iris.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.family']='SimHei' #顯示中文\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>花萼長度</th>\n",
       "      <th>花萼寬度</th>\n",
       "      <th>花瓣長度</th>\n",
       "      <th>花瓣寬度</th>\n",
       "      <th>屬種</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  花萼長度  花萼寬度  花瓣長度  花瓣寬度           屬種  type\n",
       "0   1   5.4   3.7   1.5   0.2  Iris-setosa     1\n",
       "1   2   4.8   3.4   1.6   0.2  Iris-setosa     1\n",
       "2   3   4.8   3.0   1.4   0.1  Iris-setosa     1\n",
       "3   4   4.3   3.0   1.1   0.1  Iris-setosa     1\n",
       "4   5   5.8   4.0   1.2   0.2  Iris-setosa     1\n",
       "5   6   5.7   4.4   1.5   0.4  Iris-setosa     1\n",
       "6   7   5.4   3.9   1.3   0.4  Iris-setosa     1\n",
       "7   8   5.1   3.5   1.4   0.3  Iris-setosa     1\n",
       "8   9   5.7   3.8   1.7   0.3  Iris-setosa     1\n",
       "9  10   5.1   3.8   1.5   0.3  Iris-setosa     1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('./train.csv', dtype = {'花萼長度' : np.float, '花萼寬度' : np.float, '花瓣長度' : np.float, '花瓣寬度' : np.float})\n",
    "test = pd.read_csv('./test.csv')\n",
    "submission = pd.read_csv('./submission.csv')\n",
    "#顯示訓練的前10筆資料\n",
    "train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>花萼長度</th>\n",
       "      <th>花萼寬度</th>\n",
       "      <th>花瓣長度</th>\n",
       "      <th>花瓣寬度</th>\n",
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       "      <th>0</th>\n",
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       "      <td>5.1</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "    <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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  花萼長度  花萼寬度  花瓣長度  花瓣寬度\n",
       "0   1   5.1   3.5   1.4   0.2\n",
       "1   2   4.9   3.0   1.4   0.2\n",
       "2   3   4.7   3.2   1.3   0.2\n",
       "3   4   4.6   3.1   1.5   0.2\n",
       "4   5   5.0   3.6   1.4   0.2\n",
       "5   6   5.4   3.9   1.7   0.4\n",
       "6   7   4.6   3.4   1.4   0.3\n",
       "7   8   5.0   3.4   1.5   0.2\n",
       "8   9   4.4   2.9   1.4   0.2\n",
       "9  10   4.9   3.1   1.5   0.1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#顯示test的前10筆資料\n",
    "test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id        int64\n",
       "花萼長度    float64\n",
       "花萼寬度    float64\n",
       "花瓣長度    float64\n",
       "花瓣寬度    float64\n",
       "屬種       object\n",
       "type      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#資料行列筆數\n",
    "train.shape\n",
    "\n",
    "#資料型態\n",
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      id  花萼長度  花萼寬度  花瓣長度  花瓣寬度             屬種  type\n",
      "121  122   5.2   NaN   5.1   1.8  Iris-new_type     4\n",
      "      id  花萼長度  花萼寬度  花瓣長度  花瓣寬度             屬種  type\n",
      "120  121   NaN   3.0   4.9   1.2  Iris-new_type     4\n"
     ]
    }
   ],
   "source": [
    "#觀察資料內空值\n",
    "train.isnull().sum()\n",
    "\n",
    "#查看空值\n",
    "print(train[train['花萼寬度'].isnull()])\n",
    "print(train[train['花萼長度'].isnull()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#刪掉不影響model的資料\n",
    "del train['id']\n",
    "del train['屬種']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          花萼長度      花萼寬度      花瓣長度      花瓣寬度\n",
      "花萼長度  1.000000 -0.132300  0.861842  0.813901\n",
      "花萼寬度 -0.132300  1.000000 -0.437289 -0.368907\n",
      "花瓣長度  0.861842 -0.437289  1.000000  0.958595\n",
      "花瓣寬度  0.813901 -0.368907  0.958595  1.000000\n"
     ]
    }
   ],
   "source": [
    "corr = train[['花萼長度', '花萼寬度', '花瓣長度', '花瓣寬度']].corr()\n",
    "print(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#將nan刪除(筆數少不影響結果)\n",
    "train.dropna(axis=0,inplace=True)\n",
    "\n",
    "X = train[['花萼長度', '花萼寬度', '花瓣長度', '花瓣寬度']]\n",
    "y = train['type']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SCV Score :  0.972972972973\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "model=SVC()\n",
    "model.fit(X_train,y_train)\n",
    "pred=model.predict(test[['花萼長度', '花萼寬度', '花瓣長度', '花瓣寬度']])\n",
    "print(\"SCV Score : \",model.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       1.00      1.00      1.00        14\n",
      "          2       0.91      1.00      0.95        10\n",
      "          3       1.00      0.92      0.96        13\n",
      "\n",
      "avg / total       0.98      0.97      0.97        37\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "print(metrics.classification_report(y_test, model.predict(X_test)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#預測結果儲存csv\n",
    "Submission = pd.DataFrame({'id' : submission.id , 'type' : pred})\n",
    "Submission.to_csv('./result.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <th>6</th>\n",
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       "      <th>7</th>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id  type\n",
       "0    1     1\n",
       "1    2     1\n",
       "2    3     1\n",
       "3    4     1\n",
       "4    5     1\n",
       "5    6     1\n",
       "6    7     1\n",
       "7    8     1\n",
       "8    9     1\n",
       "9   10     1\n",
       "10  11     2\n",
       "11  12     2\n",
       "12  13     2\n",
       "13  14     2\n",
       "14  15     2\n",
       "15  16     2\n",
       "16  17     2\n",
       "17  18     2\n",
       "18  19     2\n",
       "19  20     2\n",
       "20  21     3\n",
       "21  22     3\n",
       "22  23     3\n",
       "23  24     3\n",
       "24  25     3\n",
       "25  26     3\n",
       "26  27     2\n",
       "27  28     3\n",
       "28  29     3\n",
       "29  30     3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Submission = pd.read_csv('./result.csv',dtype={'type' : np.int32})\n",
    "Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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