mPbt2+Pk5MTEiRO5ePEi69evz/PKD6W9/kpTfjx4mk6DPwXMzB/fjw0/HCL9ViYDe7RlcnAP3gpagNlsJsC3FVWecXlgmtKsi1cTRn+yht5B88g25jA+qBvbY2JJv52J/xttCB/2Jt2DFpFtNNHbpyWVK7rQuZ0zew+focf7czGbzcwYc//vrKxYFBnIx4u+J81wm8xMI9uXhwAqhk/MXQIxfM43zB3nh06r5eyFv9i8+2jJVvgxyHSy/+Pu7s6CBQvYtGkTOp2OESNGPPC8nj17MnfuXFq1avXQvMqXL8/bb7+Nv78/Li4uZGZmotVqMRqNBapLhw4d2L9/P35+fuTk5DBs2DCqVKlCSEgIR44cwc7Ojpo1a5KUlESlSpWe6PsWllqde2f6bnXdK1v+/JpXY17zaoxeg6VX+6A0pZm9nQ0LogY+9HjHlxvRu1PTPD1atVrNlA96PzRNaeL73tw8+9OWbs2zPzTyn05PyCdr70t/PiGJ19+de9/nT4OSnk6mMj8tE+8eg9FoZOnSpQwdOhSAgIAAgoODeemll4q97IyCxXbF3B14rcHft7JLugp5lHfQKD6UkB/39qNKugp5/LzqI8WGPwrq9tH5hUp/NvFWgc+tW8m+UGU9iNX0eIuSVqvl9u3bdO/eHZ1OR5MmTQr0HiQhRNkgC6EXk5CQEEJCQkq6GkIIK1TSQw2lNvAKIcRDFTLyduvWDScnJyD3/WtTp059rPQSeIUQZU5hppPdmXp674yrxyGvdxdClDkqVcG3e50+fZrbt28zePBg+vfvz7Fjxx67fOnxCiHKnMLcW7O1tbU80HXhwgXefvttfvjhB7TagodTCbxCiDKnMEMNtWrVombNmqhUKmrVqoWLiwvXr1/n2WefLXAeMtQghChzCjPU8PXXX/PJJ58AkJiYiMFgoGLFx1uzWXq8QogypzCTGnr27MnYsWPx8/NDpVIxZcqUxxpmAAm8QogyqDBjvHq9nk8//bRQ5UvgFUKUQfLkmhBCKKrMLoQuhBAlRZaFFEIIhZXZhdCFEKLESI9XCCGUJauTCSGEwmSMVwghFFZS70q8QwKvEKLMkaEGIYRQmAw1CCGEwmQ6mRBCKEx6vEIIoTAJvEIIoTAZahBCCIVJj1cIIRQm08mEEEJp0uMVQghlyRivEEIorDALoZtMJiIjIzlz5gx6vZ7JkydTs2bNxyv/yYsXQoinlOoxtnvs2rWLrKws1q1bxwcffGB54/DjkB6vEKLMKcxQw5EjR2jXrh0Anp6enDhx4rHzkMBbxGytsEWtqU62zrqSrsJ9Kjlb14Xf7aPzS7oK97HGOhWGXSF+hgaDAUdHR8u+RqPBaDQ+1iveresXJ4QQVs7R0ZH09HTLvslkeqygCxJ4hRDisTRr1oyYmBgAjh07Rt26dR87D5XZbDYXdcWEEKK0ujOr4ezZs5jNZqZMmULt2rUfKw/p8VqBy5cv07t37zyfxcTEsG7duiIva+fOnSQmJhZ5vkVByXbIz5IlS4iNjX2sNIGBgZw/f76YanS/4mivx/ne+ZUVHR3N7t27n7gu1kqtVjNx4kTWrl3LunXrHjvogtxcs1peXl7Fku9//vMfIiMjqVSpUrHkX9SKqx3y884775RIuYVV2PZ6nO+dX1k9evQoVF1KMwm8ViQwMBBXV1dSU1Pp2rUrFy9eJCgoiJEjR2IwGMjIyGD06NG0bNkyT7odO3awdOlStFotVatWZfr06aSnpzNu3DhSUlIACA8P59q1a5w6dYrQ0FBWr17NypUr2bJlC1qtlubNmzN69GiOHDnCtGnT0Gq1ODs7M3PmTADGjRtHWloaKSkp9OrVC39/f6tqh+zsbHx8fNi8eTP29vYsW7YMrVZL586diYiIIDMzExsbGyZNmkROTg5Dhw7FxcUFLy8v7O3t2bRpE2q1mmbNmhEaGsqYMWPw8fGhRYsWjB07lqtXr5KdnU1ERASNGjUiLCyMS5cukZOTw6BBg/Dx8bHUJTU1ldGjR2MwGMjJyWHkyJG0bt2a119/HXd3d/R6PbNmzbLK9jp9+jQ+Pj4kJyezceNGTCYTI0aM4PLly6xatYpy5cqh0+ks3zcuLo6+ffvywQcfULlyZS5dukTjxo2Jiopi3rx5uLm50adPHyZPnkxsbCzZ2dkEBQXx6quvMn78eP766y9SUlLw8vIiODi4yNrE2kngtTK+vr54e3sTHR0NQEJCAsnJyaxYsYIbN25w4cKF+9J8//33DBw4kK5du7Jp0yYMBgOLFy+mVatW+Pv7c+HCBcaOHcuaNWuoX78+kZGRxMfHs23bNtauXYtWqyUoKIgff/yRX3/9FW9vb4YMGcKePXtITU0lJSWFrl270qlTJxITEwkMDCzWwPsk7aDT6ejUqRM7duygW7dubN26leXLlxMVFUVgYCDt27dn//79zJw5k1GjRnH9+nU2btyIXq/nrbfeIiIiAk9PT1avXo3RaLTku3btWqpWrcrs2bM5e/Ys+/bt4+TJk7i6ujJjxgwMBgM9evSgVatWljSLFi2iTZs2DBgwgMTERPz8/Ni1axe3bt3i/fffp0GDBlbbXqdPn7ac4+zszKJFi7h58yaRkZFs2rQJvV5P//797yv/woULLF++HDs7Ozp27Mj169ctx3bv3k1KSgpff/01169fZ+XKlTz//PN4enrSq1cvMjMzJfCKklWrVq08+3Xq1CEgIICQkBCMRiOBgYEcPnyYuXPnAjBkyBDGjh3L4sWLWbNmDR4eHnTs2JGzZ89y4MABtm3bBuT2wu4WFxdH06ZN0elyJzQ2b96cc+fO8d577/HZZ58xYMAAKlWqRJMmTXBzc+PLL79kx44dODo65glM1tQOvXr1IjIyEg8PD9zd3XF1deXs2bMsXryYZcuWYTabLd+3WrVq6PV6AKZOncrnn3/OzJkz8fT05O77zXFxcZZL6rp161K3bl2ioqJo06YNkDu1qHbt2ly6dMmS5vz58/j6+gJQqVIlHB0duXnz5gO/V1EpqvZ6UJ4JCQnUrl0bOzs7AF544YX7yq9Ro4ZlbmvFihXJzMy0HIuPj8fT09NybNSoURgMBo4fP86BAwdwdHQkKyuriFri6SCB18rc+9rpM2fOkJ6ezpIlS0hKSqJv377s2bOHr776ynLOnDlzCAoKokKFCowfP56dO3fi4eHBG2+8ga+vLzdu3GDDhg2W/M1mMx4eHnzxxRcYjUY0Gg2HDh2iW7dufPfdd3Tv3p3Q0FAWL17M+vXrSUtLw9PTE39/fw4cOMBPP/1kle0AYDabWbZsGX5+fgB4eHgwePBgmjVrxvnz5zl06BCQe4PkjvXr1xMVFYWNjQ1Dhgzh6NGjlmO1a9fm+PHjdOzYkUuXLjFnzhxeeOEFDh8+jLe3NwaDgbNnz1KtWrU8aQ4fPkyDBg1ITEwkNTUVFxeX+8otSkXVXne7U9caNWoQFxdHRkYGer2e2NhYPDw8Hln+3Tw8PPjhhx8ASEtLIzg4mPbt2+Pk5MTEiRO5ePEi69evx2w2l/hr15UigdfKubu7s2DBAjZt2oROp2PEiBH3ndOkSRMGDRqEi4sLDg4OvPLKK7zyyiuMGzeO9evXYzAYGD58OJDbW/noo4/4/PPPee211/Dz88NkMvHiiy/SsWNHYmNjGTNmDPb29uh0OiZOnMiVK1eIjIzku+++w8XFBY1GQ1ZWlqXHaC3tANCzZ0/mzp1rufQPDQ0lMjKSzMxMMjIyGDdu3H1p6tWrR8+ePXF1daVSpUo0bdrUcsnet29fwsLC6NevHzk5OYSFhVGvXj0iIiLw8/MjMzOT4cOHU6FCBUt+7777LmFhYWzfvp2MjAwmTpz42BPsC+tJ2+tBypcvz9tvv42/vz8uLi5kZmai1WoLfOXToUMH9u/fj5+fHzk5OQwbNowqVaoQEhLCkSNHsLOzo2bNmiQlJT01N30LS+bxCiEeyWg0snTpUoYOHQpAQEAAwcHBvPTSSyVcs6eX9HiFEI+k1Wq5ffs23bt3R6fT0aRJE5o3b17S1XqqSY9XCCEUJk+uCSGEwiTwCiGEwiTwCiGEwiTwihJz8OBBWrduTWBgIIGBgfTu3fu+eaYFNXPmTKKjozl16hTz5z980e7HWSQoJiaGMWPG3FfnUaNGPTRNdHS05THr/DzOuaJ0kVkNokS1atWK2bNnA5CVlUWXLl148803cXZ2fqL86tevT/369R96/GlbJEiUThJ4hdUwGAyo1Wo0Gk2ehV+WLFlCZGQkFy9exGQyERwcTMuWLdm+fTuLFi2ifPnyZGdn4+HhwcGDB1m7di2zZ89mw4YNrFmzBpPJRIcOHWjcuHGeRYLWrVvH999/j0qlwsfHh/79+3P+/HnCwsKws7PDzs6OcuXKPbS+K1euZMeOHRiNRpycnJg3bx6Quzj2gAEDMBgMBAUF8corr/Drr78ye/ZsNBoN1atXZ+LEiUo1q7BCEnhFiTpw4ACBgYGoVCp0Oh0RERE4ODgA/yz8snr1alxdXZkyZQopKSn069ePLVu2MGPGDDZs2ICLi8t9yxneuHGDpUuX8u2336LX6/nkk0946aWXLIsEJSQksHXrVlavXo1KpWLgwIG0bduWuXPnMmLECF5++WWWLFlCXFzcA+ttMpn43//+x4oVK1Cr1QwZMoTjx48DYGdnx5IlS7h58ya9evWiXbt2REREsHr1aipUqMCcOXP45ptvFH+aTVgP+T8vStTdQw33urNIy9mzZzly5IhlgW6j0UhycjKOjo6WhV3uXbjl0qVL1KlTB1tbWwDCwsLyHD979ixXr15l4MCBAPz9998kJCRw7tw5mjRpAuS+4uVhgVetVqPT6QgJCcHe3p6//vrL8gjtiy++iEqlokKFCjg5OZGSkkJSUpJl9a2MjAxefvllatSo8VhtJUoPCbzCat1ZMMXDw4PKlSvz3nvvkZGRwaJFi3B2diYtLY2bN29Svnx5jh8/TuXKlS1p7yzscmdNiREjRjBu3Lg8iwQ999xzLFu2DJVKxYoVK6hbty4eHh4cPXoULy+vR762+/Tp0+zatYsNGzZw+/ZtevToYVnV7E7P9/r169y6dQtXV1cqV67MwoULcXJyYvfu3djb23Pt2rVibD1hzSTwCqvXt29fwsPD6devHwaDAX9/f/R6PVOnTmXIkCGUK1fuvsv2Owu79OvXD5VKxauvvkqlSpXyLBLUunVr/Pz8yMrKokmTJlSqVIkJEyYwatQoli9fTvny5bGxsXlgnWrWrImdnR09evRAr9dTsWJFkpKSgNwebf/+/bl16xYTJ05Eo9Ewbtw43nnnHcxmMw4ODkyfPl0CbxkmjwwLIYTCZB6vEEIoTAKvEEIoTAKvEEIoTAKvEEIoTAKvEEIoTAKvEEIoTAKvEEIoTAKvEEIo7P8D3cdWf3IUS4MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = gnb.predict(X_train)\n",
    "print(classification_report(y_train, y_pred))\n",
    "plot_confusion_matrix(gnb, X_train, y_train, cmap = plt.cm.Blues, values_format = '.2f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DECISION TREE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'criterion': ('gini', 'entropy'),\n",
       "                         'max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         'min_samples_leaf': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         'min_samples_split': [2, 3, 4, 5, 6, 7, 8, 9, 10]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dct = DecisionTreeClassifier()\n",
    "parameters = {\"criterion\": (\"gini\", \"entropy\"), \"max_depth\": list(range(1, 11)), \"min_samples_split\": list(range(2, 11)), \"min_samples_leaf\": list(range(1, 11))}\n",
    "gscv = GridSearchCV(dct, param_grid = parameters, cv = 10, scoring = \"accuracy\")\n",
    "gscv.fit(X_train, y_train)"
   ]
  },
  {
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_criterion</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_min_samples_leaf</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>split5_test_score</th>\n",
       "      <th>split6_test_score</th>\n",
       "      <th>split7_test_score</th>\n",
       "      <th>split8_test_score</th>\n",
       "      <th>split9_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1630</th>\n",
       "      <td>0.001501</td>\n",
