ex3_dataScience.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "999b1420",
   "metadata": {},
   "source": [
    "### Sckit-learn 으로 분류(Classfication)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3e7d018a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# 불필요한 경고 출력을 방지한다.\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a6ee1808",
   "metadata": {},
   "outputs": [],
   "source": [
    "# iris 데이터셋\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "97605410",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터셋 로드\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bf4ccd5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "                \n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
      "Machine Learning Repository, which has two wrong data points.\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n"
     ]
    }
   ],
   "source": [
    "print(iris['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "739c0657",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = iris['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "edb93c27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "62ebbc67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "# 지도학습 : 분류\n",
    "target = iris['target']\n",
    "print(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0809b1fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "print(iris['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "32d3d8b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터프레임에 데이터셋 넣기\n",
    "df_iris = pd.DataFrame(data, columns=iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c5a91b6d",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1581f367",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_iris['target']=target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6737cfe0",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>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",
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       "    <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",
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      "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ffa4a4cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f1c1fe30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatterplot : 산점도 그래프scatterplot : 산점도 그래프\n",
    "sns.scatterplot('sepal width (cm)', 'sepal length (cm)', hue='target', palette='muted', data=df_iris)\n",
    "plt.title('Sepal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b3607eea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatterplot : 산점도 그래프scatterplot : 산점도 그래프\n",
    "sns.scatterplot('petal width (cm)', 'petal length (cm)', hue='target', palette='muted', data=df_iris)\n",
    "plt.title('Petal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "aa183113",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "fig = plt.figure(figsize=(8, 6))\n",
    "ax = Axes3D(fig, elev=-150, azim=110)\n",
    "X_reduced = PCA(n_components=3).fit_transform(df_iris.drop('target', 1))\n",
    "ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=df_iris['target'],\n",
    "           cmap=plt.cm.Set1, edgecolor='k', s=40)\n",
    "ax.set_title(\"Iris 3D\")\n",
    "ax.set_xlabel(\"x\")\n",
    "ax.w_xaxis.set_ticklabels([])\n",
    "ax.set_ylabel(\"y\")\n",
    "ax.w_yaxis.set_ticklabels([])\n",
    "ax.set_zlabel(\"z\")\n",
    "ax.w_zaxis.set_ticklabels([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a52e552a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 학습할 데이터와 테스트 데이터를 나눈다\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "540717e1",
   "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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\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",
       "145                6.7               3.0                5.2               2.3\n",
