Iris_dataset_practice.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0678af05",
   "metadata": {},
   "source": [
    "<center><u><H1>Iris dataset (various models)</H1></u></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a681e6ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "81af7b5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data = load_iris()\n",
    "df_iris = pd.DataFrame(iris_data.data, columns=iris_data.feature_names)\n",
    "df_iris['target'] = pd.Series(iris_data.target)\n",
    "df_iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea0b1265",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2844e06c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_iris.iloc[:, 0:4]\n",
    "y = df_iris['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "828eb784",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d0599e8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "X_train_std = sc.fit_transform(X_train)\n",
    "X_test_std = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a874f1",
   "metadata": {},
   "source": [
    "# Creating models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bfcbf1c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "36ff1872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train_std, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7445d228",
   "metadata": {},
   "source": [
    "# Predictions (DT - one sample value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "51292830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.predict(X.iloc[80:81, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a6c3c75e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0.]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.predict_proba(X.iloc[80:81, :])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a598733d",
   "metadata": {},
   "source": [
    "# Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c24903cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_pred = dt.predict(X_test)\n",
    "gnb_pred = gnb.predict(X_test_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acdde231",
   "metadata": {},
   "source": [
    "# DT - report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4a3a0292",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTree CV Score:\n",
      " 0.975\n"
     ]
    }
   ],
   "source": [
    "print(\"DecisionTree CV Score:\\n\", cross_val_score(dt, X_test, y_test, cv=10).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ff0a9670",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTree Accuracy:\n",
      " 0.9777777777777777\n"
     ]
    }
   ],
   "source": [
    "print(\"DecisionTree Accuracy:\\n\", accuracy_score(y_test, dt_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "df57c86d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTree Confusion Matrix:\n",
      " [[17  0  0]\n",
      " [ 0 16  0]\n",
      " [ 0  1 11]]\n"
     ]
    }
   ],
   "source": [
    "print(\"DecisionTree Confusion Matrix:\\n\", confusion_matrix(y_test, dt_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0cbc42a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTree Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        17\n",
      "           1       0.94      1.00      0.97        16\n",
      "           2       1.00      0.92      0.96        12\n",
      "\n",
      "    accuracy                           0.98        45\n",
      "   macro avg       0.98      0.97      0.98        45\n",
      "weighted avg       0.98      0.98      0.98        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"DecisionTree Classification Report:\\n\", classification_report(y_test, dt_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bd2eca0",
   "metadata": {},
   "source": [
    "# GNB - report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b1f056c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GNB CV Score:\n",
      " 0.9800000000000001\n"
     ]
    }
   ],
   "source": [
    "print(\"GNB CV Score:\\n\", cross_val_score(gnb, X_test_std, y_test, cv=10).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9a6456bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GNB Accuracy:\n",
      " 0.9555555555555556\n"
     ]
    }
   ],
   "source": [
    "print(\"GNB Accuracy:\\n\", accuracy_score(y_test, gnb_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fad82af9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GNB Confusion Matrix:\n",
      " [[17  0  0]\n",
      " [ 0 16  0]\n",
      " [ 0  2 10]]\n"
     ]
    }
   ],
   "source": [
    "print(\"GNB Confusion Matrix:\\n\", confusion_matrix(y_test, gnb_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8c796832",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GNB Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        17\n",
      "           1       0.89      1.00      0.94        16\n",
      "           2       1.00      0.83      0.91        12\n",
      "\n",
      "    accuracy                           0.96        45\n",
      "   macro avg       0.96      0.94      0.95        45\n",
      "weighted avg       0.96      0.96      0.95        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"GNB Classification Report:\\n\", classification_report(y_test, gnb_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f36b465",
   "metadata": {},
   "source": [
    "# Plotting models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "65132e24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(10, 4))\n",
    "res = 0.02\n",
    "X2 = X.to_numpy()[:,[2,3]]\n",
    "\n",
    "x1min, x1max = X2[:,0].min() - 1, X2[:,0].max() + 1\n",
    "x2min, x2max = X2[:,1].min() - 1, X2[:,1].max() + 1\n",
    "xx1, xx2 = np.meshgrid(np.arange(x1min, x1max, res), np.arange(x2min, x2max, res))\n",
    "\n",
    "for clf, ax, title in zip([dt, gnb], axs, ['Decision Tree', 'Naive Bayes']):\n",
    "    clf.fit(X2, y)\n",
    "    output = clf.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
    "    output = output.reshape(xx1.shape)\n",
    "    ax.pcolormesh(xx1, xx2, output, cmap=plt.cm.Accent, shading='auto')\n",
    "    ax.scatter(X2[y==0, 0], X2[y==0, 1], s=30, color='yellow', marker='^')\n",
    "    ax.scatter(X2[y==1, 0], X2[y==1, 1], s=30, color='black', marker='o')\n",
    "    ax.scatter(X2[y==2, 0], X2[y==2, 1], s=30, color='violet', marker='x')\n",
    "    ax.set_title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc5659db",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}