       "      <td>0.000671</td>\n",
       "      <td>0.000608</td>\n",
       "      <td>0.000496</td>\n",
       "      <td>entropy</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 9, 'min_...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.055277</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>0.001824</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.000972</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>gini</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 4, 'min_sam...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.055277</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>0.000891</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>gini</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 5, 'min_sam...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.055277</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1539</th>\n",
       "      <td>0.001080</td>\n",
       "      <td>0.000308</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.000489</td>\n",
       "      <td>entropy</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 8, 'min_...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.055277</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>0.002073</td>\n",
       "      <td>0.000447</td>\n",
       "      <td>0.000763</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>entropy</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 4, 'min_...</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.941667</td>\n",
       "      <td>0.053359</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "1630       0.001501      0.000671         0.000608        0.000496   \n",
       "280        0.001824      0.000418         0.000972        0.000072   \n",
       "370        0.000891      0.000297         0.000500        0.000500   \n",
       "1539       0.001080      0.000308         0.000399        0.000489   \n",
       "1185       0.002073      0.000447         0.000763        0.000405   \n",
       "\n",
       "     param_criterion param_max_depth param_min_samples_leaf  \\\n",
       "1630         entropy               9                      2   \n",
       "280             gini               4                      2   \n",
       "370             gini               5                      2   \n",
       "1539         entropy               8                      2   \n",
       "1185         entropy               4                      2   \n",
       "\n",
       "     param_min_samples_split  \\\n",
       "1630                       3   \n",
       "280                        3   \n",
       "370                        3   \n",
       "1539                       2   \n",
       "1185                       8   \n",
       "\n",
       "                                                 params  split0_test_score  \\\n",
       "1630  {'criterion': 'entropy', 'max_depth': 9, 'min_...           1.000000   \n",
       "280   {'criterion': 'gini', 'max_depth': 4, 'min_sam...           1.000000   \n",
       "370   {'criterion': 'gini', 'max_depth': 5, 'min_sam...           1.000000   \n",
       "1539  {'criterion': 'entropy', 'max_depth': 8, 'min_...           1.000000   \n",
       "1185  {'criterion': 'entropy', 'max_depth': 4, 'min_...           0.916667   \n",
       "\n",
       "      ...  split3_test_score  split4_test_score  split5_test_score  \\\n",
       "1630  ...                1.0                1.0                1.0   \n",