       "146                6.3               2.5                5.0               1.9\n",
       "147                6.5               3.0                5.2               2.0\n",
       "148                6.2               3.4                5.4               2.3\n",
       "149                5.9               3.0                5.1               1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.drop('target', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "5d76f093",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_valid, y_train, y_valid = train_test_split(df_iris.drop('target', 1), df_iris['target'], test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "144a3b14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((120, 4), (30, 4))"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "16a71eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((120,), (30,))"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape, y_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "274c5c2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='target', ylabel='count'>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PJJ8Evgo8PckdSc6Ydk3aLScApwMrk2zoXidPu6ihOJxTkhrjGb8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfjUvySFJ/nSAflYlOabvfqRdMfglOASYOPgztif/dlYBBr+mznH8al6SbTOjfge4GngWcCiwGPiLqrqsm8jri4wn8no2cDLweuB1wBzjCffWV9X7kzwV+AgwAv4H+BPgMOAK4L+716uq6rtD/Rml+QZ/2Lr0GHQO8MyqWpFkf+AJVXVvkiXA15Jsm0rjaGB1VX0tyXOAVwHHMv6AuJ7xnO4wfoD3G6vqliTPBf6hqlZ2v+eKqrpkyD+ctD2DX/pFAf6me9DKQ4ynyj682/b9qvpat3wCcFlV/RT4aZLPwc9me3w+8OnxdDAAPG6o4qVJGPzSL3ot40s0z66q/0tyO3Bgt+0nExy/H3BPVa3opzzpkfPmrgT3AQd3y78MbOlC/0XAkxc45lrgZUkO7M7yXwrQzen+vSSvhp/dCD52J/1IU2Pwq3lV9SPg2u7B6SuAmSQ3Mr55++0FjvkG42m0bwC+ANzI+KYtjP/XcEaSbwE38fNHal4EvDXJN7sbwNJUOKpH2kNJDqqq+5M8AbgGWLPtOa7SY5nX+KU9t7b7QtaBwDpDX3sLz/glqTFe45ekxhj8ktQYg1+SGmPwS1JjDH5Jasz/AzD/ID6YdFxrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 0, 1, 2 class 데이터 불균형이 있는지 확인할 필요가 있다.\n",
    "sns.countplot(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d9055231",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 클래스별 데이터의 분포를 균등하게 배분\n",
    "# stratify : 정한 Data의 비율을 유지한다.\n",
    "x_train, x_vaild, y_train, y_vaild = train_test_split(df_iris.drop('target', 1), df_iris['target'], test_size=0.2, stratify=df_iris['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "38d8ddd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='target', ylabel='count'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAAM6ElEQVR4nO3dfaxkhVnH8e8PFkQKbSF7g7x2SUNICLYgV1vZpInQRsS2kJYaiFCq6GpMEYypwcRY00RjIjYSrCabFgqVtLGAQptobZBKJIC9CyiwW6S2lFJBbkELxSDFPv4xs2VZeRlezjncfb6fZLIzZ+bOeTaz+92zZ86cSVUhSepjt6kHkCSNy/BLUjOGX5KaMfyS1Izhl6Rm1k09wCLWr19fGzZsmHoMSVpTtmzZ8u2qWtp5+ZoI/4YNG1hZWZl6DElaU5J849mWu6tHkpox/JLUjOGXpGYMvyQ1Y/glqRnDL0nNDBb+JJckeSjJnTss2z/JF5PcM/91v6HWL0l6dkNu8X8SOGmnZRcA11XVEcB189uSpBENFv6qugF4ZKfFpwCXza9fBpw61PolSc9u7E/uHlBVD8yvPwgc8FwPTLIJ2ARw2GGHLbyC4z50+cuZTwvY8kfvH+y57/vIjw723Jo57HfvGOR5N168cZDn1dNuPPfGV+R5Jntzt2Zf/fWcX/9VVZurarmqlpeW/t+pJiRJL9HY4f+PJAcCzH99aOT1S1J7Y4f/WuDs+fWzgWtGXr8ktTfk4ZyfBm4Cjkxyf5JzgD8E3pHkHuDt89uSpBEN9uZuVZ3xHHedONQ6JUkvzE/uSlIzhl+SmjH8ktSM4ZekZgy/JDVj+CWpGcMvSc0YfklqxvBLUjOGX5KaMfyS1Izhl6RmDL8kNWP4JakZwy9JzRh+SWrG8EtSM4Zfkpox/JLUjOGXpGYMvyQ1Y/glqRnDL0nNGH5JasbwS1Izhl+SmjH8ktSM4ZekZgy/JDVj+CWpGcMvSc0YfklqZpLwJ/mNJHcluTPJp5PsNcUcktTR6OFPcjDw68ByVR0N7A6cPvYcktTVVLt61gE/nGQdsDfw7xPNIUntjB7+qvoWcCFwH/AA8J2q+rudH5dkU5KVJCurq6tjjylJu6wpdvXsB5wCHA4cBLwmyZk7P66qNlfVclUtLy0tjT2mJO2yptjV83bg61W1WlXfA64Gjp9gDklqaYrw3we8NcneSQKcCGybYA5JammKffy3AFcCtwJ3zGfYPPYcktTVuilWWlUfBj48xbolqTs/uStJzRh+SWrG8EtSM4Zfkpox/JLUjOGXpGYMvyQ1Y/glqRnDL0nNGH5JasbwS1Izhl+SmjH8ktSM4ZekZgy/JDVj+CWpGcMvSc0YfklqxvBLUjOGX5KaMfyS1Izhl6RmDL8kNWP4JakZwy9JzRh+SWrG8EtSM4Zfkpox/JLUjOGXpGYMvyQ1Y/glqZlJwp/k9UmuTPKVJNuS/OQUc0hSR+smWu9FwN9W1WlJ9gT2nmgOSWpn9PAneR3wNuADAFX1JPDk2HNIUldT7Oo5HFgFLk1yW5KPJ3nNBHNIUktThH8d8GPAn1fVscDjwAU7PyjJpiQrSVZWV1fHnlGSdllThP9+4P6qumV++0pm/xA8Q1VtrqrlqlpeWloadUBJ2pWNHv6qehD4ZpIj54tOBLaOPYckdTXVUT3nAlfMj+j5GvALE80hSe0sFP4k11XViS+0bFFVdTuw/FJ+VpL08jxv+JPsxewY+/VJ9gMyv+u1wMEDzyZJGsALbfH/CnA+cBCwhafD/yjwp8ONJUkayvOGv6ouAi5Kcm5VXTzSTJKkAS20j7+qLk5yPLBhx5+pqssHmkuSNJBF39z9FPBG4Hbgf+eLCzD8krTGLHo45zJwVFXVkMNIkoa36Ae47gR+ZMhBJEnjWHSLfz2wNck/Af+zfWFVvXuQqSRJg1k0/L835BCSpPEselTPPww9iCRpHIse1fMYs6N4APYE9gAer6rXDjWYJGkYi27x77v9epIApwBvHWooSdJwXvRpmWvmr4GffuXHkSQNbdFdPe/Z4eZuzI7rf2KQiSRJg1r0qJ537XD9KeBeZrt7JElrzKL7+P2iFEnaRSy0jz/JIUn+KslD88tVSQ4ZejhJ0itv0Td3LwWuZXZe/oOAz82XSZLWmEXDv1RVl1bVU/PLJ4GlAeeSJA1k0fA/nOTMJLvPL2cCDw85mCRpGIuG/xeBnwMeBB4ATgM+MNBMkqQBLXo450eAs6vqPwGS7A9cyOwfBEnSGrLoFv+btkcfoKoeAY4dZiRJ0pAWDf9uSfbbfmO+xb/o/xYkSa8ii8b7j4Gbknx2fvt9wO8PM5IkaUiLfnL38iQrwAnzRe+pqq3DjSVJGsrCu2vmoTf2krTGvejTMkuS1jbDL0nNGH5JasbwS1Izhl+SmjH8ktTMZOGfn+XztiSfn2oGSepoyi3+84BtE65fklqaJPzzr238WeDjU6xfkjqbaov/T4DfAr7/XA9IsinJSpKV1dXV0QaTpF3d6OFP8k7goara8nyPq6rNVbVcVctLS37LoyS9UqbY4t8IvDvJvcBngBOS/MUEc0hSS6OHv6p+u6oOqaoNwOnA31fVmWPPIUldeRy/JDUz6bdoVdWXgC9NOYMkdeMWvyQ1Y/glqRnDL0nNGH5JasbwS1Izhl+SmjH8ktSM4ZekZgy/JDVj+CWpGcMvSc0YfklqxvBLUjOGX5KaMfyS1Izhl6RmDL8kNWP4JakZwy9JzRh+SWrG8EtSM4Zfkpox/JLUjOGXpGYMvyQ1Y/glqRnDL0nNGH5JasbwS1Izhl+SmjH8ktSM4ZekZkYPf5JDk1yfZGuSu5KcN/YMktTZugnW+RTwm1V1a5J9gS1JvlhVWyeYRZLaGX2Lv6oeqKpb59cfA7YBB489hyR1Nek+/iQbgGOBW57lvk1JVpKsrK6ujj6bJO2qJgt/kn2Aq4Dzq+rRne+vqs1VtVxVy0tLS+MPKEm7qEnCn2QPZtG/oqqunmIGSepqiqN6AnwC2FZVHx17/ZLU3RRb/BuBs4ATktw+v5w8wRyS1NLoh3NW1T8CGXu9kqQZP7krSc0YfklqxvBLUjOGX5KaMfyS1Izhl6RmDL8kNWP4JakZwy9JzRh+SWrG8EtSM4Zfkpox/JLUjOGXpGYMvyQ1Y/glqRnDL0nNGH5JasbwS1Izhl+SmjH8ktSM4ZekZgy/JDVj+CWpGcMvSc0YfklqxvBLUjOGX5KaMfyS1Izhl6RmDL8kNWP4JakZwy9JzUwS/iQnJbk7yVeTXDDFDJLU1ejhT7I78DHgZ4CjgDOSHDX2HJLU1RRb/D8BfLWqvlZVTwKfAU6ZYA5JailVNe4Kk9OAk6rql+a3zwLeUlUf3Olxm4BN85tHAnePOui41gPfnnoIvSS+dmvbrv76vaGqlnZeuG6KSRZRVZuBzVPPMYYkK1W1PPUcevF87da2rq/fFLt6vgUcusPtQ+bLJEkjmCL8XwaOSHJ4kj2B04FrJ5hDkloafVdPVT2V5IPAF4DdgUuq6q6x53iVabFLaxfla7e2tXz9Rn9zV5I0LT+5K0nNGH5JasbwT8hTV6xdSS5J8lCSO6eeRS9OkkOTXJ9ka5K7kpw39Uxjcx//ROanrvhX4B3A/cyOdjqjqrZOOpgWkuRtwHeBy6vq6Knn0eKSHAgcWFW3JtkX2AKc2unvnlv80/HUFWtYVd0APDL1HHrxquqBqrp1fv0xYBtw8LRTjcvwT+dg4Js73L6fZn/4pKkl2QAcC9wy8SijMvySWkqyD3AVcH5VPTr1PGMy/NPx1BXSRJLswSz6V1TV1VPPMzbDPx1PXSFNIEmATwDbquqjU88zBcM/kap6Cth+6optwF966oq1I8mngZuAI5Pcn+ScqWfSwjYCZwEnJLl9fjl56qHG5OGcktSMW/yS1Izhl6RmDL8kNWP4JakZwy9JzRh+tZfk9Ul+bYT1nJrkqKHXI70Qwy/B64GFw5+Zl/J351TA8GtyHsev9pJsPzPq3cD1wJuA/YA9gN+pqmvmJ/P6ArOTeR0HnAy8HzgTWGV2wr0tVXVhkjcCHwOWgP8GfhnYH/g88J355b1V9W9j/R6lHY3+ZevSq9AFwNFVdUySdcDeVfVokvXAzUm2n0rjCODsqro5yY8D7wXezOwfiFuZndcdZl/g/atVdU+StwB/VlUnzJ/n81V15Zi/OWlnhl96pgB/MP+ile8zO1X2AfP7vlFVN8+vbwSuqaongCeSfA5+cMbH44HPzk4JA8APjTW8tAjDLz3TzzPbRXNcVX0vyb3AXvP7Hl/g53cD/quqjhlmPOnl881dCR4D9p1ffx3w0Dz6PwW84Tl+5kbgXUn2mm/lvxNgfl73ryd5H/zgjeA3P8t6pMkYfrVXVQ8DN86/OP0YYDnJHczevP3Kc/zMl5mdRvtfgL8B7mD2pi3M/tdwTpJ/Bu7i6a/U/AzwoSS3zd8AlibhUT3SS5Rkn6r6bpK9gRuATdu/y1V6NXMfv/TSbZ5/IGsv4DKjr7XCLX5JasZ9/JLUjOGXpGYMvyQ1Y/glqRnDL0nN/B80ieUU73rAxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y_vaild)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28d8040e",
   "metadata": {},
   "source": [
    "#### Logistic Regression\n",
    "독립변수의 선형 결함을 사용하여 사건의 발생 가능성을 예측한다 (pdf 참조)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "34fcea7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "baa58d9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. 모델 선언\n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "850296ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. 모델 학습\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "226a5f09",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. 학습이 잘 되었는지 확인\n",
    "prediction = model.predict(x_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f4e63515",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 2, 2, 0, 0, 1, 2, 1, 1, 1, 2, 0, 2, 1, 2, 2, 0, 0, 0, 1, 2,\n",