       "280   ...                1.0                1.0                1.0   \n",
       "370   ...                1.0                1.0                1.0   \n",
       "1539  ...                1.0                1.0                1.0   \n",
       "1185  ...                1.0                1.0                1.0   \n",
       "\n",
       "      split6_test_score  split7_test_score  split8_test_score  \\\n",
       "1630           0.916667                1.0           0.916667   \n",
       "280            0.916667                1.0           0.916667   \n",
       "370            0.916667                1.0           0.916667   \n",
       "1539           0.916667                1.0           0.916667   \n",
       "1185           0.916667                1.0           0.916667   \n",
       "\n",
       "      split9_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "1630           0.916667         0.950000        0.055277                1  \n",
       "280            0.916667         0.950000        0.055277                1  \n",
       "370            0.916667         0.950000        0.055277                1  \n",
       "1539           0.916667         0.950000        0.055277                1  \n",
       "1185           0.916667         0.941667        0.053359                5  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv_res = pd.DataFrame(gscv.cv_results_).sort_values(\"rank_test_score\")\n",
    "gscv_res.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=5, min_samples_leaf=2, min_samples_split=4)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dct = DecisionTreeClassifier(criterion = \"gini\", max_depth = 5, min_samples_leaf = 2, min_samples_split = 4)\n",
    "dct.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        39\n",
      "Iris-versicolor       0.93      1.00      0.96        37\n",
      " Iris-virginica       1.00      0.93      0.96        44\n",
      "\n",
      "       accuracy                           0.97       120\n",
      "      macro avg       0.97      0.98      0.98       120\n",
      "   weighted avg       0.98      0.97      0.98       120\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = dct.predict(X_train)\n",
    "print(classification_report(y_train, y_pred))\n",
    "plot_confusion_matrix(dct, X_train, y_train, cmap = plt.cm.Blues, values_format = '.2f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**FINAL CHECK**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "algorithm = {\n",
    "    'Logistic Regression': LogisticRegression(penalty = \"elasticnet\", solver = \"saga\", max_iter = 5000, C = 1, l1_ratio = 0.6),\n",
    "    'K Neighbors Classifier': KNeighborsClassifier(n_neighbors = 12, leaf_size = 30, p = 2),\n",
    "    'SVC': SVC(C = 1, kernel = \"linear\"),\n",
    "    'Gaussian NB': GaussianNB(),\n",
    "    'Decision Tree Classifier': DecisionTreeClassifier(criterion = \"gini\", max_depth = 5, min_samples_leaf = 2, min_samples_split = 4)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_scoring(model):\n",
    "    scores = {}\n",
    "    score = cross_validate(estimator = model, X = X_train, y = y_train, cv = 10,\n",
    "                  scoring = ['accuracy'])\n",
    "    for key, value in score.items():\n",
    "        scores[key] = (round(value.mean(),4))\n",
    "        scores[key + 'std'] = (round(value.std(),4))\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {}\n",