       "       0, 0, 0, 0, 0, 1, 2, 0])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "793a7038",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression Model은 96%의 정확도를 갖는다.\n",
    "(prediction==y_valid).mean()    # 평균값"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c595c65",
   "metadata": {},
   "source": [
    "#### SGDClassifier (Stochastic Gradient Descent) : 확률적 경사하강법"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "841714f6",
   "metadata": {},
   "source": [
    "Deterministic vs Stochastic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6e720211",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "716acbda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. 모델 선언\n",
    "# random_state => random_seed\n",
    "sgd = SGDClassifier(random_state=110)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "8bc7643e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(random_state=110)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. 모델 학습\n",
    "sgd.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "d154fdb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. 학습 결과 확인(테스트)\n",
    "prediction = sgd.predict(x_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "957f367a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8666666666666667"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(prediction==y_valid).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c3541f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 하이퍼파리미터 튜닝\n",
    "# random_state : 하이퍼파리미터 튜닝시 고정\n",
    "# n_jobs = -1 : CPU CORE를 모두 사용 (학습속도가 빨라진다)\n",
    "# GPU가 아닌 CPU로 처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "4d703493",
   "metadata": {},
   "outputs": [],
   "source": [
    "# overfitting(과적합)을 방지하기 위해 L1(Lasso Regulization)을 적용시킨다.\n",
    "sgd = SGDClassifier(penalty='l1', random_state=119, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ef6417b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(n_jobs=-1, penalty='l1', random_state=119)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "915261f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = sgd.predict(x_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "a439a939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(prediction==y_valid).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b0e056f",
   "metadata": {},
   "source": [
    "#### KNN(K-Neighbors) Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "3e0be62e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55bf1eca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. 모델 정의\n",
    "# default k = 5\n",
    "model = KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "bdeaecee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. 모델 학습\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "a4a34c18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. 모델 테스트\n",
    "prediction = model.predict(x_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "9baeeb0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(prediction==y_valid).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "028b651c",
   "metadata": {},
   "source": [
    "k = 11 이면"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "9fefb236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# iris 데이터셋의 경우 n_neighbors값이 높을 수록 정확한 편이다\n",
    "model = KNeighborsClassifier(n_neighbors=11)\n",
    "model.fit(x_train, y_train)\n",
    "prediction = model.predict(x_valid)\n",
    "(prediction==y_valid).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d59f669",
   "metadata": {},
   "source": [
    "#### 서포트 벡터 머신 (SVC)\n",
    "새로운 데이터가 어느 카테고리에 속할지 판단하는 비확률적 이진 선형 분류 모델을 만듬.\n",