    "parameter = None\n",
    "for key, value in algorithm.items():\n",
    "    results[key] = get_scoring(value)\n",
    "results = pd.DataFrame(results)\n",
    "results = results.rename(index = {'fit_time': 'Fit Time',\n",
    "                                  'score_time': 'Score Time',\n",
    "                                  'test_accuracy': 'Accuracy',\n",
    "                                  'score_timestd': 'Score Time Std',\n",
    "                                  'test_accuracystd': 'Accuracy Std',\n",
    "                                 })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = results.T.reset_index()\n",
    "results.rename(columns = {'index': 'Model'}, inplace = True)\n",
    "results.drop(['Fit Time', 'fit_timestd', 'Score Time', 'Score Time Std'], axis = 1, inplace = True)\n",
    "\n",
    "# ALGORITHM COMPARISON\n",
    "plt.figure(figsize = (20, 4))\n",
    "sns.barplot(x = 'Accuracy', y = 'Model', data = results.sort_values('Accuracy', ascending = False), palette = 'winter')\n",
    "plt.title(\"ACCURACY\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Accuracy Std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.9583</td>\n",
       "      <td>0.0559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K Neighbors Classifier</td>\n",
       "      <td>0.9667</td>\n",
       "      <td>0.0553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.9583</td>\n",
       "      <td>0.0559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gaussian NB</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.0553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.0553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model  Accuracy  Accuracy Std\n",
       "0       Logistic Regression    0.9583        0.0559\n",
       "1    K Neighbors Classifier    0.9667        0.0553\n",
       "2                       SVC    0.9583        0.0559\n",
       "3               Gaussian NB    0.9500        0.0553\n",
       "4  Decision Tree Classifier    0.9500        0.0553"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## **4. FINE TUNE YOUR MODELS AND COMBINE THEM INTO A GREAT SOLUTION**\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf1 = LogisticRegression(penalty = \"elasticnet\", solver = \"saga\", max_iter = 5000, C = 1, l1_ratio = 0.6)\n",
    "clf2 = KNeighborsClassifier(n_neighbors = 12, leaf_size = 30, p = 2)\n",
    "clf3 = SVC(C = 1, kernel = \"linear\")\n",
    "clf4 = GaussianNB()\n",
    "clf5 = DecisionTreeClassifier(criterion = \"gini\", max_depth = 5, min_samples_leaf = 2, min_samples_split = 4)\n",
    "eclf = VotingClassifier(estimators=[(\"lr\", clf1), (\"knn\", clf2), (\"svc\", clf3), (\"gnb\", clf4), (\"dct\", clf5)], voting='hard')\n",
    "eclf = eclf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        39\n",
      "Iris-versicolor       0.97      0.92      0.94        37\n",
      " Iris-virginica       0.93      0.98      0.96        44\n",
      "\n",
      "       accuracy                           0.97       120\n",
      "      macro avg       0.97      0.97      0.97       120\n",
      "   weighted avg       0.97      0.97      0.97       120\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = eclf.predict(X_train)\n",
    "print(classification_report(y_train, y_pred))\n",
    "plot_confusion_matrix(eclf, X_train, y_train, cmap = plt.cm.Blues, values_format = '.2f')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}