    "경계로 표현되는 데이터들 중 가장 큰 폭을 가진 경계를 찾는 알고리즘"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c0d80ef",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)Image('https://csstudy.files.wordpress.com/2011/03/screen-shot-2011-02-28-at-5-53-26-pm.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "8df3aacd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "ce5e6006",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC(random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "d5a36f64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(random_state=123)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "1d60dcf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = model.predict(x_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "1a3cb9ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(prediction==y_valid).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49e2ec8b",
   "metadata": {},
   "source": [
    "#### 의사 결정 트리(Decision Tree)\n",
    "스무고개처럼, 나무 가지치기를 통해 소그룹으로 나누어 판별하는 것"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1b42eaf",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)Image('https://www.researchgate.net/profile/Ludmila_Aleksejeva/publication/293194222/figure/fig1/AS:669028842487827@1536520314657/Decision-tree-for-Iris-dataset.png', width=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "21d22a53",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "e05e3352",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DecisionTreeClassifier(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "8fe6ce4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(random_state=0)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "1be8c2ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = model.predict(x_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "b32d1c43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(prediction==y_valid).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "027bf996",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting graphviz\n",
      "  Downloading graphviz-0.16-py2.py3-none-any.whl (19 kB)\n",
      "Installing collected packages: graphviz\n",
      "Successfully installed graphviz-0.16\n"
     ]
    }
   ],
   "source": [
    "#!pip install graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "0238c719",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(334.8, 587.0880000000001, 'X[3] <= 0.75\\ngini = 0.666\\nsamples = 120\\nvalue = [38, 40, 42]'),\n",
       " Text(283.2923076923077, 456.624, 'gini = 0.0\\nsamples = 38\\nvalue = [38, 0, 0]'),\n",
       " Text(386.3076923076923, 456.624, 'X[3] <= 1.75\\ngini = 0.5\\nsamples = 82\\nvalue = [0, 40, 42]'),\n",
       " Text(206.03076923076924, 326.1600000000001, 'X[2] <= 4.95\\ngini = 0.169\\nsamples = 43\\nvalue = [0, 39, 4]'),\n",
       " Text(103.01538461538462, 195.69600000000003, 'X[3] <= 1.65\\ngini = 0.05\\nsamples = 39\\nvalue = [0, 38, 1]'),\n",
       " Text(51.50769230769231, 65.23200000000008, 'gini = 0.0\\nsamples = 38\\nvalue = [0, 38, 0]'),\n",
       " Text(154.52307692307693, 65.23200000000008, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
       " Text(309.04615384615386, 195.69600000000003, 'X[3] <= 1.65\\ngini = 0.375\\nsamples = 4\\nvalue = [0, 1, 3]'),\n",
       " Text(257.53846153846155, 65.23200000000008, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]'),\n",
       " Text(360.55384615384617, 65.23200000000008, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
       " Text(566.5846153846154, 326.1600000000001, 'X[2] <= 4.85\\ngini = 0.05\\nsamples = 39\\nvalue = [0, 1, 38]'),\n",
       " Text(515.0769230769231, 195.69600000000003, 'X[0] <= 5.95\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]'),\n",
       " Text(463.5692307692308, 65.23200000000008, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
       " Text(566.5846153846154, 65.23200000000008, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]'),\n",
       " Text(618.0923076923077, 195.69600000000003, 'gini = 0.0\\nsamples = 36\\nvalue = [0, 0, 36]')]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "tree.plot_tree(model, fontsize=10)"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}